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How to Paraphrase Text with the help of AI paraphrase generator?

paraphrase generator

Paraphrasing generators are advanced AI tools that help many writers. So, how does one go about using it?

Paraphrasing tools are a life-saving device for many writers. Removing plagiarism, altering content tone, and rewriting content within seconds are all common traits and reasons for their usage. That’s why their employment in recent times has increased significantly.

When around 80% of college students admit that plagiarizing content is a part of their routine, such tools are even more important to use. Thus, using them is being taught at academic and professional levels.

Today, we’ll understand what paraphrasing tools are and why you must use them. But, most importantly, we’ll be looking at how you should use an AI-based paraphraser. So, let’s begin:

Understanding A Paraphrase Generator: Defining Traits

A paraphrase generator is an AI-enthused tool used for various purposes. Now, an AI-based paraphrasing tool can be software that helps with the writing process. It can be used by copywriters to rephrase content or by students to rewrite essays.

These paraphrase tools are designed to assist and make the writing process more manageable. However, paraphrasing tools are one of the most popular types of AI-based tools. These AI-based assistants even generate content ideas at scale.

But, their primary usage is to recreate or revamp content when needed. To sum it up, here are some of the common elements provided by paraphrasers:

  • Rewriting content quickly
  • Finding alternative synonyms and phrases to describe the same ideas
  • Removing plagiarism by changing the content
  • Offering various content tones
  • Changing up to hundreds of words at a time

These factors are some of the commonly provided aspects of a paraphraser. Since a paraphrasing tool is based on advanced AI algorithms, speedy rewriting is one of the chief traits of a paraphrase tool.

Reason To Use Paraphrase Tool As An Assistant

A paraphrasing tool is a software that helps people rewrite content without plagiarizing. It does this by replacing the original text with synonyms, phrases, or a combination of these.

The goal is to make the rewritten content sound like the original text but still have it be different enough so that it won’t get flagged for plagiarism. To help you understand, here are three main reasons to use AI Paraphrasing tools as an assistant:

  1. Paraphrasing generators are a quick and easy way to rephrase content. They are also useful for plagiarism detection, tone alteration, and rewriting content quickly
  2. Paraphrasing tools can be used for plagiarism detection by comparing the original text with the paraphrase to check if it is an exact copy of the original
  3. Paraphrasing tools can help you quickly rewrite content without worrying about plagiarism and tone

Therefore, paraphrasing tools are great for people who want to rewrite their own blog posts or articles but don’t have time to do so. It’s also good for people who want to change the tone of their writing but don’t know how and want an unbiased opinion on how they should do it.

Paraphrasing Text With The Help of Paraphrase Generator

Using a paraphrase generator is a straightforward paraphrasing process. But, to help you understand the right way of using it, we’ve formulated a basic process that every writer can employ. So, let’s get started:

Step One: Pick A Paraphrasing Tool

The first step is to pick a paraphrase tool. But what do you need to look for when finding one? Besides the tool featuring outstanding AI algorithms, it must have a few key features, such as:

  • Extensive word-count limit, preferably 1000 and above
  • Various supported languages
  • Quick rephrasing
  • Easy UI design

For demonstration, let’s pick a paraphrase generator by Editpad.org. It has all the key features we’ve just discussed, allowing us to rephrase our content quickly.

Step Two: Identify The Purpose

Now that you have a tool, you need to identify the purpose of rephrasing your content using a paraphraser. In most cases, academic or professional, the common goals include:

  • Removing plagiarism
  • Changing content tone
  • Refreshing content, i.e., making it new/better
  • Making content flow better

If your purpose is one of the following four, then you need to keep that in mind from the get-go.

Step Three: Pick Content Tone (If Available)

The third step is to pick a content tone. Granted, not many tools offer those, but some do. Therefore, use it if you have options such as:

  • Fluent
  • Standard
  • Creative

If not, the tool knows which content tone is best for you. So, there might not be a need to pick one. However, if the tool offers it, it’s suggested that you try out each one before you find the one that matches your natural writing tone.

Step Four: Revamp/Rewrite Content

The fourth and main step to paraphrasing text with the help of an AI paraphrasing tool is to paste or upload your content to the tool. Once you do, click on the paraphrasing button. However, some tools might require a captcha check before doing so.

Once the tool begins rewriting, it’ll show you a progress bar, as seen here. When the tool has finished rewriting, this is the outcome you’ll expect:

The content marked in bold is the changed content. You can try to rewrite it once more by copying the rephrased content and pasting it inside the editor. Moreover, you can try additional options like summarizing the rewritten text or checking it for plagiarism.

Step Five: Proofread

The final step you’ll take is to proofread your content. Now, why do you need to proofread if a paraphraser can paraphrase content emphatically? Because:

  • It’ll allow you to match your original content tone
  • Remove any unnatural-sounding phrases/words
  • Find any rare grammatical errors
  • Change or remove the text elements you don’t need

Therefore, it’s imperative that you proofread, regardless of how well a paraphrasing tool rewrites your text.

Conclusion

This is the process every writer should employ, regardless of their setting. For both students and pro writers, this approach can help them paraphrase text with an AI tool quite quickly and effectively. Therefore, identify your purpose and paraphrase away.

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General Motors suspends advertising on Twitter after Elon Musk takeover

General Motors suspends advertising on Twitter

General Motors Co. (GM) has temporarily suspended advertising on Twitter after the head of rival automaker Tesla, Elon Musk, acquired the social media platform on Thursday. 

The Detroit automaker, gearing up to catch up with Tesla in electric vehicle (EV) development, said on Friday that it is in talks with Twitter to discuss how the platform will transform. GM said it will stop advertising until the company has a better understanding of what will happen to it with Musk now at the helm.

GM spokesperson David Barnas said that the company is engaging with Twitter to better understand the platform’s direction under its new ownership. He added that GM has temporarily paused their paid advertising on Twitter, as is the ordinary course of action with a major change in any media platform. GM will continue to carry out its customer care interactions on Twitter.

Read More: Tesla Shares Fall After Production And Deliveries Lag Due To Logistic Hurdles

On October 27, Musk completed the acquisition of Twitter. Musk fired Twitter CEO Parag Agarwal after the acquisition. Legal executive Vijaya Gadde, Chief Financial Officer Ned Segal, and General Counsel Sean Edgett were also fired. There are also speculations about top executives being asked to leave Twitter. 

Since completing his acquisition Thursday, Musk has said he will convene a content council to decide on standards for users and their tweets. Among the considerations will be whether public figures suspended, such as former US President Donald Trump, should be allowed back on the platform.

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Software Testing Courses

software testing courses

Software testing is essential to the software development life cycle (SDLC) since it finds and corrects software bugs. It is a method of evaluating the effectiveness and quality of software applications created with a specific goal in mind and if necessary, making adjustments. Software testing is tedious and repetitive, but new software methodologies combine manual and automated testing. If this excites you and you want to learn software testing, here is a list of top software testing online courses in 2022. 

1. Selenium 4 WebDriver with Java – Udemy

“Selenium 4 WebDriver with Java” is an advanced-level software testing course on Udemy. The course has 48 sections and 462 lectures describing Selenium, Selenium WebDrivers, testing frameworks, allure reporting, Selenium grid, database testing, and more. This course is highly extensive, including seven live projects, teaching how to automate web-based applications and implement various frameworks like data-driven, hybrid, page object model, page factories, cucumber BDD, etc. You also learn major reporting and customization, including TestNG reports, ReportNG, extent reports, allure reports, and cucumber JVM reporting.

Link to the course: Selenium 4 WebDriver with Java

2. ISTQB Software Testing Foundation – Reed

“ISTQB Software Testing Foundation” is a self-paced, software testing online course on Reed. This course is aimed at professionals and beginners who want practical knowledge of software testing fundamentals. The syllabus is designed by the International software testing qualification board (ISTQB) with major chapters on the process of testing, ensuring effective testing, test design techniques and management, choosing test techniques, and the test development process. There are six modules in the course containing different chapters. Additionally, the skills one can learn in this course are the seven testing principles, debugging, static testing, dynamic testing, and keeping software under control. There is no prerequisite for the course, but being familiar with software testing and its terminology helps. The target audience of this course is junior software testing professionals or any job related to software testers. This is a paid software testing course, and also you need to pay separately for exams and assessments to get certification.

Link to the course: ISTQB Software Testing Foundation

3. The Complete 2022 Software Testing Bootcamp – Udemy

“The Complete 2022 Software Testing Bootcamp” is a software testing course on Udemy. The course has 32 sections and 309 lectures, laying down software testing concepts from beginner to advanced level in 27 hours. This vast course includes manual and agile testing basics, API & web service testing, performance testing, freelance testing websites, unit testing, black-box testing techniques, and white-box testing techniques. This course is a source of everything a software tester needs to learn and has no prerequisites. The course targets people who want to begin a new career and those who are up for a part-time or freelance job in software testing.   

Link to the course: The Complete 2022 Software Testing Bootcamp

Read more: Amazon Rolls out Alexa Skill A/B testing tool to Boost Voice App Engagement

4. Software Testing and Automation Specialization – Coursera

“Software Testing and Automation Specialization” is a series of courses in software testing that provides extensive training in software testing for approximately four months. The course is available on Coursera and offered by the University of Minnesota. This specialization consists of four courses, introduction to software testing, black-box and white-box testing, introduction to automated analysis, and web and mobile testing with selenium. These software testing online courses are intended for beginners to intermediate-level software testers and developers who want to develop skills in software testing, practice, and master theory, techniques, and tools to use the software effectively. The course covers black-box testing techniques, white-box testing techniques, unit testing, static analysis, testing automation, writing test plans and defect reports, execution of tests, and understanding testing theory. This specialization course also includes a hands-on project to be completed successfully to earn certification. 

Link to the course: Software Testing and Automation Specialization 

5. In-Depth Software Testing Training Course From Scratch – Udemy

“In-Depth Software testing Training Course From Scratch” is a 26-hour software testing course provided by Udemy, intended for beginner to advanced-level students and professionals. The course contains eight sections and 17 lectures with comprehensive information on software testing. This course covers unique topics and skills and helps learners gradually make their way into the testing world. The course also offers a live end-to-end software testing project to give a practical learning experience. The chapters in this course are the introduction to software testing, test scenario, test cases and test plan writing, test execution, test strategy and defect management, JIRA & Bugzilla tools, and automation overview with QTP. There are no prerequisites for this course, anyone with basic computer knowledge can easily take this course and learn software testing. 

Link to the course: In-Depth Software testing Training Course From Scratch

6. Automated Software Testing: Unit Testing, Coverage Criteria, and Design for Testability – edX

“Automated Software Testing: Unit Testing, Coverage Criteria and Design for Testability” is an online software testing course on edX. It is a self-paced series course containing two courses, unit testing, and coverage criteria and design for testability that takes approximately five weeks to complete. The first course teaches types of testing, including specification-based testing, boundary testing, unit vs system testing, and test code quality. The second course covers test adequacy, code coverage, mock objects, and design for testability. Testers will learn how to test any software system using current state-of-art techniques, how to derive test cases dealing with an exceptional, corner, and bad-weather cases, how to develop testable architectures and write maintainable test code, and learn the limitations of current testing techniques. By the end of the course, software testing will never be the same again, and the tester will be able to choose the best testing strategies for different projects. Overall, the course provides a highly practical approach with various test programs using different techniques throughout lessons. The prerequisite for the course is to have an introductory knowledge of programming, especially Java. 

Link to the course: Automated Software Testing

Read more: NIST announces four post-quantum cryptography algorithms

7. Software Testing – NPTEL

“Software Testing” course on the National programme on technology enhanced learning (NPTEL), co-ordinated by IIT Kharagpur, is an elective online course in software testing. NPTEL is an initiative of seven Indian Institutes of Technology and the Indian Institute of Science, Bangalore, to provide quality education to everybody. The course duration is four weeks focusing on four major topics, one a week, including an introduction to software testing and test process, black-box testing, white-box testing and integration, regression, system testing, and test automation. The target learners are elective courses of UG and PG students and anyone interested in software development and testing. The prerequisite for this course is to have basic knowledge of programming. The course is free, but to get the certification, you need to qualify for an examination conducted by NPTEL that has some charges.

Link to the course: Software Testing

8. Business Analyst: Software Testing Processes & Techniques – Udemy

“Business Analyst: Software Testing Processes & Techniques” is a software testing course for business analysts who want to run software tests efficiently and accurately. Organizations are demanding more and more from business analysts, and software testing is only one aspect. This course provides training in software testing and teaches the repeatable fundamentals, testing processes, and techniques. The topics covered in this course are software testing basics, testing documentation, defect tracking, and eight steps to successful testing. This course follows the BA’s Guide technique of ‘TEACH, SHOW, DO,’ ensuring total comprehension of the topics at hand and retaining maximum information after the course. 

Link to the course: Business Analyst: Software Testing Processes & Techniques

9. Automated Testing: End to End – Pluralsight

“Automated Testing: End to End” is a practical software testing course that teaches how and what to test at the unit, integration, and functional UI levels of software testing and then brings them all together with the continuous integration build server. This course is available on Pluralsight and is 3.3 hours long, combining all sessions, making it the shortest course in this list among other software testing online courses. The information in this course is concise and comprehensive to deliver a simple understanding of automated testing. As automated testing can detect defects earlier than manual testing, soon automated testing will significantly streamline testing operations.  

Link to the course: Automated Testing: End to End

10. Monday Productivity Pointers – LinkedIn Learning

“Monday Productivity Pointers” is a beginner-level productive and technology management course on the LinkedIn learning platform. This extensive 11-hour and 45 minutes long course introduces tools and tips to use software and services more efficiently and powerfully. It is a weekly series of practicing productivity with the insulators Jess Stratton, Garrick Chow, and Nick Brazzi. This course provides skills like productivity improvement, productivity software, computer skills (Mac and Windows), social networking skills, and using Google platforms. Anybody can take this course, as the prerequisites are simple knowledge and experience in technology and software. Although it is a concise and informative course, they don’t provide certification because it’s an ongoing course.  

Link to the course: Monday Productivity Pointers

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What is Data Wrangling in Data Science?

what is data wrangling

Data processing is a high-priority task because of the exponential rise in data consumption. Data is analyzed by being manipulated to get insights and useful information according to one’s requirement, beginning with collecting or scraping data, conducting analysis, and producing dashboards. Retaining raw data into information and using it to perform further business predictions drives enterprises toward data-driven decisions. For this, the industry of data science and analytics is booming. Although data management and data modeling are crucial aspects of data analysis, data wrangling in data science has been the core emphasis since the beginning. Data wrangling is a collection of processes that transform raw data and can be labeled as the prerequisite to a successful data analysis. Let’s see what data wrangling is in data science, its importance, the steps for data wrangling, and the skills for data wrangling.  

What is Data Wrangling?

Data wrangling, also known as data munging, or data remediation, is a collection of processes, including cleaning, organizing, structuring, and enriching raw data to transform it into a readily usable format. The methods for data wrangling vary greatly depending on the dataset and the objective of the project. This is an important prior step to data analysis to provide data quality.

Though new technologies have empowered shortcuts to ease heavy workloads, it is not so much in data wrangling. For which, the whole implementation of data wrangling remains manual. The process being manual consumes a lot of time, and according to Forbes, data scientists and analysts spend 80% of their time on data wrangling. And for a fact, data wrangling is not the most enjoyable part of the role. The reason why data wrangling is time-consuming is the whole process is fluid, and the steps to begin and end are vague or not definite for all datasets. However, there are six steps to data wrangling that give a general idea of what one must look for data quality and data reliability. Also, data wrangling methods need to adapt to a particular dataset, which is iterative, making data wrangling a labor-intensive process. Overall, the data wrangling process depends on factors like the source of data, the quality of data, the data architecture of the firm, and the aim of data analysis.  

Importance of Data Wrangling

Data wrangling is necessary for the data science process as it delivers information through analysis. Any analysis eventually brings helpful insight into information or trends in a business, be its data analysis for modeling and prediction, building dashboards, or making reports. The process serves as an initial step to remove the risk of errors ensuring the data is reliable for further analysis. Alike laying a foundation initially goes long for a strong establishment, data wrangling enables the transformation of data into the desired format, which then produces valuable outputs. And if data wrangling is avoided, it may lead to significant downfalls, missed opportunities, and erroneous models, costing you time, money, resources, and the firm’s reputation. 

The primary tasks that data wrangling tools help with are –

  • Increasing data usability: Making raw data usable by transforming it into another format and securing data quality.
  • Ease of data collection: Gathering data from various sources into a single centralized location.
  • Clean data: Detection of noise or flaws and missing observations is simpler when sorting data of the preferred format. 
  • Business-oriented approach: Gathering raw data in one place and converting it to the required format eases the tasks of identifying the business’s best interest and improves the targeting of audience. 
  • Quick decision-making: As most errors and mistakes are eliminated already in data wrangling, further data processing is smoother to provide rapid data-driven decisions or models.
  • Visualization of data: Visualization is the key to understanding anything at first glance. Many data analysts and scientists prefer to include a visual representation in data wrangling and exploratory analysis, ensuring the best aspects of the data are reflected. Once the data is wrangled, export it to a visual analytics platform that will summarize, sort, and analyze the data.  

Read more: upGrad acquires Data Science Institute INSOFE

Six Steps in Data Wrangling 

Each step in data wrangling offers to manipulate data to understand better and extract information hidden in the data.

1. Discovery

The first step of data wrangling is to discover. As simple as it sounds, discovering data means getting to know the data and conceptualizing how you can use it. Manually getting familiar with the dataset is crucial in ultimately catching patterns and pushing the limits of what one can do with it. In discovery, the easiest errors to find is missing or incomplete values, and plan to structure the data in an organized manner.   

2. Structuring

As the collection of data may come from more than one source, it may contain numerous data formats and sizes. The data required to be restructured and organized to make it more manageable for the analytical model. This step includes general standardization like string for names, integers for salary, date format for date, and so on.  

3. Cleaning

Data cleaning consists of tasks dealing with errors, including duplicate entries, invalid values, and null values. Many people think data wrangling and data cleaning are the same, but that is not true. Data cleaning is included in the step of gaining wrangled data. It includes tasks like making corrections, removing errors, handling outliers, eliminating unnecessary data points, etc. Data cleaning can be performed swiftly with programming languages like Python, R, and SQL. 

4. Enriching

This step determines if the data need to consider external data for better performance. This especially helps data miners address certain labels not included in the dataset beforehand but prove to bring out relevant information in the dataset. Here, the goal is to fill the gaps in the data (if any) to derive meaningful information and, in the end improve the analysis. Enriching is optional in data wrangling but holds great significance if the current data cannot provide better insights. 

5. Validating

As data is continuously manipulated and edited in data wrangling, this step checks the quality of the wrangled data. The process is to verify whether or not the data has quality, consistency, accuracy, security, and authenticity. The validation is extensively thorough, using some automated techniques in programming. And if the data doesn’t fit the requirement, the issues are resolved using different techniques, and the whole process is iterative until you reach the desired or best possible outcome.   

6. Publishing

Publishing is the final step in data wrangling, where the wrangled data output is ready for analytics. The data is to be published in an easily accessible location for the team to work on it, such as a new data architecture or database server. The output dataset here is a standardized version of itself, without the errors, sorted and categorized. 

Read more: Data Science and Machine Learning jobs are rising in 2022 says LinkedIn

Data Wrangling Skills

Good data wrangling skills are one of the most essential skills of a data scientist and analyst. Knowing the dataset entirely allows you to enrich the data by integrating information from multiple sources and solving common transformation problems and data quality issues. To promote data wrangling skills for the job, companies prefer to train interns and freshers with skills like data annotations, web scraping, and data transformation, including merging, ordering, aggregation, and so on. This training helps to induce the mindset to find and fix errors by knowing where the errors could come. Though the source of errors is vague, the idea is to eliminate errors by seeing through the raw data. 

The tools used for data wrangling include programming languages, software, and open-source data analytics platform. Some tools are MS Excel Power Query, Python and R, Alteryx APA, and more. Some visual data wrangling tools like OpenRefine, Trifacta, and Tableau are also designed for beginners and non-programmers. Each tool has its specifications, such Trifacta features cloud integration, standardization, and easy flow, MS Excel features broad connectivity with data sources and combines tables, Tableau features visual appeal, high security and real-time sharing, and so on. There is no best or all-rounder tool for data wrangling yet in the market, as the use depends on the requirement and goal for analysis using a dataset. 

As data wrangling consumes a lot of time, new automated solutions are developed that use machine learning algorithms. Yet the development of automated solutions for data wrangling is tough as the process requires intelligence and not only a repeated process of work. These automated tools aim to validate data mapping and inspect data samples thoroughly at each step of transformation. There are few automated software available today using end-to-end machine learning pipelines, performing the three domains of automation in data wrangling, cleaning, structural curation, and data labeling. 

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Classification Algorithms in Machine Learning

classification algorithms in machine learning

Machine learning has given an idea of innovation and power to young minds. With the advancements in computer technology, it has become the stepping stone into the future. Machine learning provides various algorithms for solving different problems, one of which is classification. Classification recognizes, understands, and groups ideas and objects into categories or classes. Now, let’s see some types of classification algorithms in machine learning. 

1. Logistic Regression

Logistic regression is a supervised machine learning technique used for classification problems. Here, we predict the categorical dependent variable using the given independent variables. The predicted outcome is binary: yes or no, 0 or 1, etc. The working of logistic regression is that the relationship between the dependent and independent variables is statistically analyzed with the sigmoid (aka logistic) function to carry out prediction, which is close to linear regression where a regression line is fitted to data. It is on top of the types of classification algorithms in machine learning because of its working principle, which is as simpler as linear regression. The sigmoid function is a mathematical function that is an S-shaped curve used to convert values into probabilities. 

Mathematically, we define the probabilities of outcomes and events by measuring the impact of multiple variables in the given data. Logistic regression plays an important role in machine learning because of its ability to provide probabilities and classification of new data using historic continuous or discrete data. There are two assumptions for logistic regression:

  • The nature of the dependent variable must be categorical
  • No multi-collinearity in independent variables

           The equation for logistic regression is,

Logistic Regression in Machine Learning
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where, log[y / 1-y] is the logarithm of the likelihood of the dependent variable

           b0 is the y-intercept

           x1, x2, x3… are the independent variables

           b1, b2, b3… are the slope coefficients

Based on the number of outcome categories, logistic regression are of three types.

  • Binomial – There are only two possible outcomes, 0 or 1, yes or no, etc.
  • Multinomial – When there are three or more unordered possible outcomes. For example, categories of water bodies, ‘sea’, ‘lake’, or ‘river’. 
  • Ordinal – When there can be three or more ordered possible outcomes, such as ‘good’, ‘better’, or ‘best’.

2. K-nearest Neighbor 

K-nearest neighbor or KNN is a non-parametric supervised learning classifier. It is one of the simplest classification techniques in machine learning used for both regression and classification problems. This algorithm works on the neighbors-based classification, a type of lazy learning as it does not learn from the training data immediately but stores it for the execution stage. KNN identifies an object or new data by finding the similarity between new data and the stored training set. It puts the new data into the category it finds the most similar to the available data. The classification computation is done by the majority of votes of the k-nearest neighbors of each data point. Mathematically, the Euclidean distance between the new data point and training data points is calculated. Then the new data point is assigned to the category or class which has the highest number of k-neighbors to the new data point.  

credit

What decides the ‘k’ in KNN?

‘K’ indicates the number of neighbors a data point has. It is considered a hyperparameter in KNN, which has to be decided beforehand to get the most suitable fit for the data set. When k is small, it gives the most adjustable fit to the data but will have low bias and high variance. Meanwhile, when k has a higher value, it is more flexible to outliners and has a lower variance but high bias. There is no right way to find the best value of ‘k,’ it depends on the dataset, but the most preferred value is 5.

Read more: Indian student’s machine learning software to be sent to space 

3. Decision Tree

Decision tree is a supervised learning algorithm that uses a tree representation to solve the problem by producing a sequence of rules that can classify the data. This algorithm can be visualized among the other types of classification algorithms in machine learning, making it simple to understand. A decision tree requires little data preparation and can handle both numerical & categorical data. The algorithm is a flowchart tree-like structure in which each leaf node corresponds to a class label and represents attributes on the internal nodes. Decision trees can be defined as a graphical representation for getting all possible solutions to a problem based on the given conditions. 

Here is the basic decision tree structure.

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A decision tree works to predict the class of a given dataset. It starts from the root node and goes to leaf nodes. The algorithm works as a classification model by comparing the values of root attributes and record (dataset) attributes based on this, we follow the branch and jump on the next node. This process of comparison continues until you reach the leaf node. To find the best attribute in the dataset attribute selection measure (ASM) is used. ASM is a technique of selecting the best attribute in the given dataset performed either by information gain or Gini index. 

The information gain is the change in entropy after segmentation of a dataset, or it is the measure of how much information an attribute provides about a class. The objective of the decision tree is to maximize the information gain, and the node with the highest information gain is split first. 

The formula calculates information gain, 

Information Gain= Entropy(S)- [(Weighted Avg) *Entropy(each feature)

And the formula for entropy is,

Entropy(s)= -P(yes)log2 P(yes)- P(no) log2 P(no)

where, S= total number of samples, P(yes)= probability of yes, and P(no)= probability of no

Gini Index is a metric measuring impurity or purity of an element. It calculates the amount of probability of a specific attribute classified incorrectly when selected at random. The decision tree prefers a low Gini index attribute to create binary splits. The formula for calculating the Gini index is,

Gini Index= 1- ∑jPj2

where Pj is the probability of an element being classified for ‘j’ distinct class.

4. Support Vector Machine

Support vector machine (SVM) is one of the most widely used supervised learning classification methods because of its significant accuracy with less computation power. The objective of SVM is to fit a hyperplane to the data points in an N-dimensional space that distinctly classifies data points and helps to categorize a new data point. Hyperplanes are decision boundaries that can segregate the N-dimension space into classes. The dimension of the hyperplane depends on the number of features present in the given dataset. 

SVM chooses extreme points or support vectors that help create the hyperplane, thus, the algorithm’s name. Support vectors are defined as the data points closer to the hyperplane, which influence the position and orientation of the hyperplane. With these support vectors, SVM tries to maximize the margin of the classifier. The shortest distance between the observed data points and the threshold is called the margin, and the threshold is the largest distance between the two classes. See the diagram below for a better understanding of the hyperplane.

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There can be two types of SVM:

  • Linear SVM – When the dataset is linearly separable, that is, if the data is classified into two classes by a single straight line. Then, the classifier is called a linear SVM.
  • Non-linear SVM – When the dataset is non-linearly separable, the data can not be classified using a straight line. Then, the classifier is called a non-linear SVM. 

Read more: How Cropin is transforming the agroecosystem with machine learning?

5. Naive Bayes

Naive Bayes is a probabilistic supervised learning algorithm based on the Bayes theorem used to solve types of classification problems. It makes predictions based on the probability of an object. Naive Bayes classifier is widely used in text classification, spam filtration, and sentiment analysis. It is one of the most simple but fast, accurate, and reliable algorithms in machine learning. Now, Bayes’ theorem or Bayes’ law is the basis of the algorithm, which is used to calculate the probability of a hypothesis with prior knowledge and works on conditional probability. Conditional probability is a measure of the probability of an event occurring, given that another event has occurred. 

The formula for Bayes’ theorem is,

Naïve Bayes Classifier Algorithm
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Where,

P(A|B) = Posterior probability, i.e the probability of hypothesis A on the observed event B.

P(B|A) = Likelihood probability, i.e the probability of the evidence given that the probability of a hypothesis is true.

P(A) = Prior Probability, the probability of hypothesis before observing the evidence.

P(B) = Marginal Probability, the probability of evidence.

The fundamental assumption of the Naive Bayes classification model is that each feature makes an independent and equal contribution to the outcome. To be noted, the assumption is not generally found in real-world situations. In fact, the independence assumption is never correct but often works well in practice. 

There are three types of Naive Bayes classification models,

  • Gaussian – If predictors take continuous values instead of discrete, the dataset’s features follow the normal distribution. Then, the Naive Bayes model is called a gaussian model.
  • Multinomial – When the data is multinomially distributed, the classifiers use the frequency of words for the predicators to assign a category called the multinomial model.
  • Bernoulli – Similar to the multinomial model, the predictor values are independent boolean variables.

Machine learning provides various classification techniques, and we have discussed the five most simple and basic types of classification algorithms. The above-stated algorithms are easy and straightforward for implementation yet give good accuracy. These algorithms use mathematically & statistically proven methods & laws to perform analytical tasks. 

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IBM announces expansion of its embeddable AI software portfolio

IBM expansion of embeddable AI software portfolio

IBM has announced an expansion of its embeddable AI software portfolio with the launch of three new libraries created to help IBM Ecosystem partners, developers, and clients more easily, quickly, and cost-effectively build their AI-powered solutions and bring them to market. 

Now generally available, the AI libraries were designed in IBM Research and developed to provide Independent Software Vendors (ISVs) throughout industries an easily scalable method to build natural language processing, speech-to-text, and text-to-speech capabilities into applications across any hybrid, multi-cloud environment.

The expanded portfolio enables access to the AI libraries that power famous IBM Watson products. It is designed to assist lower the barrier for AI adoption by helping clients and partners address the skills shortage and development costs needed to build machine learning models from scratch. IT and developer teams also have the liberty to embed the new Watson libraries they prefer into their applications to help create customized products without data science expertise.

Read More: IBM Report Says Cost Of Data Breaches Averaged ₹17.6 Crore In 2022

With the three new software libraries, developers can access AI capabilities. They can also choose the specific functionality, e.g., natural language processing, that they want to embed in various parts of an application. The libraries include innovations by IBM Research as well as open-source technology and are designed to decrease the time and resources taken by a developer to add powerful AI to an application.

The release is built on the existing portfolio of embeddable AI products of IBM, which includes industry-leading products such as IBM Watson Discovery, IBM Maximo Visual Inspection, IBM Instana Observability, IBM Watson APIs, and IBM Watson Assistant. With IBM’s embeddable AI portfolio, CXOs and other IT decision-makers can utilize AI to assess business insights and build enhanced end-user experiences.

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Capgemini Enters a Share Purchase Agreement to Acquire Quantmetry

capgemini enters agreement to acquire quantmetry

Capgemini plans to acquire Quantmetry as it enters a share purchase agreement with the company and enhances its data transformation capabilities in France. As per the agreement, Quantmetry will aid Capgemini in embracing intelligent industries and businesses with technological transformations. 

Quantmetry is an independent AI consulting firm specializing in mathematical modeling and developing technological solutions. Within a decade of being founded in 2011 in Paris, the company has built a global reputation in the retail, consumer goods, energy, and manufacturing sectors.

The acquisition of Quantmetry aims to strengthen Capgemini Invent’s value realization, digital transformation, and capacity enhancement in France. Capgemini Invent is the group’s digital innovation and transformation segment that focuses on curating technology-driven consumer experiences.

Read More: Meta AI Releases EnCodec, a Neural Network to Reconstruct Input Audio Signals

Capgemini is looking forward to becoming an industry leader in data and AI consulting with expertise from Quantmetry.

Quantetry’s CEO and founder, Jeremy Harroch, said on the agreement, “Our consultants, engineers and researchers will be able to put our R&D and machine learning expertise at the center of an ecosystem of excellence.”

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Top Data Analyst Interview Questions

data analytics interview questions

With the generation of an enormous amount of data regularly, analyzing and interpreting data is the need of the hour. Data science and data analytics fields are blooming and are expected to have exploding employment in the next coming years. According to Forbes, after reading the report ‘Gartner Top 10 Data and Analytics Trends for 2020‘, they suggest paying attention to three main trends in the industry: becoming a data analyst or scientist, automated decision making using AI, and data marketplaces and exchanges. The employment growth in data analytics results from companies’ demand and high-paying job profiles. Although there is tough competition around the job title, many opt to become data analysts for the thrill of data-driven processes and enthusiasm for data.

Critical requirements for Data Analysts

The minimum education qualification for data analysts is graduation or post-graduation in science with at least mathematics or statistics as a subject. It is a plus to have programming and business or finance knowledge. The basic skills required for the job include knowledge of programming, familiarity with data analysis and data visualization tools, and an understanding of statistics and machine learning algorithms. 

Responsibilities of Data Analysts

Data analysts seek insight into data for making data-driven decisions in the company. The key responsibilities are:

  • Provide reports on the analysis of data using statistical methods.
  • Identifying, analyzing, and interpreting data patterns and trends in datasets. 
  • Collecting, processing, and maintaining datasets and data systems. 
  • Working side-by-side with the management sector to prioritize business needs.
  • Designing new processes for improving data consumption and extraction. 

The data analytics interview questions can vary from company to company as the job profile of data analysts varies greatly. Although there is a specific need for data analyst jobs, the general subjects to keep in mind for a data analytics interview questions are programming in Python or R and SQL, statistics, machine learning, and tools like Excel, Power BI, and Tableau. Here is a list of data analyst interview questions organized according to the career levels of a data analyst. The list consists of data analyst interview questions and answers for preparation.

Read more: Popular Machine Learning papers on Papers with Code

List of Data Analyst Interview Questions

Beginner level

1. What are the characteristics of a good data model?

A good data model has four characteristics:

  • Easy consumption of data: The data in a good data model should be clean, transparent, comprehendible, and reflect insights into the data.
  • Scaling of data: A good data model should be capable of scaling in proportions when a change occurs in data. 
  • Predictable performance: A good data model should have room for performance improvements to get an accurate and precise estimate of the outcomes.
  • Adaptive and responsive: As growing businesses demand changes from time to time, a good data model should be adaptable and responsive to integrate the changes in the model and data.

2. Define overfitting and underfitting.

Overfitting and underfitting are modeling errors for which models fail to make accurate predictions. In overfitting, the model is fitted too well to the training data, as a result, the model produces accurate output on training data but is not able to make accurate predictions for new test data. On the contrary, in underfitting, the model is poorly fitted to the training data and is not able to capture enough trends or underlying patterns in the dataset to make predictions.

3. What is data cleansing?

Data cleansing or cleaning, or wrangling, is a process of identifying and modifying incorrect, incomplete, inaccurate or missing data. This process is important to ensure the data handled is correct and usable and that it won’t provide any further errors. There are five primary issues under data cleansing: dealing with missing data, duplicate data, structural errors, outliers, and multi-sourced data. Also, each issue can be solved with a different method, like deleting or updating missing data and fixing structural errors by thoroughly analyzing the dataset, and so on.  

4. Define data visualization and its types.

Data visualization is the process of representing data graphically to reflect the important information it contains. With visualization, the understanding and analysis of data are easier and more efficient. Many types of data visualization techniques include diagrams, graphs, charts, and dashboards. 

5. Differentiate between variance and covariance.

The statistical definition of variance is the deviation or spread of data set from its mean value, and covariance is the measure of how two random variables are related in a dataset. The main difference between variance and covariance is variance talks about the overall dataset, including all data points, and covariance focuses on two randomly chosen variables in the dataset.

6. Which Python libraries are used for data analytics?

The primary Python libraries used for data analytics are Pandas, NumPy, Matplotlib, and Seaborn. Pandas and NumPy are used for mathematical or statistical computations in the data frame, including describing, summarizing, computing means and standard deviations, updating/deleting rows and columns, and so on. And, Matplotlib and Seaborn are used for data visualization, including commands for graphs and plots, representing the correlation between variables in the data frame, and more. 

Read more: Top data analytics books

Intermediate level

7. What is an outlier and how are they detected?

An outlier is a data point or value in the dataset that is far away from other recorded data points. It can indicate either variability in measurement or an experimental error. There are many ways to detect outliers, including the box plot method, the Z-score method, and so on.

8. What are the data validation techniques used in data analytics?

Data validation is the process of verifying the dataset through data cleaning and ensuring data quality. There are four main data validation techniques:

  • Field level validation: Data validation starts as the data enters the field, and errors can be fixed under ongoing processing of model building.
  • Form level validation: It is a user-based validation performed while collecting the data. The errors are highlighted as users submit the data and get it fixed.
  • Data saving validation: This validation technique is used when a file or database is saved entirely, and multiple data forms are validated at once.
  • Search criteria validation: The validation method is used when searching or querying the data. Validation at this stage provides users with accurate and relevant results. 

9. Differentiate between the WHERE clause and HAVING clause in SQL.

The WHERE clause operates on row data, and the filter occurs before any groupings are made. In contrast, the HAVING clause operates on aggregated data and filters values from a group.

The syntax of the WHERE clause is:

SELECT column_name(s)

FROM table_name

WHERE condition

The syntax of the HAVING clause is:

SELECT column_name(s)

FROM table_name

WHERE condition

GROUP_BY column_name(s)

HAVING condition

ORDER BY column_name(s)

10. Define a Pivot table in Excel.

A Pivot table in Excel is a way of summarizing large amounts of data. It brings together information from various locations in a workbook and presents it on a table. It is helpful to present data findings and analyze numerical data in detail, which helps query large amounts of data.

Experienced level

11. What is time series analysis and time series forecasting?

Time series analysis is the technique to learn new information from time series data by analyzing them using different statistical methods. Four primary variations are seen in time series analysis: seasonal, trend, cyclical, and random. Time series forecasting can be considered to be based on time series analysis, but in forecasting, the focus is on building a model for predicting future values from previously stored data. 

12. Define collaborative filtering. 

Collaborative filtering is a popular technique used in recommender systems where models provide automatic predictions or filter users’ interests based on past choices. The three major components of collaborative filtering are users, items, and interests. This method is based on user behavioral data, assuming that people who agree on particular items will likely agree again in the future.  

13. What is Hypothesis testing, and name a few forms of hypothesis tests?

Hypothesis testing is a statistical technique to determine the significance of a finding or statement. Two mutually exclusive statements are considered on a population or sample dataset, and this method decides which statement best reflects or is relevant to the sample dataset. There are many forms of hypothesis tests, including p-test, t-test, chi-square test, ANOVA test, and more. These tests have different criteria for considering a statement to be more relevant to the sample data like t-tests computes the difference between the means of a pair of groups, ANOVA compares more than two pair of groups, and so on. 

14. Explain clustering and name properties and types of clustering. 

Clustering is the process of classifying data points into clusters or groups using a clustering algorithm. It helps to identify similarities or similar properties between data points, which can be hierarchical or flat, hard or soft, iterative and disjunctive. The types of clustering are based on the similarities in data points and have four basic types: centroid-based clustering, density-based clustering, distribution-based clustering, and hierarchical clustering. 

15. State the benefits of using version control.

Version control or source control is a mechanism to configure the software so that the changes to the software code can be tracked and managed. There are five benefits of using version control:

  • The process of software development becomes clear and transparent.
  • It helps to distinguish between different document versions so that the latest version can be used.
  • With version control, the storing and maintenance of multiple variants of code files is easy.
  • Analysis of changes to a dataset or code file can be reviewed quickly. 
  • It provides security and can help revive the project in case of failure of the central server. 

As mentioned earlier, the interview questions on data analytics may vary according to the company’s needs. There can be more in-depth questions on Python libraries, Excel, SQL querying, and data visualization tools. This list is an overview of data analyst interview questions that a candidate must know. Prepare for the data analytics interview as per your interests and goal. All the very best!

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Norway establishes metaverse tax office to embrace Web3

Norway establishes metaverse tax office

The Norwegian government has taken steps to embrace Web3 by establishing a metaverse tax office.

Norway’s central register, The Brønnøysund, and the nation’s tax authority, Skatteetaten, announced that they’re collaborating with consulting firm Ernst and Young (EY) to open an office in Decentraland. The announcement came at the Nokios conference on Wednesday. According to Nokios, the initiative aims to deliver services to tech-native individuals while establishing their Web3 footprint.

Magnus Jones, Nordic blockchain lead at EY, said that he hopes this partnership will spearhead education in the crypto space by educating users about taxes related to non-fungible tokens (NFT) and decentralized finance (DeFi). 

Read More: Google Acquires AI Avatar Startup Alter To Boost Its Content Game

The Brønnøysund is also exploring several other Web3 services, such as wallets, smart contracts, decentralized autonomous organizations (DAO), and many more.

Apart from the metaverse, Norway has been slowly integrating crypto services nationally. In June, the government suggested using the Ethereum scaling service Arbitrum to release capitalization tables platforms for unlisted companies. In September, Norway, Israel, and Sweden joined hands with the Bank for International Settlements to assess the possibility of introducing a central bank digital currency (CBDC) for cross-border payments.

As the Scandinavian nation delves deeper into crypto, other countries are also integrating Web3 tools nationally. In July, a policy briefing by the Shanghai city government said it plans to bolster its metaverse industry to $52 billion by 2025. And earlier this month, Japan’s prime minister said the country would incorporate the metaverse and NFTs in its plans for digital transformation.

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Types of Software Testing

types of software testing

While many industries were affected by the pandemic and suffered losses, the software applications and information technology industry was at its peak and kept growing. Today, the trends like artificial intelligence, machine learning, the internet of things, cloud computing, and many others swamped the software development market and created a huge impact globally. In the production of software applications, also known as the software development life cycle (SDLC), software testing is one of the most important steps. Software testing is a diverse task to find defects or errors in software. It is a process of examining a software’s performance, behavior, and value under test by validation and verification. 

There are various types of software testing, and each type has its own features, advantages, and disadvantages. And based on the requirement, a tester selects the type of software testing to be used. The types of software testing are a long list and have more than 20 types of testing. To make it simpler, the types of software testing can be divided into two parts, manual and automation testing. The manual testing takes the box approach of testing, including white, black, and grey box testing, and further, the black box includes functional and non-functional testing. 

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The Box Approach of Software Testing

There are various software testing methods, and traditionally the in box approach is divided into three types, white-box, black-box, and grey-box testing. The white-box and black-box testing take the approach of describing a tester’s point of view when designing the test cases. And grey-box testing is a hybrid approach that develops tests from specific design elements.  

White-box Testing

White-box testing is inspecting every line of code before the tests even start. It verifies the internal structures or working of a program as opposed to the functionality and is also known as clear box testing, glass box testing, transparent box testing, or structural testing. The source code and programming skills are used in white-box testing to design test cases. These test cases involve verifying the product’s underlying structure, architecture, and code to validate input-output flow. Generally, white-box testing is applied at the unit level but can also be applied to integration and system levels of software testing types.

Read more: China develops new Quantum Computing Programming Software isQ-Core

Black-box Testing 

Black-box testing (aka functional testing) is a manual testing technique where testers in software engineering analyze the requirements of the software, look for defects or bugs, and decide to send it back to the development level for rectification. In this approach, the software is treated as a black box, and the examination is done without any knowledge of the source code. The testing includes methods like equivalence partitioning, boundary value analysis, decision table testing, fuzz testing, and use case testing. Black-box testing is categorized into functional and non-functional testing, which is then divided into different types of software testing that are discussed below. Additionally, black-box testing can be applied to all levels of software testing, including unit, integration, system, and acceptance.    

Grey-box Testing 

Grey-box testing is a type of testing in software engineering that tests the software or application with partial knowledge of the internal structure of the software. The testing uses reverse engineering to determine the errors. The goal of grey-box testing is to find and identify defects resulting from improper code structure and irregular use of the software. Grey-box testing determines intelligent test scenarios from the provided software’s limited information that are applied to data type handling, exception handling, and more. 

List of Types of Software Testing

Here the list consists of the main levels of software testing after functional and non-functional software testing. The first four types, unit, integration, system, and acceptance testing, come under functional testing. And the rest types, security, performance, usability, and compatibility, are under non-functional testing.

1. Unit Testing

Unit testing is the first level of functional testing in software testing, performed on an individual unit or component to test for correction. It is called unit testing because the tester examines the software module independently or tests all the module functionality. Testers often use test automation tools like NUnit, Xunit, and JUnit to execute unit testing, and each unit is viewed as a method, function, procedure, or object. The objective is to validate the performance of unit components. Unit testing is a curial part of types of testing in SDLC as most defects can be identified at the unit test level.  

2. Integration Testing

Among software testing types, interaction testing is where two or more modules of an application or software are logically grouped and tested altogether. This testing is the second level of functional testing that focuses on the defect of an interface, communication, and data flow between modules. The objective of the testing is to test the statement’s accuracy between each module. Integrating testing is further divided into two parts, incremental and non-incremental, comprising four different types of software testing: top-down, bottom-up, sandwich, and big-bang testing. Top-down and bottom-up incremental integration testing works by adding the modules incrementally at each step and then test the data flow between the modules. And the difference between the two is that the modules added in the top-down must be the child of the earlier module, and in the bottom-down, the module must be the parent of the earlier one. When the data flow gets complex and difficult to classify a module as parent and child, non-incremental integration is applied.    

Read more: Microsoft Open-Sources Counterfit, A Tool To Automate Security Testing In Machine Learning Models

3. System Testing

System testing is the third level of functional testing, also known as end-to-end testing, in which the test cases are operated while the test environment and production environment are parallel. In system testing, each attribute of the software goes under test for the working of end features based on the business requirements, and then the software product is analyzed as a complete system. Various software testing types are performed under system testing, including end-to-end, smoke, sanity, monkey, and so on.  

4. Acceptance Testing

Acceptance testing (aka user acceptance testing) is a type of testing in software engineering where the client or business tests the software with real-time business scenarios. This is the fourth and final level of functional testing, after clearing the test, the software goes into production. The testing is a quality assurance process determining the degree of success of meeting the clients’ requirements and getting thier approval. Several methods can be used in acceptance testing, such as Alpha testing, Beta testing, and operational acceptance testing (OAT).  

5. Security Testing

Security testing is a non-functional type of software testing intended to reveal defects in the security mechanism of an information system protecting the data and maintaining functionality. The testing includes checking how the software or application is secure from internal and external threats and how much software is secure from malicious programs and viruses. The tests check to provide maximum security if any cyber attack happens, going on with how software behaves under a hacker attack and how secure and strong is the authorization. A few security testing methods are penetration testing, vulnerability scanning, and risk assessment.

6. Performance Testing

Performance testing tests a software’s stability and response time by applying load in the software. Testers focus on four things in performance testing: response time, load, scalability, and stability of the software. The goal of performance testing is to identify, rectify, and eliminate the performance bottlenecks in software. The testing consists of different types of testing, including load testing, stress testing, scalability testing, stability testing, volume testing, endurance testing, and spike testing, and each test serves one or more focus points. Performance testing is done with tools like Loader.IO, JMeter, LoadRunner, etc.  

7. Usability Testing

Usability testing is another non-functional type of testing in software engineering which works from the users’ point of view to check the user-friendliness of the software application. A user-friendly software has two aspects: the application must be easily understood and look appealing and feel good for working on it. The purpose of usability testing is for the application to look appealing and showcase information at a glance. Some of the testing methods in usability testing are exploratory testing, cross-browser testing, and accessibility testing. Additionally, four questions help guide the usability testing process: screening questions, pre-test questions, in-test questions, and post-test questions.   

8. Compatibility Testing

Compatibility testing is a non-functional type of testing that validates how software application behaves and runs in various environments, web servers, hardware, and network environments. The goal of the testing is to ensure that software is capable of working on different configurations, databases, browsers, and their versions. There are two types of compatibility testing, backward or downward and forward compatibility testing.  

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