If you are starting your career in data science, it is essential to master coding as early as possible. However, choosing the right programming language can be tough, especially if you’re new to coding. With many coding languages available, some are better suited for data science and allow you to work with large datasets more effectively.

This article will provide you with the top 10 data science programming languages in 2024, allowing you to either begin or advance your programming skills. Let’s get started!

What Is Data Science?

Data science is the study of structured, semi-structured, and unstructured data to derive meaningful insights and knowledge. It is a multi-disciplinary approach that combines principles from various fields, including mathematics, statistics, computer science, machine learning, and AI. This allows you to analyze data for improved business decision-making.

Every data science project follows an iterative lifecycle that involves the following stages:

Business Understanding

The business understanding stage involves two major tasks: defining project goals and identifying relevant data sources.

To define objectives, you must collaborate with your customers and other key stakeholders to thoroughly understand their business challenges and expectations. Following this, you can formulate questions to help clarify the project’s purpose and establish key performance indicators (KPIs) that will effectively measure its success. Compile detailed documentation of the business requirements, project objectives, formulated questions, and KPIs to serve as a reference throughout the project lifecycle.

Once you understand the business objectives, you can identify the relevant data sources that provide the information required to answer the formulated questions. 

Data Acquisition and Exploratory Analysis

Data acquisition involves using data integration tools to set up a pipeline to help you ingest data from identified sources to a destination. Then, you must prepare the data by resolving the issues, including missing values, duplicates, and inconsistencies.

Finally, you can perform an exploratory analysis of the processed data using data summarization and visualization techniques to help you uncover patterns and relationships. This data analysis allows you to build a predictive model for your needs.

Since data acquisition and exploratory analysis is an ongoing process, you can re-configure your pipeline to automatically load new data at regular intervals. 

Data Modeling

Data modeling includes three major tasks: feature engineering, model training, and model evaluation. In feature engineering, you must identify and extract only the relevant and informative features from the transformed data for model training.

After selecting the necessary features, the data is randomly split into training and testing datasets. With the training data, you can develop models with various machine learning or deep learning algorithms. Following this, you must evaluate the models by assessing them on the testing dataset and compare the predicted results to the actual outcomes. This evaluation allows you to select the best model based on the performance.

Model Deployment

In this stage, your stakeholders should validate that the system meets their business requirements and answers the formulated questions with acceptable accuracy. Once validated, you can deploy the model to a production environment through an API. This API enables end users to quickly use the model from various platforms, including websites, back-end applications, dashboards, or spreadsheets.

Monitoring and Maintenance

After deploying the model, it is essential to continually monitor its performance to ensure it meets your business objectives. This involves tracking key metrics like accuracy, response time, and failure rates. You also need to check that the data pipeline remains stable and the model continues to perform well as new data comes in. In addition, you must regularly retrain the model if performance declines due to data drift or other changes.

Role of Programming Languages in Data Science

Programming languages are essential in data science for efficient data management and analysis. Here are some of the data science tasks you can perform using programming languages:

• Programming languages help you clean, organize, and manipulate data into a usable format for analysis. This involves removing duplicates, handling missing data, and transforming data into an analysis-ready format.

• You can use programming languages to perform mathematical and statistical analyses to find patterns, trends, or relationships within the data.

• Data science programming is crucial for developing machine learning models, which are algorithms that allow you to learn from data and make predictions. The models can range from simple linear regression to complex deep learning networks.

• With programming languages, you can create a range of visualizations, such as graphs, charts, and interactive dashboards. These tools help to visually represent data, making it easier to share findings, trends, and insights with stakeholders.

10 Best Data Science Programming Languages

Choosing the best programming language can make all the difference in efficiently solving data science problems. Here’s a closer look at some of the powerful and versatile languages that you should consider mastering:

Python

Python is a popular, open-source, easy-to-learn programming language developed by Guido van Rossum in 1991. According to PopularitY of Programming Language Index (PYPL), Python holds the top rank with a market share of 29.56%.

Originally designed for web and game development, Python’s versatility extends to various fields, including data science, artificial intelligence, machine learning, automation, and more.

If you’re new to data science and uncertain about which language to learn first, Python is a great choice due to its simple syntax. With its rich ecosystem of libraries, Python enables you to perform various data science tasks, from preprocessing to model deployment.

Let’s look at some Python libraries for data science programming:

• Pandas: A key library that allows you to manipulate and analyze the data by converting it into Python data structures called DataFrames and Series.
• NumPy: A popular package that provides a wide range of advanced mathematical functions to help you work with large, multi-dimensional arrays and matrices.
• Matplotlib: A standard Python library that helps you create static, animated, and interactive visualizations.
• Scikit-learn and TensorFlow: Allows you to develop machine learning and deep learning models, respectively, by offering tools for data mining and data analysis.
• Keras: A high-level neural networks API, integrated with TensorFlow, that enables you to develop and train deep learning models using Python.
• PyCaret: A low-code machine learning library in Python that facilitates the automation of several aspects of a machine learning workflow.

R

R is an open-source, platform-independent language developed by Ross Ihaka and Robert Gentleman in 1992. With R, you can process and analyze large datasets in the field of statistical computing. It includes various built-in functions, such as t-tests, ANOVA, and regression analysis, for statistical analysis.

R also provides specialized data structures, including vectors, arrays, matrices, data frames, and lists, to help you organize and manipulate statistical data. One of R’s advantages is that it is an interpreted language; it doesn’t need compilation into executable code. This makes it easier to execute scripts and perform analysis.

R supports data science tasks with some key libraries. including:

• dplyr: A data manipulation library that allows you to modify and summarize your data using pre-defined functions like mutate(), select(), and group_by(). 
• ggplot2: A data visualization package that enables you to create data visualizations in scatter plots, line charts, bar charts, dendrograms, and 3-D charts.
• knitr: A package that integrates with R markdown to convert dynamic analysis into high-quality reports that can include code, results, and narrative text. 
• lubridate: An R library that provides simple functions like day(), month(), year(), second(), minute(), and hour() to easily work with dates and times.
• mlr3: A useful R tool for building various supervised and unsupervised machine learning models.

Scala

Scala is a high-level programming language introduced by Martin Odersky in 2001. It supports both functional and object-oriented programming (OOP) paradigms.

With the OOP approach, Scala allows you to write modular and reusable code around objects, making it easy to model complex systems. On the other hand, the functional programming paradigm helps you write pure immutable functions, where data cannot be changed once the function is created. These pure functions do not have any side effects and are independent of the external state. This multi-paradigm approach makes Scala ideal for developing scalable and high-performance data science projects, especially when handling large datasets.

Scala supports several powerful libraries for data science. Let’s look at some of them:

• Breeze: A numerical processing library that helps you perform linear algebra operations, matrix multiplications, and other mathematical computations in data science tasks.

• Scalaz: A Scala library that supports functional programming with advanced constructs such as monads, functors, and applicatives. These constructs allow you to build complex data pipelines and handle transformations to convert data into usable formats.

• Algebird: Developed by Twitter, this library offers algebraic data structures like HyperLogLog and Bloom filters to help you process large-scale data in distributed systems.

• Saddle: This is a data manipulation library that provides robust support for working with structured datasets through DataFrames and Series.

• Plotly for Scala: A data visualization library that enables you to create interactive, high-quality visualizations to present the data analysis results clearly.

Julia

Julia is a high-performance, dynamic, open-source programming language built for numerical and scientific computing. It was developed by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman in 2012.

Julia offers speed comparable to languages like C++ while maintaining an ease of use similar to Python. Its ability to handle complex mathematical operations effectively enables it for data science projects that require high-speed computations. Julia is particularly well-suited for high-dimensional data analysis, ML, and numerical computing due to its speed and multiple dispatch features. Julia’s multiple dispatch system allows you to define the functions that behave differently based on the types of inputs they receive.

Here are some essential Julia libraries and frameworks for data science tasks:

• DataFrames.jl: Provides tools to manipulate tabular data, similar to Python’s Pandas library.
• Flux.jl: A machine learning library for building and training complex models, including large neural networks. 
• DifferentialEquations.jl: A library to solve differential equations and perform simulations and mathematical modeling.
• Plots.jl: A plotting library that helps you visualize data and results from scientific computations.
• MLJ.jl: A Julia framework offering tools for data processing, model selection, and evaluation with a range of algorithms for classification, clustering, and regression tasks. 

MATLAB

MATLAB (Matrix Laboratory), released by MathWorks, is a proprietary programming language widely used for numerical computing, data analysis, and model development. Its major capability is to manage multi-dimensional matrices using advanced mathematical and statistical functions and operators. Along with the functions and operators, MATLAB offers pre-built toolboxes. These toolboxes allow you to embed machine learning, signal processing, and image analysis functionalities in your data science workflows.

Some popular MATLAB toolboxes for data science include:

• Statistics and Machine Learning Toolbox: It offers pre-built functions and applications to help you explore data, perform statistical analysis, and build ML models.

• Optimization Toolbox: A software that allows you to solve large-scale optimization problems like linear, quadratics, non-linear, and integer programming with various algorithms.

• Deep Learning Toolbox: This enables you to design, train, and validate deep neural networks using applications, algorithms, and pre-trained models.

• MATLAB Coder: Converts MATLAB code into C/C++ for increased performance and deployment of different hardware platforms, such as desktop systems or embedded hardware.

Java

Java was originally introduced by Sun Microsystems in 1995 and later acquired by Oracle. It is an object-oriented programming language that is widely used for large-scale data science projects.

One of the key benefits of Java is the Java Virtual Machine (JVM), which allows your applications to run on any device or operating system that supports the JVM. This platform-independent nature of Java enables it to be a good choice for big data processing in distributed environments. You can also take advantage of Java’s garbage collection and multithreading capabilities, which help you manage memory effectively and process tasks in parallel.

Some libraries and frameworks useful for data science in Java are:

• Weka (Waikato Environment for Knowledge Analysis): Weka offers a set of machine learning algorithms for data mining tasks.

• Deeplearning4j: A distributed deep-learning library written for Java that is also compatible with Scala. It facilitates the development of complex neural network configurations.

• Apache Hadoop: A Java-based big data framework that allows you to perform distributed processing of large datasets across clusters of computers.

• Apache Spark with Java: Provides a fast and scalable engine for big data processing and machine learning.

Swift

Swift, introduced by Apple in 2014, is an open-source, general-purpose programming language used for all iOS and macOS application development. However, its performance, safety features, and ease of use have made it a good choice for data science applications tied to Apple’s hardware and software.

Key libraries and tools for data science in Swift include:

• Swift for TensorFlow: A powerful library that combines the expressiveness of Swift with TensorFlow’s deep learning capabilities. It facilitates advanced model building and execution.

• Core ML: Apple’s machine learning framework that helps you embed machine learning models into iOS and macOS apps, enhancing their functionality with minimal effort.

• Numerics: A library for robust numerical computing functionalities that are necessary for high-performance data analysis tasks.

• SwiftPlot: A data visualization tool that supports the creation of various types of charts and graphs for effective presentation of data insights.

Go

Go, also known as Golang, is an open-source programming language developed by Google in 2009. It uses C-like syntax, making it relatively easy to learn if you are familiar with C, C++, or Java.

Golang is well-suited for building efficient, large-scale, and distributed systems. However, Go’s presence in the data science community isn’t as widespread as Python or R. Yet, its powerful concurrency features and fast execution make it one of the important languages for data science tasks.

Here are a few useful Go libraries for data science:

• GoLearn: A Go library that provides a simple interface for implementing machine learning algorithms.
• Gonum: A set of numerical libraries offering essential tools for linear algebra, statistics, and data manipulation.
• GoML: A machine learning library built to integrate machine learning into your applications. It offers various tools for classification, regression, and clustering.
• Gorgonia: A library for deep learning and neural networks.

C++

C++ is a high-level, object-oriented programming language widely used in system programming and applications that require real-time performance. In data science, C++ is often used to execute machine learning algorithms and handle large-scale numerical computations with high performance.

Popular C++ libraries for data science include:

• MLPACK: A comprehensive C++ library that offers fast and flexible machine learning algorithms designed for scalability and speed in data science tasks.
• Dlib: A toolkit consisting of machine learning models and tools to help you develop C++ apps to solve real-world data science challenges.
• Armadillo: A C++ library for linear algebra and scientific computing. It is particularly well-suited for matrix-based computation in data science.
• SHARK: A C++ machine learning library that offers a variety of tools for supervised and unsupervised learning, neural networks, and linear as well as non-linear optimization.

JavaScript

JavaScript is a client-side scripting language primarily used in web development. Recently, it has gained attention in data science due to its ability to help develop interactive data visualizations and dashboards. With a variety of libraries, JavaScript is now used to perform a few data science tasks directly within the browser.

Some key JavaScript libraries for data science include:

• D3.js: A powerful library for creating dynamic, interactive data visualizations in web browsers.
• TensorFlow.js: A library that allows you to run machine learning models in client-side applications, Node.js, or Google Cloud Platform (GCP).
• Chart.js: A simple and flexible plotting library for creating HTML-based charts for your modern web applications. 
• Brain.js: Helps you build GPU-accelerated neural networks, facilitating advanced computations in browsers and Node.js environments.

10 Factors to Consider When Choosing a Programming Language for Your Data Science Projects

• Select a language that aligns with your current skills and knowledge to ensure a smoother learning process.

• Opt for languages with libraries that support specific data science tasks.

• Look for languages that can easily integrate with other languages or systems for easier data handling and system interaction.

• A language that supports distributed frameworks like Apache Spark or Hadoop can be advantageous for managing large datasets efficiently.

• Some projects may benefit from a language that supports multiple programming paradigms like procedural, functional, and object-oriented. This offers flexibility to resolve multiple challenges.

• Ensure the language will help you in creating clear and informative visualizations.

• Evaluate the ease of deployment of your models and applications into production environments using the language.

• Check if the language supports or integrates with version control systems like Git, which are crucial for team collaboration and code management.

• If you are working in industries with strict regulations, you may need to utilize languages that support compliance with relevant standards and best practices.

• Ensure the language has a strong community and up-to-date documentation. These are useful for troubleshooting and learning.

Conclusion 

With an overview of the top 10 data science programming languages in 2024, you can select the one that’s well-suited to your requirements. Each language offers unique strengths and capabilities tailored to different aspects of data analysis, modeling, and visualization.

Among the many languages, Python is the most popular choice for beginners and experienced data scientists due to its versatility and extensive library support. However, when selecting a programming language for your needs, the specific factors listed here can help.

Ultimately, the right language will enable you to utilize the power of data effectively and drive insights that lead to better decision-making and business outcomes. To succeed in this evolving field of data science, you should master two or more languages to expand your skill set. 

FAQs

Which programming language should I learn first for data science?

Python is a highly recommended programming language to learn first for data science. This is mainly because of its simplicity, large community support, and versatile libraries. 

What is the best language for real-time big data processing?

Scala, Java, and Go are popular choices for real-time big data processing due to their robust performance and scalability, especially in distributed environments.

Can I use multiple programming languages in a data science project?

Yes, you can use multiple programming languages in a data science project. Many data scientists combine languages like Python for data manipulation, R for statistical analysis, and SQL for data querying.

Artificial intelligence has been integrated into applications across diverse sectors, from automobiles to agriculture. According to the Grand View Research report, the AI market is projected to grow at a CAGR of 36.6% from 2024 to 2030. However, with the incorporation of these rapid innovations, it’s equally essential to address the safe and ethical use of AI.

This is where the need for artificial intelligence regulations comes into the picture. Without regulation, AI can lead to issues such as social discrimination, national security risks, and other significant issues. Let’s look into the details of why artificial intelligence regulations are necessary and what you need to understand about them.

Why AI Regulation is Required?

The progressively increasing use of AI in various domains globally has brought with it certain challenges. This has led to the need for regulatory frameworks. Here are some of the critical reasons why AI regulation is essential:

Threat to Data Privacy

The models and algorithms in AI applications are trained on massive datasets. These datasets contain data records consisting of personally identifiable information, biometric data, location, or financial data.

To protect such sensitive data, you can deploy data governance and security measures at the organizational level. However, these mechanisms alone cannot ensure data protection.

Setting guidelines at the regional or global level to obtain consent from people before using their data for AI purposes can ensure better data protection. This facilitates the preservation of individual rights and also establishes a common consensus among all stakeholders on using AI.

Ethical Concerns

If the training datasets of AI models contain biased or discriminatory data, it will reflect in the outcomes of AI applications. Without proper regulations, such biases can affect decisions in hiring, lending, or insurance issuance processes. The absence of guidelines for using artificial intelligence in judiciary proceedings can lead to discriminatory judgments and erosion of public trust in the law.

Regulatory frameworks compelling regular audits of AI models could be an efficient way to address ethical issues in AI. Having a benchmark for data quality and collecting data from diverse sources enables you to prepare an inclusive dataset.

Lack of Accountability

If there is an instance of misuse of an AI system like deepfakes, it can be difficult to impart justice to the victims. This is because without a regulatory framework, no specific stakeholder can be held responsible.  Having a robust set of artificial intelligence regulations helps resolve this issue by clearly defining the roles of all stakeholders involved in AI deployment.

With such an arrangement, developers, users, deployers, and any other entity involved can be held accountable for any mishaps. To foster transparency, regulatory frameworks should also make it compulsory to document the training process of AI models and how they make any specific decision.

Important AI Regulatory Frameworks Around the World

Let’s discuss some artificial intelligence laws enforced by various countries around the world:

India

Currently, India lacks specific codified laws that regulate artificial intelligence. However, some frameworks and guidelines were developed in the past few years to introduce a few directives:

• Digital Data Protection Act, 2023, which is yet to be enforced to manage personal data. 
• Principles for Responsible AI, February 2021, contains provisions for ethical deployment of AI across different sectors.
• Operationalizing Principles for Responsible AI, August 2021, emphasized the need for regulatory policies and capacity building for using AI.
• National Strategy for Artificial Intelligence, June 2018, was framed to build robust AI regulations in the future.

A draft of the National Data Governance Framework Policy was also introduced in May 2022. It is intended to streamline data collection and management practices to provide a suitable ecosystem for AI-driven research and startups.

To further promote the use of AI, the Ministry of Electronics and Information Technology (MeitY) has created a committee that regularly develops reports on development and safety concerns related to AI.

EU

The European Union (EU), an organization of 27 European countries, has framed the EU AI Act to govern the use of AI in Europe. Adopted in March 2024, it is the world’s first comprehensive law on artificial intelligence regulations.

While framing the law, different applications of AI were analyzed and categorized according to the risks involved in them. The Act categorizes AI applications based on risk levels:

• Unacceptable Risk: This includes applications like cognitive behavioral manipulation and social scoring. Real-time remote biometric identification is permitted under stringent conditions.

• High Risk: This includes AI systems that negatively impact people’s safety or fundamental rights. Under this category, services using AI in the management of critical infrastructure, education, employment, and law enforcement have to register in the EU database. 

To further ensure safety, the law directs Generative AI applications such as ChatGPT to follow the transparency norms and EU copyright law. More advanced AI models, such as GPT-4, are monitored, and any serious incident is reported to the European Commission.

To address issues such as deepfake, this AI law has made provisions that AI-generated content involving images, audio, or video should be clearly labeled.

The intent of the EU AI Act is to promote safe, transparent, non-discriminatory, ethical, and environment-friendly use of AI. The Act also directs national authorities to provide a conducive environment for companies to test AI models before public deployment.

USA

Currently, there is no comprehensive law in the USA that monitors AI development, but several federal laws address AI-related concerns. In 2022, the US administration proposed a blueprint for an AI Bill of Rights. It was drafted by the White House Office of Science and Technology (OSTP) in collaboration with human rights groups and common people. The OSTP also took input from companies such as Microsoft and Google.

The AI Bill of Rights aims to address AI challenges by building safe systems and avoiding algorithmic discrimination. It has provisions for protecting data privacy and issuing notices explaining AI decisions for transparent usage. The bill also necessitates human interventions in AI operations.

Earlier, the US issued some AI guidelines, such as the Executive Order on the Safe, Secure, and Trustworthy Use of Artificial Intelligence. It requires AI developers to report potentially threatening outcomes to national security.

Apart from this, the Department of Defense, the US Agency for International Development, and the Equal Employment Opportunity Commission have also issued orders for the ethical use of AI. Industry-specific bodies like the Federal Trade Commission, the US Copyright Office, and the Food and Drug Administration have implemented regulations for ethical AI use. 

China

China has different AI regulatory laws at national, regional, and local levels. Its Deep Synthesis Provisions monitors deepfake content, emphasizing content labeling, data security, and personal information protection.

The Internet Information Service Algorithmic Recommendation Management Provisions mandates AI-based personalized recommendation providers to protect your rights. These provisions are grouped as general provisions, informed service norms, and user rights protection. It includes directions to protect the identity of minors and allows you to delete tags about your personal characteristics.

To regulate Generative AI applications, China recently came up with interim measures on generative AI. It directs GenAI service providers that they should not endanger China’s national security or promote ethnic discrimination.

To strengthen the responsible use of AI, China has also deployed the Personal Information Protection Law, the New Generation AI Ethics Specification, and the Shanghai Regulations.

Several other nations, including Canada, South Korea, Australia, and Japan, are also taking proactive measures to regulate AI for ethical use.

Challenges in Regulating AI

The regulation of AI brings with it several challenges owing to its rapid evolution and complex nature. Here are some of the notable challenges:

Defining AI

There are varied views regarding the definition of artificial intelligence. It is a broad term that involves the use of diverse technologies, including machine learning, robotics, and computer vision. As a result, it becomes difficult to establish a one-size-fits-all regulatory framework. For example, you cannot monitor AI systems like chatbots, automated vehicles, or AI-powered medical diagnostic tools with the same set of regulations.

Cross-Border Consensus

Different regions and nations, such as the EU, China, the US, and India, are adopting different regulations for AI. For example, the AI guidelines of the EU emphasize transparency, while those of the US focus on innovation. Such an approach creates operational bottlenecks in a globalized market, complicating compliance for multinational entities.

Balancing Innovation and Regulation

Excessive AI regulation can hamper the development of AI to the full extent, while under-regulation can lead to ethical breaches and security issues. Most companies avoid implementing too many regulations, fearing that it could reduce innovation.

Rapid Pace of Development

The speed with which AI is developing is outpacing the rate at which regulations are developed and enforced. For instance, considerable damage occurred even before regulatory bodies could create rules against deepfake technology. It is also challenging for regulators to create long-term guidelines that can adapt to the rapidly evolving nature of AI technologies.

Lack of Expertise among Policymakers

Effective AI regulation requires policymakers to have a good understanding of the potential risks and mitigation strategies of this technology.  Policymakers usually lack this expertise, leading to the designing of policies that are irrelevant or insufficient for proper monitoring of AI usage.

Key Components of Effective AI Regulation

Here are a few components that are essential to overcome hurdles in framing artificial intelligence regulations:

Data Protection

AI systems are highly data-dependent, which makes it crucial for you to prevent data misuse or mishandling. Regulations like  GDPR or HIPAA ensure that personal data is utilized with consent and responsibly.

You can take measures such as limiting data retention time, masking data wherever required, and empowering individuals to control how their personal information is used.

Transparency

AI systems often operate as black boxes that are difficult to understand. Having transparency ensures that the processes behind AI decision-making are accessible for verification.

To achieve this, the regulatory framework mandates companies to design AI products with auditing features so that the underlying decision-making logic is accessible for verification. If there are any discrepancies, you can challenge the AI decisions, and the developers will be held accountable for providing remedies.

Human Oversight

A fully autonomous AI system makes all decisions on its own and can sometimes take actions that lead to undesirable consequences. As a result, it is important to have some proportion of human intervention, especially in sectors such as healthcare, finance, and national security. 

For this, you can set up override mechanisms where humans can immediately intervene when AI behaves irrationally or unexpectedly.

Global Standards and Interoperability

With the increase in cross-border transactions, it is essential to develop AI systems that facilitate interoperability and adhere to global standards. This will simplify cross-border operations, promote international collaboration, and reduce legal disputes over AI technologies.

Way Forward

There has been an increase in instances of misuse of AI, including deepfakes, impersonations, and data breaches. Given this trend, artificial intelligence regulations have become the need of the hour.

Several countries have introduced legal frameworks at basic or advanced levels to address many artificial intelligence-related questions. However, we still need to fully understand the implications AI will have on human lives.

In the meantime, it is the responsibility of policymakers and technical experts to create public awareness about the impacts of good and bad use of AI. This can be done through education, training, and public engagement through digital platforms. With these initiatives, we can ensure that AI’s positive aspects overpower its negative consequences.

FAQs

To whom does the EU AI Act apply?

The EU AI Act applies to all businesses operating within the EU. It is compulsory for providers, deployers, distributors, importers, and producers of AI systems to abide by the rules of the EU AI Act. 

What are some other examples of data protection regulations?

Some popular examples of the data protection regulations include:

• Digital Personal Data Protection (DPDP) Act, India
• General Data Protection Regulation (GDPR), EU
• California Consumer Privacy Act (CCPA), US
• Protection of Personal Information Act (POPIA), South Africa
• Personal Information Protection and Electronic Documents Act (PIPEDA), Canada

Object-oriented programming (OOP) is the most widely used programming paradigm. It enables you to organize and manage code in a structured way. Like other general-purpose programming languages such as Java, C++, and C#, Python also supports OOP concepts, making it an OOP language. To learn more about Python OOPs and how to apply them effectively in your projects, you have come to the right place.

Let’s get started!

What Is OOPs in Python?

Python supports OOPs, which allows you to write reusable and maintainable Python programs using objects and classes. An object is a specific instance of a class that represents a real-world entity, such as a person, vehicle, or animal. Each object is bundled with attributes (characteristics) and methods (functions) to describe what it is and what it can do. A class is like a blueprint or template that defines the attributes and functions every object should have.

An OOP Example

Let’s understand this clearly by considering a scenario where you are designing a system to manage vehicles. Here, a class can be a template for different types of vehicles. Each class will describe common vehicle characteristics like color, brand, and model, along with functions such as starting or stopping. 

For instance, you might have a class called “Car,” which has these attributes and behaviors. Then, an object would be a specific car, such as a red Mercedes Benz. This object has all the features defined in the class but with particular details: “red” for color, “Mercedes Benz” for brand, and “Cabriolet” for the model. Another object could be a yellow Lamborghini, which has “yellow” for color, “Lamborghini” for the brand, and “Coupe” for the model. 

In this way, the class is a blueprint, while the object is a unique instance of that blueprint with real-world information. 

How Do You Implement Classes and Objects in Python?

To create a class in Python, you must use a keyword class as follows:

class classname:
   <statements> 

For example, 

class demo:
	name = "XYZ"

Once you define a class, you can use it to create objects using the following syntax:

object_name = classname()
print(object_name.field_name)

For instance,

Object1 = demo()
print(Object1.name)

This will produce XYZ as output.

Instance Attributes and Methods 

Each object (instance) of a class can have its own attributes. In the above example, Car is a class with attributes like color, model, and brand. Using an __init__ method, you can initialize these attributes whenever a new object is created. You can pass any number of attributes as parameters to the __init__method,  but the first one must always be named self. When you create a new instance of a class, Python automatically forwards that instance to the self parameter. This allows you to ensure that attributes like color, brand, and model are specific to an object rather than being shared among all instances. 

Objects can also have methods, which are functions that belong to a class and work with specific instances of that class. They use an object’s attributes to perform an action. The first parameter of an instance method is always self, which refers to the object on which the method is being called. Using self parameter, the object methods can access and modify the attributes in the current instance. Inside an instance method, you can use a self to call other methods of the same class for the specific instance. 

To get more idea on this, let’s create a simple implementation for the Car class using classes and objects:

# Define the Car class
class Car:
    def __init__(self, color, brand, model):
        self.color = color  # Attribute for color
        self.brand = brand  # Attribute for brand
        self.model = model  # Attribute for model
    
    # Start Method for Car
    def start(self):
        print(f"The {self.color} {self.brand} {self.model} is starting.")
    
    # Stop Method for Car
    def stop(self):
        self.start() # Calling another method
        print(f"The {self.color} {self.brand} {self.model} is stopping.")

# Creating two objects of the Car class
car1 = Car("red", "Mercedes Benz", "Cabriolet")
car2 = Car("yellow", "Lamborghini", "Coupe")

# Calling the methods of the Car class to execute Car1 object
car1.start()  
car1.stop()   

# Calling the methods of the Car class to execute Car2 object
car2.start()  
car2.stop()   

Click here to try the given program and customize it according to your needs. 

Here is a sample output: 

The above program demonstrates how OOP helps in modeling real-world entities and how they interact with each other.  

Key Principles of OOP

Apart from classes and objects, there are four more principles that make Python an object-oriented programming language. Let’s take a look at each of them:

Inheritance

Inheritance is a key concept in OOPs that provides the ability for one class to inherit the attributes and methods of another class. A class that derives these characteristics is referred to as the child class or derived class. On the other hand, the class from which data and functions are inherited is known as the parent class or base class. 

By implementing inheritance in Python programs, you can efficiently reuse the existing code in multiple classes without rewriting it. This is done by creating a base class with common properties and behaviors. Then, you can derive child classes that inherit the parent class functionality. In the derived classes, you have the flexibility to add new features or override existing methods by preserving the base class code. 

Types of Python Inheritance

Single Inheritance

In single inheritance, a child class helps you inherit from one base class. Let’s extend the above Car class to model a vehicle management system through single inheritance:

# Define the Vehicle class (Parent class)
class Vehicle:
    def __init__(self, color, brand, model):
        self.color = color  
        self.brand = brand
        self.model = model
    
    # Start Method for Vehicle
    def start(self):
        print(f"The {self.color} {self.brand} {self.model} is starting.")
    
    # Stop Method for Vehicle
    def stop(self):
        print(f"The {self.color} {self.brand} {self.model} is stopping.")

class Car(Vehicle): # Car is a child class that inherits from the Vehicle class
# Defining function for Car
    def carfunction(self):
	    print("Car is functioning")

# Creating an object of the Car class
car1 = Car("red", "Mercedes Benz", "Cabriolet")

# Calling the methods
car1.start()
car1.carfunction()  
car1.stop()   

Sample Output:

Hierarchical Inheritance

In hierarchical inheritance, multiple derived classes inherit from a single base class, which helps different child classes to share functionalities. Here is the modified vehicle management program to illustrate hierarchical inheritance: 

class Bike(Vehicle):  # Bike inherits from Vehicle
    def kick_start(self):
        print("Bike is kick-started.")

class Jeep(Vehicle):  # SportsBike inherits from Bike
    def off_road(self):
        print("Jeep is in off-road mode!")

# Creating an object of Bike and Jeep child class
bike1 = Bike("yellow", "Kawasaki", "Ninja")
jeep1 = Jeep("Black", "Mahindra", "Thar")
bike1.kick_start() 
bike1.start()  
bike1.stop()
jeep1.off_road() 
jeep1.start()
jeep1.stop()

Sample Output:

Multilevel Inheritance

Multilevel inheritance is referred to as a chain of inheritance, which enables a child class to inherit from another child class. Here is the code fragment demonstrating multilevel inheritance that you can add to the above vehicle management program:

class SportsBike(Bike):  # SportsBike inherits from Bike
   
    def drift(self):
        print(f"The {self.color} {self.brand} {self.model} is drifting!")

# Creating an object of SportsBike
sports_bike = SportsBike("yellow", "Kawasaki", "Ninja")
sports_bike.start()  
sports_bike.kick_start()  
sports_bike.drift()  
sports_bike.stop()

Sample Output:

Multiple Inheritance

In multiple inheritance, a derived class can inherit from more than one base class. This helps the child class to combine functions of multiple parent classes. Let’s include one more parent class in the vehicle management program to understand the multiple inheritance concept:

# Define the Vehicle class
class Vehicle:
    def __init__(self, color, model):
        self.color = color  # Attribute for color
        self.model = model  # Attribute for model

    def start(self):
        print(f"The {self.color} {self.model} is starting.")
    
    def stop(self):
        print(f"The {self.color} {self.model} is stopping.")

# Define the Manufacturer class
class Manufacturer:
    def __init__(self, brand):
        self.brand = brand  # Attribute for brand

    def get_brand(self):
        print(f"This vehicle is manufactured by {self.brand}.")

# Define the Car class that inherits from both the Vehicle and the Manufacturer
class Car(Vehicle, Manufacturer):
    def __init__(self, color, model, brand):
        Vehicle.__init__(self, color, model)
        Manufacturer.__init__(self, brand)

    def show_details(self):
        self.get_brand()
        print(f"The car is a {self.color} {self.brand} {self.model}.")

# Creating an object of Car
car = Car("red", "Cabriolet", "Mercedes Benz")
car.start()
car.show_details()
car.stop()

Sample Output:

Polymorphism

Polymorphism refers to the capability of different objects to respond in multiple ways using the same method or function call. Here are the four ways to achieve polymorphism in Python:

Duck Typing

In duck typing, Python allows you to use an object based on its attributes and method instead of its type. This indicates that it only considers if the object has a required method and provides support for dynamic typing in Python. 

For example, if an object looks like a list, Python will consider it a list type. With duck typing, you can focus on what an object can do instead of worrying about its specific type. 

Let’s look at an example of how duck typing is implemented in Python:

# Define a class Dog with a method speak
class Dog:
    def speak(self):
        return "Yes, it barks bow bow"

# Define a class Cat with a method speak
class Cat:
    def speak(self):
        return "Yes, it cries meow meow"  

# Function that takes any animal object and calls its speak method
def animal_sound(animal):
    return animal.speak()  # Calls the speak method of the passed object

# Create instances of Dog and Cat
dog = Dog()
cat = Cat()

# Call animal_sound function with the dog and cat objects, printing their sounds
print(animal_sound(dog))  
print(animal_sound(cat))

Sample Output:

In the above code, the animal_sound() function works with any object passed to it as long as that object has a speak() method. The animal_sound() function does not check if the object is an instance of Dog, Cat, or another class; it just calls speak() on the object, assuming it will behave as expected. This flexibility through the duck typing concept enables the Python compiler to focus on what an object can do rather than its actual type or class.  

Method Overriding

Method overriding occurs when a subclass provides a specific implementation of a method that is already defined in its parent class. 

Here is an example:

# Base class Vehicle with a start method
class Vehicle:
    def start(self):
        return "Vehicle is starting."  

# Subclass Car that inherits from Vehicle and overrides the start method
class Car(Vehicle):
    def start(self):
        return "Car is starting."  

# Creating instances of Vehicle and Car
vehicle = Vehicle()
car = Car()

print(vehicle.start())  
print(car.start())

Sample Output:

Method Overloading

Python does not support traditional method overloading as Java does. However, you can implement it using default arguments or handling different input types. Let’s see an example:

class Calculator:
    def add(self, a, b=None):
        if b is not None:
            return a + b  
        return a  

# Create an instance of the Calculator class
calc = Calculator()

# Calling the add method with two arguments
print(calc.add(5, 10))

# Calling the add method with one argument
print(calc.add(5))      

Sample Output:

Operator Overloading

Operator overloading allows you to define custom methods for standard operators like + or -.

Here is an example:

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __add__(self, other):
        return Point(self.x + other.x, self.y + other.y)

    def __repr__(self):
        return f"Point({self.x}, {self.y})"

p1 = Point(2, 3)
p2 = Point(4, 1)

p3 = p1 + p2
print(p3)  

Sample Output:

Encapsulation

Encapsulation is the practice of restricting direct access to certain attributes and methods of an object to protect data integrity. In Python, you can achieve data encapsulation by prefixing an attribute with a single underscore (_) or a double underscore (__). 

Attributes marked with a single underscore are considered protected. These attributes can be accessed from within the classes and their derived classes but not from outside the class. If the attributes prefixed with a double underscore are private, they are intended to be inaccessible from within or outside the class. This mechanism ensures better data security and helps you maintain the internal state of an object.

Let’s see an example to achieve encapsulation in Python:

class Person:
    def __init__(self, name, age, phone_number):
        self.name = name                # Public attribute
        self.age = age                  # Public attribute
        self._email = None              # Protected attribute
        self.__phone_number = phone_number  # Private attribute

    def get_phone_number(self):
        return self.__phone_number  # Public method to access the private attribute

    def set_phone_number(self, phone_number):
        self.__phone_number = phone_number  # Public method to modify the private attribute

    def set_email(self, email):
        self._email = email  # Public method to modify the protected attribute

    def get_email(self):
        return self._email  # Public method to access the protected attribute

# Creating an instance of the Person class
person = Person("Xyz", 20, "9863748743")

# Accessing public attributes
print(person.name)  
print(person.age)   

# Accessing protected attribute
person.set_email("xyz@sample.com")
print(person.get_email())  

# Accessing a private attribute using public methods
print(person.get_phone_number())  

# Modifying the private attribute using a public method
person.set_phone_number("123456789")
print(person.get_phone_number())

Sample Output:

Data Abstraction

Abstraction helps you hide complex implementation details and only show an object’s essential features. The Abstract Base Classes (ABC) module allows you to implement data abstraction in your Python program through abstract classes and methods. 

Here is an example of implementing abstraction:

# Importing ABC module to define abstract classes
from abc import ABC, abstractmethod

# Defining an abstract class Animal that inherits from ABC
class Animal(ABC):
    # Declaring an abstract method move that must be implemented by subclasses
    @abstractmethod
    def move(self):
        pass  # No implementation here; subclasses will provide it

# Defining a class Bird that inherits from the abstract class Animal
class Bird(Animal):
    # Implementing the abstract method move for the Bird class
    def move(self):
        print("Flies")  

# Creating an instance of the Bird class
bird = Bird()
bird.move()

Sample Output:

Use Cases of Python OOPs

• Game Development: Python OOPs help you create maintainable code by defining classes for game entities such as characters, enemies, and items. 
• Web Development: OOP concepts enable you to develop web applications by organizing the code into classes and objects. 
• GUI Applications: In GUI development, utilizing OOP concepts allows you to reuse the code of the components like buttons and windows. This will enhance the organization and scalability of your application. 
• Data Analysis: In Python, you can structure data analysis workflows using classes, encapsulating required data processing methods and attributes.

10 Best Practices for OOP in Python

1. You must use a descriptive naming convention, follow the CapWords for classes, and lowercase with an underscore for attributes and methods. 
2. Ensure each class has only one responsibility to reduce the complexity of modifying it according to the requirements. 
3. Reuse code through functions and inheritance to avoid redundancy. 
4. You must add comments next to your Python classes and methods with docstrings (“””) to understand the code in flow. 
5. Optimize your memory usage using the __slots__ method in your classes. 
6. Keep inheritance hierarchies simple to maintain readability and manageability in your code.
7. Minimize the number of arguments in your methods to improve the code clarity and fix the errors quickly. 
8. Utilize duck typing or other polymorphism techniques to write a flexible program that adapts to varying data types.
9. Write unit tests for your classes to ensure that modifications do not break existing functionality. 
10. Leverage lazy loader packages to initialize the objects on demand, which can improve performance and resource management in your application. 

Conclusion

You have explored Python OOPs with examples in this article. By understanding key OOP concepts like classes, objects, inheritance, polymorphism, encapsulation, and abstraction, you are well-equipped to build scalable Python applications. The various use cases highlighted show the versatility of OOP in applications ranging from game development to data analysis. In addition, following the recommended best practices ensures that your OOP implementations remain clean, efficient, and reliable. 

FAQs

How do you modify properties on objects?

You can modify the properties of an object by directly accessing them and assigning new values. 

For example: 

class Car:

    def __init__(self, color):

        self.color = color

my_car = Car("red")

print(my_car.color)     

# Modifying the car color from red to yellow

my_car.color = "yellow" 

print(my_car.color)

Can you delete properties on objects?

Yes, you can delete the object attributes using the del keyword. 

How do static methods and class methods differ from instance methods in Python? 

Instance methods operate on an object and can access its properties using self parameter. In contrast, class methods work on the class itself and are defined with the @classmethod decorator. They accept cls as the first parameter. Compared to these methods, static methods do not accept any parameters. It is defined with a @staticmethod decorator and cannot access or modify class or object data. 

The field of data science continues to develop advancements in machine learning, automation, computing, and other big data technologies. These advancements allow various professionals to easily interpret, analyze, and summarize data. Looking ahead to 2025, you can expect to see even more robust data science tools that will revolutionize how your business makes decisions.

This article will discuss the top tools utilized by data science professionals to navigate through the continuously changing data landscape.

What is Data Science? 

Data science is a multidisciplinary approach. It combines principles and practices from the fields of mathematics, statistics, AI, and computational engineering. You can use data science to study datasets and get meaningful insights. These insights help you answer critical questions about your business problem, such as what happened, why it happened in a certain way, and what can be done. 

Data Science Life Cycle

The data science life cycle is a structured framework with several key steps. The process starts by identifying the problem your business aims to solve. Once the problem is clearly defined, you can extract relevant data from sources such as databases, data lakes, APIs, and web applications to support the analysis process. 

The collected data comes in different forms and structures, so it needs to be cleaned and transformed. This process is called data preparation, and it includes handling missing values, data normalization, aggregation, and more. After the data is ready, you can conduct exploratory analysis using statistical techniques to understand the correlations and patterns within it. 

Through reporting, the insights gained from EDA are communicated to stakeholders, business decision-makers, and relevant teams. The insights help the decision-makers analyze all the aspects of the business problem and related solutions, facilitating better decision-making.  

5 Data Science Tools and Technologies To Lookout For in 2025

1. Automated Machine Learning (ML) Tools 

Auto ML tools simplify the creation and building of machine learning models. These tools automate tasks like module selection, which helps you identify the most appropriate ML algorithm and implement hyperparameter tuning to optimize model performance. They also help you with feature engineering, which enables you to select features that improve model accuracy. In the next few years, these tools will democratize data science by enabling non-experts to build machine learning models with minimal coding.

Following are two robust Auto ML tools: 

DataRobot

DataRobot is a robust AI platform designed to automate and simplify the machine learning lifecycle. It helps you build, govern, and monitor your enterprise AI, where the application can be organized using three stages. 

The first stage is Build, which focuses on organizing datasets to create predictive and generative AI models. Developing a model that generates new content or predicts outcomes requires a lot of trial and error. WorkBench is an interface offered by DataRobot that simplifies the modeling process, enabling efficient training, tuning, and comparison of different models.  

The second stage is called Govern. Here, you create a deployment-ready model package and compliance documentation using a Registry. It is another robust solution offered by DataRobot. Through Registry, you can register and test your model and then deploy it with a single click. DataRobot’s automation will create an API endpoint for your model in your selected environment.

The third stage involves monitoring the operating status of each deployed model. For this, DataRobot uses Console, a solution that provides a centralized hub. The Console allows you to observe a model’s performance and configure numerous automated interventions to make adjustments. 

Azure Auto ML 

Azure machine learning simplifies the model training process by automating the experimentation. During the training phase, Azure ML creates parallel pipelines that run different algorithms and parameters for you. It iterates through algorithms paired with feature selection, producing a different model with a training score. The iteration stops once it fulfills the exit criteria, which are defined in the experiment. The better the score, the more the model is fitted for your dataset. 

2: DataOps Tools 

Data operation tools are software that help your organization improve and simplify various aspects of data management and analytics. The tools provide you with a unified platform where you can perform the data operations and easily collaborate with teams, sharing and managing data. These operations include data ingestion, transformation, cataloging, quality check, monitoring, and more. Using the data operations tools, you can reduce the time to insight and improve data quality for the analysis process.

Here are two popular data operation tools: 

Apache Airflow 

Apache Airflow is a platform that you can optimize to develop, schedule, and monitor batch-oriented workflows programmatically. It allows you to create pipelines using standard Python, which includes date-time formats for scheduling. 

The Airflow UI helps you monitor and manage your workflows, giving you a complete overview of the status of your completed and ongoing tasks. Airflow provides many play-and-plug operators, which enable you to execute tasks on Google Cloud, AWS, Azure, and other third-party services. Using flow, you can also build ML models and manage your infrastructure. 

Talend

Talend is a robust data management tool. The Talend Data Fabric combines data integration, quality, and governance in a single low-code platform. You can deploy Talend on-premises, in the cloud, or in a hybrid environment. It enables you to create ELT/ETL pipelines with change data capture functionality that helps you integrate batch or streaming data from the source.  

Using Talend Pipeline Designer, you can build and deploy pipelines to transfer data from a source to your desired destination. This data can be utilized to derive business insights. In addition, Talend also provides solutions such as data inventory and data preparation for data cleaning and quality improvement.

3: Graph Analytics 

Graph analytics is a technique or a method that is focused on studying and determining the relationship between different data entities. Using this method, you can analyze the strengths and relationships among data points represented on the graph. Some examples of data that are well-suited for graph analysis include road networks, communication networks, social networks, and financial data. 

Here are two robust graph analytics tools: 

Neo4j

At its core, Neo4j is a native graph database that stores and manages data in a connected state. It stores data in the form of nodes and relationships instead of documents or tables. It has no pre-defined schema, providing a more flexible storage format. 

Besides a graph database, Neo4j provides a rich ecosystem with comprehensive tool sets that improve data analytics. The Neo4j Graph Data Science gives you access to more than 65 graph algorithms. You can execute these algorithms with Neo4j, optimizing your enterprise workloads and data pipelines to get insights and answers to critical questions. 

Neo4j also offers various tools that make it easy for you to learn about and develop graph applications. Some of these tools include Neo4j Desktop, Neo4j Browser, Neo4j Operations Manager, Video Series, Neo4j Bloom and Data Importer.

Amazon Neptune 

Amazon Neptune is a graph database service offered by AWS that helps you build and run applications that work with highly connected datasets. It has a purpose-built, high-performance graph database engine optimized for storing relational data and querying the graph. Neptune supports various property-graph query languages, such as Apache Tinker Pop Gremline, W3C’s RDF, SPARQL, and Neo4j’s Open Cypher. 

The support for these languages enables you to build queries that efficiently navigate to connected data. It also includes features like read replicas, point-in-time recovery, replication across availability zones, and continuous backup, which improve data availability. Some graph use cases of Neptune are fraud detection, knowledge graphs, network security, and recommendation systems. 

4: Edge Computing 

The data generated by connected devices is unprecedented and quite complex. Edge computing is a distributed framework that helps you analyze this data more efficiently. It brings computation and storage closer to the data sources. The connected devices either process data locally or using a nearby server (edge). 

This method reduces the need to send large amounts of data to distant cloud servers for processing. Reducing the amount of data transferred not only conserves bandwidth but also speeds up data analysis. It also enhances data security by limiting the exposure to sensitive information sent to the cloud. In the coming year, edge computing will allow you to deploy models directly over devices, reducing latency and improving business performance.

The following are two robust Edge Computing tools: 

Azure IoT Edge 

Azure IoT Edge is a device-focused runtime. It is a feature of the Azure IoT hub that helps you scale out and manage IoT solutions over the cloud. Azure Edge allows you to run, deploy, and manage your workloads by bringing analytical power closer to your devices. 

It is made up of three components. The first is IoT Edge modules, which can be deployed to IoT Edge devices and executed locally. The second is IoT Edge runtime, which manages modules deployed on each device. The third is the cloud-based interface to monitor these devices remotely. 

AWS IoT Greengrass

AWS IoT Greengrass is an open-source edge run-time service offered by Amazon. It helps you build, deploy, and manage device software and provides a wide range of features that accelerate your data processing operations. Greengrass’s Local processing functionality allows you to respond quickly to local events. It supports various AWS IoT Device Shadows functions, which cache your device’s state and help you synchronize it with the cloud when connectivity is available. 

Greengrass also provides an ML Inference feature, making it easy for you to perform ML inference locally on its devices using models built and trained on the cloud. Other features of Greengrass include data stream management, scalability, updates over the air, and security features to manage credentials, access control, endpoints, and configurations.

5: Natural Language Processing Advancements

Natural Language Processing is a subfield of data science. It enables computers or any digital device to understand, recognize, and create text and speech by combining computational linguistics, statistical modeling, and machine learning methods. 

NLP has already become a part of your everyday life. It is used to power search engine systems, prompt chatbots to provide better customer service, and for question-answering assistant devices like Amazon’s Alexa or Apple Siri. By 2025, NLP will play a significant role in helping LLMs and Gen AI applications. It will help you to understand user requests better and provide assistance in developing more robust conversational applications. 

Types of NLP tools

There are various types of NLP tools that are optimized for different tasks, including: 

• Text Processing tools for breaking down raw text data into manageable components and helping you clean and structure it. Some examples include spaCy, NLTK Stopwords, and Stanford POS Tagging.
• Sentiment Analysis tools are utilized to analyze emotions in the text, such as positive, negative, and neutral. Some examples include, but are not limited to, VADER and TextBlob.
• Text Generation tools are used to generate text based on input prompts. Some examples of these tools include ChatGPT4 and Gemini.
• Machine translation tools, such as Google Translate, help you automatically translate text between languages. 

Importance of Data Science Tools

Data science tools help in enhancing various business capabilities. From data interpretation to strategic planning, it helps your organization to improve efficiency and gain a competitive edge. Below are some key areas where these tools provide value:

• Problem Solving: Data science tools assist your business in identifying, analyzing, and solving complex problems. These tools can uncover patterns and insights from vast datasets. For instance, if a particular business product or service is underperforming, your team can use data science tools to get to the root of the problem. A thorough analysis will help you improve your product.
• Operational Efficiency: Data science tools help you automate tasks such as data clearing, processing, and reporting. This automation not only saves time but also improves data quality, enhancing operational efficiency. 
• Customer Understanding: You can get insights into customer data such as buying behavior, preferences, and interaction with products or services using data science tools. This helps you understand them better and provide personalized recommendations to them to improve customer engagement. 
• Data-Driven Decision Making: Some data science tools utilize advanced ML algorithms to facilitate in-depth analysis of your data. This analysis provides insights that help your business make data-backed decisions rather than going with intuition. These decisions facilitate better resource allocation and risk management strategies. 

Conclusion 

In 2025, the field of data science is poised for significant advancements that will generate new opportunities in various business domains. These advancements will enable you to build and deploy models to improve operational performance and facilitate innovation. Tools like automated ML, data integration, edge computing, graph analytics, and more will play a major role in harnessing the value of data and fostering data-driven decisions.

FAQs 

What Is the Trend in Data Science in 2024?

AI and machine learning are two of the most significant trends shaping algorithms and technologies in data science.

What are the Three V’s of Data Science? 

The three Vs of data science are volume, velocity, and variety. Volume indicates the amount of the data, velocity indicates the processing speed, and variety defines the type of data to be processed.

Is Data Science a Good Career Option in the Next Five Years? 

Yes, data science is a good career choice. The demand for data science professionals such as data analysts and machine learning is growing, and they are one of the highest-paying jobs in the field.

Data has become increasingly crucial to make decisions that provide long-term profits, growth, and sustenance. To gain an edge over your competitors, you need to cultivate a data-literate workforce capable of employing effective data management practices and maximizing your data’s potential. 

This article comprehensively outlines the key elements of data management, its benefits, and its challenges, allowing you to develop and leverage robust strategies.  

What Is Data Management? 

Data management involves collecting, storing, organizing, and utilizing data while ensuring its accessibility, reliability, and security. Various data strategies and tools can help your organization manage data throughout its lifecycle. 

With effective data management, you can leverage accurate, consistent, and up-to-date data for decision-making, analysis, and reporting. This enables you to streamline your business operations, drive innovation, and outperform your competitors in the market. 

Why Data Management Is Important

Data management is crucial as it empowers you to transform your raw data into a valuable and strategic asset. It helps create a robust foundation for future digital transformation and data infrastructure modernization efforts. 

With data management, you can produce high-quality data and use it in several downstream applications, such as generative AI model training and predictive analysis. It also allows you to extract valuable insights, identify potential bottlenecks, and take active measures to mitigate them.

Increased data availability, facilitated by rigorous data management practices, gives you enough resources to study market dynamics and identify customer behavior patterns. This provides you with ideas to improve your products and enhance customer satisfaction, leading to the growth of a loyal user base. 

Another application of high-standard data management is adhering to strict data governance and privacy policies. By having a complete and consistent view of your data, you can effectively assess the loopholes in security requirements. This prevents the risk of cyber attacks, hefty fines, and reputational damage associated with failing to comply with privacy laws like CCPA, HIPAA, and GDPR. 

Key Elements of Data Management

Data management is a major aspect of modern organizations that involves various components that work together to facilitate effective data storage, retrieval, and analysis. Below are some key elements of data management:

Database Architecture

Database architecture helps you define how your data is stored, organized, and accessed across various platforms. The choice of database architecture—whether relational, non-relational, or a modern approach like data mesh—depends on the nature and purpose of your data. 

Relational databases use a structured, tabular format and are ideal for transactional operations. Conversely, non-relational databases, including key-value stores, document stores, and graph databases, offer greater flexibility to handle diverse data types, such as unstructured and semi-structured data.

Data mesh is a decentralized concept that distributes ownership of specific datasets to domain experts within the organization. It enhances scalability and encourages autonomous data management while adhering to organizational standards. All these architectures offer versatile solutions to your data requirements. 

Data Discovery, Integration, and Cataloging 

Data discovery, integration, and cataloging are critical processes in the data management lifecycle. Data discovery allows you to identify and understand the data assets available across the organization. This often involves employing data management tools and profiling techniques that provide insights into data structure and content.

To achieve data integration (unifying your data for streamlined business operations), you must implement ETL (Extract, Transform, Load) or ELT. Using these methods, you can collect data from disparate sources while ensuring it is analysis-ready. You can also use data replication, migration, and change data capture technologies to make data available for business intelligence workflows. 

Data cataloging complements these efforts by helping you create a centralized metadata repository, making it easier to find and utilize the data effectively. Advanced tools like Azure data catalog, Looker, Qlik, and MuleSoft that incorporate artificial intelligence and machine learning can enable you to automate these processes. 

Data Governance and Security

Data governance and security are necessary to maintain the integrity and confidentiality of your data within the organization. With a data governance framework, you can establish policies, procedures, and responsibilities for managing data assets while ensuring they comply with relevant regulatory standards. 

Data security is a crucial aspect of governance that allows you to safeguard data from virus attacks, unauthorized access, malware, and data theft. You can employ encryption and data masking to protect sensitive information, while security protocols and monitoring systems help detect and respond to potential vulnerabilities. This creates a trusted environment for your data teams to use data confidently and drive profitable business outcomes. 

Metadata Management

Metadata management is the process of overseeing the creation, storage, and usage of metadata. This element of data management provides context and meaning to data, enabling you to perform better data integration, governance, and analysis. 

Effective metadata management involves maintaining comprehensive repositories or catalogs documenting the characteristics of data assets, including their source, format, structure, and relationships to other data. This information not only aids in data discovery but also supports data lineage, ensuring transparency and accountability.

Benefits of Data Management

You can optimize your organization’s operations by implementing appropriate data management practices. Here are several key benefits for you to explore:

Increased Data Visibility 

Data management enhances visibility by ensuring that data is organized and easily accessible across the organization. This visibility allows stakeholders to quickly find and use relevant data to support business processes and objectives. Additionally, it fosters better collaboration by providing a shared understanding of the data. 

Automation

By automating data-related tasks such as data entry, cleansing, and integration, data management reduces manual effort and minimizes errors. Automation also streamlines workflows, increases efficiency, and allows your teams to focus on high-impact activities rather than repetitive tasks.

Improved Compliance and Security

Data management ensures that your data is governed and protected according to the latest industry regulations and security standards. This lowers the risk of penalties associated with non-compliance and showcases your organization’s ability to handle sensitive information responsibly, boosting the stakeholders’ trust.  

Enhanced Scalability

A well-structured data management approach enables your data infrastructure to expand seamlessly and accommodate your evolving data volume and business needs. This scalability is essential for integrating advanced technologies and ensuring your infrastructure remains agile and adaptable, future-proofing your organization. 

Challenges in Data Management

The complexity of executing well-structured data management depends on several factors, some of which are mentioned below:   

Evolving Data Requirements

As data diversifies and grows in volume and velocity, it can be challenging to adapt your data management strategies to accommodate these changes. The dynamic nature of data, including new data sources and types, requires constant updates to storage, processing, and governance practices. Failing to achieve this often leads to inefficiencies and gaps in data handling.

Talent Gap

A significant challenge in data management is the shortage of data experts who can design, implement, and maintain complex data systems. Rapidly evolving data technologies have surpassed the availability of trained experts, making it difficult to find and retain the necessary talent to manage data effectively.

Faster Data Processing

The increased demand for real-time insights adds to the pressure of processing data as fast as possible. This requires shifting from conventional batch-processing methods to more advanced streaming data technologies that can handle high-speed, high-volume data. Integrating the latest data management tools can significantly impact your existing strategies for managing data efficiently. 

Interoperability

With your data stored across diverse systems and platforms, ensuring smoother communication and data flow between these systems can be challenging. The lack of standardized formats and protocols leads to interoperability issues, making data management and sharing within your organization or between partners a complicated process.

Modern Trends in Data Management

Data management is evolving dynamically due to technological advancements and changing business needs. Some of the most prominent modern trends in data management include:

Data Fabric

A data fabric is an advanced data architecture with intelligent and automated systems for data access and sharing across a distributed environment (on-premises or cloud). It allows you to leverage metadata, dynamic data integration, and orchestration to connect various data sources, enabling a cohesive data management experience. This approach helps break down data silos, providing a unified data view to enhance decision-making and operational efficiency.

Shared Metadata Layer

A shared metadata layer is a centralized access point to data stored across different environments, including hybrid and multi-cloud architectures. It facilitates multiple query engines and workloads, allowing you to optimize performance using data analytics across multiple platforms. The shared metadata layer also catalogs metadata from various sources, enabling faster data discovery and enrichment. This significantly simplifies data management.  

Cloud-Based Data Management

Cloud-based data management offers scalability, flexibility, and cost-efficiency. By migrating your data management platforms to the cloud, you can use advanced security features, automated backups, disaster recovery, and improved data accessibility. Cloud solutions like Database-as-a-Service (DBaaS), cloud data warehouses, and cloud data lakes allow you to scale your infrastructure on demand. 

Augmented Data Management

Augmented data management is the process of leveraging AI and machine learning to automate master data management and data quality management. This automation empowers you to create data products, interact with them through APIs, and quickly search and find data assets. Augmented data management enhances the accuracy and efficiency of your data operations and enables you to respond to changing data requirements and business needs effectively.

Semantic Layer Integration

With semantic layer integration, you can democratize data access and empower your data teams. This AI-powered layer abstracts and enriches the underlying data models, making them more accessible and understandable without requiring SQL expertise. Semantic layer integration provides a clear, business-friendly view of your data, accelerates data-driven insights, and supports more intuitive data exploration.

Data as a Product

The concept of data as a product (DaaP) involves treating data as a valuable asset that you can package, manage, and deliver like any other product. It requires you to create data products that are reusable, reliable, and designed to meet specific business needs. DaaP aims to maximize your data’s utility by ensuring it is readily available for analytics and other critical business functions. 

Wrapping It Up

Data management is an essential practice that enables you to collect, store, and utilize data effectively while ensuring its accessibility, reliability, and security. By implementing well-thought strategies during the data management lifecycle, you can optimize your organization’s data infrastructure and drive better outcomes. 

Data management tools, Innovations like data fabric, augmented data management, and cloud-based solutions can increase the agility of your business processes and help meet your future business demands.  

FAQs

What are the applications of data management?

Some applications of data management include:

• Business Intelligence and Analytics: With effective data management, you can ensure data quality, availability, and accessibility to make informed business decisions.
• Risk Management and Compliance: Data management helps you identify and mitigate risks, maintain data integrity, and meet regulatory requirements.
• Supply Chain Management: Implementing data management can improve the visibility, planning, and cost-effectiveness of supply chain operations.   

What are the main careers in data management?

Data analyst, data engineer, data scientist, data architect, and data governance expert are some of the mainstream career roles in data management. 

What are the six stages of data management?

The data management lifecycle includes six stages, viz., data collection, storage, usage, sharing, archiving, and destruction. 

What are data management best practices?

Complying with regulatory requirements, maintaining high data quality, accessibility, and security, and establishing guidelines for data retention. These are some of the best data management practices.    

Business organizations view data as an essential asset for their business growth. Well-organized data helps them make well-informed decisions, understand their customers, and gain a competitive advantage. However, a huge volume of data is required to achieve these goals, and managing such large-scale data can be extremely difficult. This is where the data warehouses can play an important role. 

Data warehouses allow you to collect data scattered across different sources and store it in a unified way. You can then use this data to perform critical tasks such as sales prediction, resource allocation, or supply chain management. Considering these capabilities, let’s learn what a data warehouse is and how you can utilize it for business intelligence functions. 

What is a Data Warehouse?

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A data warehouse is a system that enables you to store data collected from multiple sources, such as transactional databases, flat files, or data lakes. After this, you can either directly load the data in raw form or clean, transform, and then transfer it to the data warehouse. 

So, the data warehouse acts as a centralized repository that allows you to retrieve the stored data for analytics and business intelligence purposes. In this way, the data warehouse facilitates effective storage and querying of data to simplify its use for real-life applications.

Overview of Data Warehouse Architecture

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Different data warehouses cater to varied data requirements, but most of them comprise similar basic architectural components. Let’s have a look at some of the common architectural elements of a data warehouse:

Central Database

The central database is the primary component of storage in a data warehouse. Traditionally, data warehouses consisted of on-premise or cloud-based relational databases as central databases. However, with the rise of big data and real-time transactions, in-memory central databases are becoming popular.

Data Integration Tools

Data integration tools enable you to extract data from various source systems. Depending on your requirements, you can prefer the ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) method to transfer this extracted data to a data warehouse. 

ETL is the preferred approach, wherein you must first clean and transform data using suitable data manipulation solutions. In ELT, you can directly load the unprocessed data in the warehouse and then perform transformations. 

Metadata

Metadata is data that provides detailed information about data records stored in warehouses. It includes:

• Location of data warehouse along with description of its components
• Names and structure of contents within the data warehouse
• Integration and transformation rules
• Data analysis metrics
• Security mechanism used to protect data

Understanding metadata helps you to design and maintain a data warehouse effectively.

Data Access Tools

Access tools enable you to interact with data stored in data warehouses. These include querying tools, mining tools, OLAP tools, and application development tools.

Data Warehouse Architectural Layers

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The architectural components of the data warehouse are arranged sequentially to ensure streamlined data warehousing processes. This ordered organization of components is called a layer, and there are different types of layers within a data warehouse architecture. Here is a brief explanation of each of these layers:

Data Source Layer

This is the first layer where you can perform data extraction. It involves collecting data from sources such as databases, flat files, log applications, or APIs.

Data Staging Layer

This layer is like a buffer zone where data is temporarily stored before you transform it using the ETL approach. Here, you can use filtering, aggregation, or normalization techniques to make the raw data analysis-ready. In the ELT approach, the staging area is within the data warehouse. 

Data Storage Layer

Here, the cleaned and transformed data is stored in a data warehouse. Depending upon the design of your data warehouse, you can store this data in databases, data marts, or operational data stores (ODS). Data marts are a smaller subset of data warehouses that enable the storage of essential business data for faster retrieval. 

ODS, on the other hand, is a data storage system that helps you perform significant business operations in real-time. For example, you can use ODS to store customer data for your e-commerce portal and utilize it for instant bill preparation.

Data Presentation Layer

In the presentation layer, you can execute queries after retrieving data to gain analytical insights. For better results, you can also leverage business intelligence tools like Power BI or Tableau to visualize your data. 

Types of Data Warehouses

Traditionally, data warehouses were deployed on-premise, but now you can opt for cloud-based solutions for better data warehousing experience. Other than this, the data warehouses can be classified into the following types:

Enterprise Data Warehouse

Large business organizations use enterprise data warehouses as a single source of truth for all their data-related tasks. They are useful for enterprise data management as well as for conducting large-scale analytical and reporting operations. 

Departmental Data Warehouse

Departmental data warehouses are used by specific departments, such as sales, marketing, finance, or small business units. They enable efficient management of medium to small datasets.

Data Mart

Data Marts are a subset of a large data warehouse usually used for faster data retrieval in high-performance applications. They require minimal resources and less time for data integration. For effective usage, you can opt for data marts to manage departmental data such as finance or sales. 

Online Analytical Processing (OLAP) Data Warehouse

OLAP data warehouses facilitate complex querying and analysis on large datasets using OLAP cubes. These are array-based multidimensional databases that allow you to analyze higher dimensional data easily.

Benefits of Data Warehouse

Data warehouses help streamline the data integration and analytics processes, enabling better data management and usage in any organization. Let’s briefly discuss some benefits of using a data warehouse: 

High Scalability

Modern cloud-based data warehouses offer high scalability by providing flexibility to adjust their storage and compute resources. As a result, you can accommodate large volumes of data in data warehouses. 

Time-saving

A data warehouse is a centralized repository that you can use to manage your data effectively. It supports data consolidation, simplifying the processes of accessing and querying data. This saves a lot of time, as you do not have to reach out to different sources each time while performing analytical operations. You can utilize this time to perform more important business tasks.

Facilitates High-Quality Data

It is easier to transform and clean the data stored in a unified manner within the data warehouse. You can perform aggregation operations, handle missing values, and remove duplicates and outlier data points in bulk on these datasets. This allows you access to standardized and high-quality data to develop businesses.

Improves Decision-making

You can analyze the centralized and transformed data in a data warehouse using analytical tools like Qlik, Datawrapper, Tableau, or Google Analytics. The data analysis outcomes provide useful information about workflow efficiency, product performance, sales, and churn rates. Using these insights, you can understand the low-performing areas and make effective decisions to refine them.

Challenges of Using Data Warehouse

While data warehouses provide numerous advantages, there are some challenges associated with their usage. Some of these challenges are:

Maintenance Complexities

Managing large volumes of data stored in traditional data warehouses or marts can be difficult. Tasks like regularly updating the data, ensuring data quality, and tuning the data warehouse for optimal query performance are complex. 

Data Security Concerns

You may face difficulties while ensuring data security in data warehouses. For this, it is essential to frame robust data governance frameworks and security protocols. Measures such as role-based access control and encryption are effective but limit data availability. 

Usually, large businesses use data warehouses, where there is a high probability of data breaches. This leads to financial losses, reputational damages, and penalties for violating regulations.

Lack of Technical Experts

Using a data warehouse requires sufficient knowledge of data integration, querying, and analysis processes. A lack of such skills can lead to issues such as poor data quality and the creation of non-useful outcomes during data analysis. You and your team should also have hands-on experience in diagnosing and resolving problems if there is a system failure.

High Deployment Cost

The cost of implementing data warehouses is very high due to the sophisticated infrastructure and technical workforce requirements. As a result, small businesses with limited budgets cannot utilize data warehouses. Even for large companies, ROI is the biggest concern, as there can be doubts about recovering the money they invested in implementation. 

Best Practices for Optimal Use of Data Warehouses

As you have seen in the previous section, there are some constraints to using data warehouses. To overcome them, you can adopt the following best practices:

Understand Your Data Objectives

First, clearly understand why you want to use a data warehouse in your organization. Then, interact with senior management, colleagues, and other stakeholders to inform them about how data warehouses can streamline organizational workflow. 

Use Cloud-based Data Warehousing Solutions

Numerous cloud-based data warehouses help you to manage business data efficiently. They offer flexibility and scalability to store and analyze large amounts of data without compromising performance. Many data warehouses support pay-as-you-go pricing models, making them cost-effective solutions. You also do not have to worry about infrastructure management when using cloud data warehouses. 

Prefer ELT Over ETL

ETL and ELT are two popular data integration methods used in data warehousing. Both help you collect and consolidate data from various sources into a unified location. However, ELT can be helpful for near-real-time operations as you can directly load data into the data warehouse, and transformation can be performed selectively later. 

Define Access Control in Advance

Clearly define the access rules based on the job roles of all your employees to ensure data security. If possible, classify data as confidential and public to protect sensitive data like personally identifiable information (PII). You should also regularly monitor user activity to detect any unusual patterns. 

Conclusion

A data warehouse can play an important role in your business organization if you are looking for efficient ways to tap the full potential of your data. It allows you to store data centrally and query and analyze it to obtain valuable information related to your business. You can use this knowledge to streamline workflow and make your business profitable.

This article explains the data warehouse’s meaning and architecture in detail. It also explains the benefits, challenges, and best practices for overcoming them so that you can take full advantage of data warehouses.

FAQs

What are some highly used data warehouses?

Some popular data warehouses are Amazon Redshift, Snowflake, Google BigQuery, Azure Synapse Analytics, IBM Db2, and Firebolt. 

What is the difference between a data warehouse and a database?

Data warehouses allow you to store and query large volumes of data for business analytics and reporting purposes. Databases, on the other hand, are helpful in querying transactional data of smaller volumes. They efficiently perform routine operations such as inserting, deleting, or updating data records.

Extracting data from websites is crucial for developing data-intensive applications that meet customer needs. This is especially useful for analyzing website data comprising customer reviews. By analyzing these reviews, you can create solutions to fulfill mass market needs.

For instance, if you work for an airline and want to know how your team can enhance customer experience, scraping can be useful. You can scrape previous customer reviews from the internet to generate insights into areas for improvement.

This article highlights the concept of Python web scraping and the different methods you can use to scrape data from web pages.

What Is Python Web Scraping?

Python web scraping is the process of extracting and processing data from different websites. This data can be beneficial for performing various tasks, including building data science projects, training LLMs, personal projects, and generating business reports.

With the insights generated from the scraped data, you can refine your business strategies and improve operational efficiency.

For example, suppose you are a freelancer who wants to discover the latest opportunities in your field. However, the job websites you refer to do not provide notifications, causing you to miss out on the latest opportunities. Using Python, you can scrape job websites to detect new postings and set up alerts to notify you of such opportunities. This allows you to stay informed without having to manually check the sites.

Steps to Perform Python Web Scraping

Web scraping can be cumbersome if you don’t follow a structured process. Here are a few steps to help you create a smooth web scraping process.

Step 1: Understand Website Structure and Permissions

Before you start scraping, you must understand the structure of the website and its legal guidelines. You can visit the website and inspect the required page to explore the underlying HTML and CSS.

To inspect a web page, right-click anywhere on that page and click on Inspect. For example, when you inspect the web scraping page on Wikipedia, your screen will split into two sections to demonstrate the structure of the page.

To check the website rules, you can review the site’s robot.txt file, for example, https://www.google.com/robots.txt. This file provides you with the website’s terms and conditions, which outline the information about the content that is permissible for scraping.

Step 2: Set up the Python Environment

The next step involves the use of Python. If you do not have Python installed on your machine, you can install it from the official website. After successful installation, open your terminal and navigate to the folder where you want to work with the web scraping project. Create and activate a virtual environment with the following code.

python -m venv scraping-env
#For macOS
source scraping-env/bin/activate
#For Windows
scraping-env\bin\activate

This isolates your project from other Python projects on your machine.

Step 3: Select a Web Scraping Method

There are multiple web scraping methods you can use depending on your needs. Popular options include using the Requests library with BeautifulSoup for simple HTML parsing and HTTP requests using web sockets, to name a few. The choice of Python web scraping tools depends on your specific requirements, such as scalability and handling pagination.

Step 4: Handle Pagination

Web pages can be difficult to scrape when the data is spread across multiple pages, or the website supports real-time updates. To overcome this issue, you can use tools like Scrapy to manage pagination. This will help you systematically capture all the relevant data without requiring manual inspection.

Python Scraping Examples

As one of the most robust programming languages, Python provides multiple libraries to scrape data from the Internet. Let’s look at the different methods for importing data using Python:

Using Requests and BeautifulSoup

In this example, we will use the Python Requests library to send HTTP requests. The BeautifulSoup library enables you to pull the HTML and XML files from the web page. By combining the capabilities of these two libraries, you will be able to extract data from any website. If you do not have these libraries installed, you can run this code:

pip install beautifulsoup4
pip install requests

Execute this code in your preferred code editor to perform Python web scraping on an article about machine learning using Requests and BeautifulSoup.

import requests
from bs4 import BeautifulSoup

r = requests.get('https://analyticsdrift.com/machine-learning/')
soup = BeautifulSoup(r.text, 'html.parser')

print(r)
print(soup.prettify())

Output:

The output will produce a ‘Response [200]’ to signify the get request has successfully extracted the page content.

Retrieving Raw HTML Contents with Sockets

The socket module in Python provides a low-level networking interface. It facilitates the creation and interaction with network sockets, enabling communication between programs across a network. You can use a socket module to establish a connection with a web server and manually send HTTP requests, which can retrieve HTML content.

Here is a code snippet that enables you to communicate with Google’s official website using the socket library.

import socket

HOST = 'www.google.com'
PORT = 80

client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_address = (HOST, PORT)
client_socket.connect(server_address)

request_header = b'GET / HTTP/1.0\r\nHost: www.google.com\r\n\r\n'
client_socket.sendall(request_header)

response = ''
while True:
    recv = client_socket.recv(1024)
    if not recv:
        break
    response += recv.decode('utf-8')

print(response)
client_socket.close()

Output:

This code defines a target server, Google.com, and the port as 80 signifies the HTTP port. You can send requests to the server by establishing a connection and specifying the header request. Finally, the server response is converted from UTF-8 to string form and presented on your screen. 

After getting the response, you can parse the data using regular expressions (RegEx), which allows you to search, transform, and manage text data.

Urllib3 and LXML to Process HTML/XML Data

While the socket library provides a low-level interface for efficient network communication, it can be complex to use for typical web-related tasks if you aren’t familiar with network programming details. This is where the urllib3 library can help simplify the process of making HTTP requests and enable you to effectively manage responses.

The following Python web scraping code performs the same operation of retrieving HTML contents from the Google website as the above socket code snippet.

import urllib3
http = urllib3.PoolManager()
r = http.request('GET', 'http://www.google.com')
print(r.data)

Output:

The PoolManager method allows you to send arbitrary requests while keeping track of the necessary connection pool.

In the next step, you can use the LXML library with XPath expressions to parse the HTML data retrieved with urllib3. The XPath is an expression language to locate and extract specific information from XML or HTML documents. On the other hand, the LXML library helps process these documents by supporting XPath expressions.

Let’s use LXML to parse the response generated from urllib3. Execute the code below.

from lxml import html

data_string = r.data.decode('utf-8', errors='ignore')
tree = html.fromstring(data_string)

links = tree.xpath('//a')

for link in links:
    print(link.get('href'))

Output:

In this code, the XPath finds all the <a> tags, which define links available on the page and highlight them in the response. You can check that the response contains all the links on the web page that you wanted to parse.

Scraping Data with Selenium

Selenium is an automation tool that supports multiple programming languages, including Python. It’s mainly used to automate web browsers, which helps with web application testing and tasks like web scraping.

Let’s look at an example of how Selenium can help you scrape data from a test website representing the specs of different laptops and computers. Before executing this code, ensure you have the required libraries. To install the necessary libraries, use the following code:

pip install selenium
pip install webdriver_manager

Here’s the sample code to scrape data using Selenium:

import time
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import TimeoutException, NoSuchElementException

def setup_driver():
    options = webdriver.ChromeOptions()
    options.add_argument("--headless")
    options.add_argument("--disable-gpu")
    options.add_argument("--window-size=1920x1080")
    options.add_argument("--disable-blink-features=AutomationControlled")
    options.add_argument("--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36")
    
    service = Service(ChromeDriverManager().install())
    return webdriver.Chrome(service=service, options=options)

def scrape_page(driver, url):
    try:
        driver.get(url)
        WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CLASS_NAME, "title")))
    except TimeoutException:
        print(f"Timeout waiting for page to load: {url}")
        return []

    products = driver.find_elements(By.CLASS_NAME, "thumbnail")
    page_data = []

    for product in products:
        try:
            title = product.find_element(By.CLASS_NAME, "title").text
            price = product.find_element(By.CLASS_NAME, "price").text
            description = product.find_element(By.CLASS_NAME, "description").text
            rating = product.find_element(By.CLASS_NAME, "ratings").get_attribute("data-rating")
            page_data.append([title, price, description, rating])
        except NoSuchElementException as e:
            print(f"Error extracting product data: {e}")

    return page_data

def main():
    driver = setup_driver()
    element_list = []

    try:
        for page in range(1, 3):
            url = f"https://webscraper.io/test-sites/e-commerce/static/computers/laptops?page={page}"
            print(f"Scraping page {page}...")
            page_data = scrape_page(driver, url)
            element_list.extend(page_data)
            time.sleep(2)

        print("Scraped data:")
        for item in element_list:
            print(item)

        print(f"\nTotal items scraped: {len(element_list)}")

    except Exception as e:
        print(f"An error occurred: {e}")

    finally:
        driver.quit()

if __name__ == "__main__":
    main()

Output:

The above code uses a headless browsing feature to extract data from the test website. Headless browsers are web browsers without a graphical user interface that helps you take screenshots of websites and automate data scraping. To execute this process, you define three functions: setup_driver, scrape_page, and main.

The setup_driver() method configures the Selenium WebDriver to control a headless Chrome browser. It includes various settings, such as disabling the GPU and setting the window size to ensure the browser is optimized for scraping without a GUI.

The scrape_page(driver, url) function utilizes the configured web driver to scrape data from the specified webpage. The main() function, on the other hand, coordinates the entire scraping process by providing arguments to these two functions.

Practical Example of Python Web Scraping

Now that we have explored different Python web scraping methods with examples, let’s apply this knowledge to a practical project.

Assume you are a developer who wants to create a web scraper to extract data from StackOverflow. With this project, you will be able to scrape questions with their total views, answers, and votes.

• Before getting started, you must explore the website in detail to understand its structure. Navigate to the StackOverflow website and click on the Questions tab on the left panel. You will see the recently uploaded questions.

• Scroll down to the bottom of the page to view the Next page option, and click on 2 to visit the next page. The URL of the web page will change and look something like this: https://stackoverflow.com/questions?tab=newest&page=2. This defines how the pages are arranged on the website. By altering the page argument, you can directly navigate to another page.

• To understand the structure of questions, right-click on any question and click on Inspect. You can hover on the web tool to see how the questions, votes, answers, and views are structured on the web page. Check the class of each element, as it will be the most important component when building a scraper.

• After understanding the basic structure of the page, next is the coding. The first step of the scraping process requires you to import the necessary libraries, which include requests and bs4.

from bs4 import BeautifulSoup
import requests

• Now, you can mention the URL of the questions page and the page limit.

URL = "https://stackoverflow.com/questions"
page_limit = 1

• In the next step, you can define a function that returns the URL to the StackOverflow questions page.

def generate_url(base_url=URL, tab="newest", page=1):
    return f"{base_url}?tab={tab}&page={page}"

• After generating the URL in a suitable format, execute the code below to create a function that can scrape data from the required web page:

def scrape_page(page=1):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
    }
    
    response = requests.get(generate_url(page=page), headers=headers)
    soup = BeautifulSoup(response.text, "html.parser")    
    question_summaries = soup.find_all("div", class_="s-post-summary")

    page_questions = []    
    for summary in question_summaries:
        try:
            # Extract question title
            title_element = summary.find("h3", class_="s-post-summary--content-title")
            question = title_element.text.strip() if title_element else "No title found"
            
            # Get vote count
            vote_element = summary.find("div", class_="s-post-summary--stats-item", attrs={"title": "Score"})
            vote_count = vote_element.find("span", class_="s-post-summary--stats-item-number").text.strip() if vote_element else "0"
            
            # Get answer count
            answer_element = summary.find("div", class_="s-post-summary--stats-item", attrs={"title": "answers"})
            answer_count = answer_element.find("span", class_="s-post-summary--stats-item-number").text.strip() if answer_element else "0"
            
            # Get view count
            view_element = summary.find("div", class_="s-post-summary--stats-item", attrs={"title": lambda x: x and 'views' in x.lower()})
            view_count = view_element.find("span", class_="s-post-summary--stats-item-number").text.strip() if view_element else "0"
            
            page_questions.append({
                "question": question,
                "answers": answer_count,
                "votes": vote_count,
                "views": view_count
            })
            
        except Exception as e:
            print(f"Error processing a question: {e}")
            continue
    
    return page_questions

• Let’s test the scraper and output the results of scraping the questions page of StackOverflow.

results = []
for i in range(1, page_limit + 1):
    page_ques = scrape_page(i)
    results.extend(page_ques)

for idx, question in enumerate(results, 1):
    print(f"\nQuestion {idx}:")
    print("Title:", question['question'])
    print("Votes:", question['votes'])
    print("Answers:", question['answers'])
    print("Views:", question['views'])
    print("-" * 80)

Output:

By following these steps, you can build your own StackOverflow question scraper. Although the steps seem easy to perform, there are some important points to consider while scraping any web page. The next section discusses such concerns.

Considerations While Scraping Data

• You must check the robots.txt file and the website’s terms and conditions before scraping. This file and documentation outline the parts of the site that are accessible for scraping, helping ensure you comply with the legal guidelines.

• There are multiple tools that allow you to scrape data from web pages. However, you should choose the best tool according to your specific needs for ease of use and the data type to scrape.

• Before you start scraping any website, it’s important to review the developer tools to understand the page structure. This will help you understand the HTML structure and identify the classes or IDs associated with the data you want to extract. By focusing on these details, you can create effective scraping scripts.

• A website’s server can receive too many requests in a short period of time, which might cause server overload or access restrictions with rate limiting. To overcome this issue, you can consider request throttling, which is a method of adding delays between requests to avoid server overload.

Conclusion

Python web scraping libraries allow you to extract data from web pages. Although there are multiple website scraping techniques, you must thoroughly read the associated documentation of the libraries to understand their functionalities and legal implications.

Requests and BeautifulSoup are among the widely used libraries that provide a simplified way to scrape data from the Internet. These libraries are easy to use and have broad applicability. On the other hand, sockets are a better option for low-level network interactions and fast execution but require more programming.

The urllib3 library offers flexibility in working with high-level applications requiring fine control over HTTP requests. In hindsight, Selenium supports JavaScript rendering, automated testing, and scraping Single-Page Applications (SPAs).

FAQs

Is it possible to scrape data in Python?

Yes, you can use Python libraries to scrape data. 

How to start web scraping with Python?

To start with web scraping with Python, you must learn HTML or have a basic understanding of it to inspect the elements on a webpage. You can then choose any Python web scraping library, such as Requests and BeautifulSoup, for scraping. Refer to the official documentation of these tools for guidelines and examples to help you start extracting data.