Over the past two decades, chronic diseases have significantly increased among the population. The most prevalent conditions are cancer, heart diseases, and diabetes, among several others. Researchers are advancing the development of treatment options as these illnesses continue to proliferate. The medical industry is turning to the new technological advances in artificial intelligence (AI) to speed up the prevention, diagnosis, and treatment of several diseases.
Today, artificial intelligence in chronic disease management is bringing radical changes in treating almost all sorts of bodily ailments. Below are some areas in which the technological advances in AI are ramping up the healthcare scenario.
Heart Diseases
At several medical institutions and organizations, researchers are taking steps to advance cardiovascular healthcare using AI. Hospitals and research centers are using AI-programmed computers to process data quickly and accurately to provide better treatment outcomes. AI programs can perform tasks including detecting and preventing heart disease, and improving diagnostic radiology capabilities.
At Johns Hopkins University, researchers explored the use of whole-heart computational models to understand better ventricular arrhythmias, which can lead to personalized medical treatments for cardiovascular diseases. Researchers say that the patient-specific models can use predictive analytics to ascertain the outcomes of a cardiac procedure or the risk of sudden cardiac death.
Artificial intelligence has made early detection, prevention, and treatment relatively more straightforward for cancer. Researchers at Tulane University have discovered that AI can accurately diagnose colorectal cancer by analyzing tissue scans much better than pathologists. AI has consistently emerged as a boon for predicting or detecting cancers related to the bladder, breasts, and lungs.
Artificial intelligence can also assist in determining the optimum course of treatment for patients. For patients with cancer, researchers are using predictive analytics to know how an individual will respond to a particular medication. AI and machine learning can also prevent unnecessary side effects from cancer treatments that may not work for some patients.
Diabetes
Over the past several years, AI has been used by researchers to investigate methods for diabetes management. Different strategies for diabetic treatments include remote patient monitoring, self-management, and support from wearable AI devices.
Individuals can track their blood sugar levels with continuous glucose monitors, which offer blood sugar estimates every five minutes. Models based on data analysis can predict the impact of meals and insulin on glucose levels, thus allowing patients to control blood sugar levels better. AI-backed mobile health tools have reduced the need for unnecessary patient-provider interaction and in-person appointments. Artificial intelligence also plays a vital role in diabetes prevention by identifying high-risk patients.
Parkinson’s and Alzheimer’s disease
AI has also revolutionized the research, prevention, and treatment of Alzheimer’s and Parkinson’s diseases. Recently, the Michael J. Fox Foundation and the research arm of IBM developed an artificial intelligence model that can group typical symptom patterns of Parkinson’s disease. The AI model can accurately identify the progression of these symptoms in a patient, regardless of whether they are taking medications to mask those symptoms or not.
With the latest artificial intelligence technology, risk prediction models, and data analytics, practitioners can promptly identify warning signs of illnesses, allowing for a quick treatment turnaround and reduced healthcare costs. As researchers learn more about artificial intelligence capabilities, technology is becoming an effective tool for chronic disease prevention and management. With the rate at which new AI technologies are intervening in the medical industry today, healthcare is becoming more reliable and accessible for humankind.
A researcher from Sweden named Almira Osmanovic Thunströmgave claims that the language model called GPT-3 has written an academic thesis about itself after she asked the model to do so. The researcher said that the thesis had a “fairly good” research introduction and even had scientific references and citations included in the text.
Thunström, a researcher at Gothenburg University, sought to publish the research paper in a peer-reviewed academic journal. After GPT-3 completed its scientific paper in just 2 hours, Thunström had to ask the model for its consent to publish the paper, to which it replied positively.
The model replied’ no’ when asked if it had any conflicts of interest. Thunström said that the authors began to treat GPT-3 as a sentient being, even though it was not.
Thunström wrote about the experiment in Scientific American, emphasizing the fact that the process of getting GPT-3 published sparked many ethical and legal questions.
Recently, the sentience of AI became a significant topic of conversation after Google engineer Blake Lemoine claimed that AI technology called LaMBDA had become sentient and even asked to hire an attorney for itself. However, experts say that technology has not yet reached the level of creating machinery precisely resembling humans.
Thunström said that the experiment had seen positive results among the AI community. She added that the other scientists trying to replicate the results of the experiment are finding that GPT-3 can write about all kinds of subjects.
Data is the crude oil of the technology-driven world, as a result, data analytics and data science have become the new buzzwords. According to IT chronicles, around 2 Quintillion bytes of data are being generated daily across all industries. Traditional data processing systems can not manage a large amount of data. However, handling big data can be solved with data science and data analytics. These terms sound similar, and people think these terms are interchangeable, creating confusion.
This article will cater to the data science vs. data analytics discussion and bring out various factors that will make it easy to detect which field is more suitable for you.
What is Data Science?
Data science is a study that deals with big data to design and build various processes for data modeling, analysis, and prediction workflows. It uses machine learning algorithms to create, train, and build predictive models that help analyze data. A report in 2020 by MicroStrategy stated that about 94% of businesses agree that data is essential for business growth. However, 63% of companies cannot gather insights, which is how data science has found its place in many industry domains, such as marketing, sales, healthcare, finance, and more.
analytixlabs
The data science lifecycle revolves around five stages — obtaining, scrubbing, exploring, modeling, and interpreting data. Obtaining data involves gathering raw unstructured data from different sources through various methods like data extraction and data entry. After collecting is the scrubbing or cleaning procedure, where the transformation of raw data into clean and structured information. Once the data is clean, data scientists process and explore it to determine its purpose. Data undergoes text mining, prediction, and qualitative analysis. The final step in the data science lifecycle is interpretation, which involves data reporting via BI (business intelligence) and data visualization.
The prerequisites for data science are machine learning and statistics. Machine learning is the spine of data science, as it is vital for quality predictions and computations. You can train and automate models to make more intelligent decisions to save time and effort through machine learning. To master machine learning, knowing statistics is essential and is an integral part of data science. With mathematical statistics, you can interpret quantitative data and better understand the correlations between different attributes of algorithms. To implement machine learning and statistics, a data scientist should know programming languages like Python or R and understand database management concepts like extracting or filtering data using basic SQL commands.
What is Data Analytics?
If data science is a house, then data analytics would be a room in that house. Data analytics is the process of exploring existing structured raw data with a specific goal in mind. It finds hidden patterns, new trends, and correlations to derive essential insights. Data analytics has many uses, such as decision-making, customer experience, operations, and marketing strategies.
voksedigital
The main steps in data analytics are understanding the problem, data cleaning, data enhancement, data exploration, and visualization of the result. Understanding the issue is the first and most crucial step as it defines the goals and areas where work is required. Once you identify the problem, data relevant to it needs to be collected and filtered. The disordered raw data needs cleaning and should be void of redundant, missing, and unwanted values. After gathering the processed data, exploration and analysis using business intelligence tools and data visualization take place to understand the data and figure areas to improve.
For data analytics, the prerequisites are Microsoft Excel, SQL, presentation skills, and machine learning. A basic understanding of Excel is fundamental as data analytics majorly involves many spreadsheets, but Excel is not efficient for more extensive datasets. SQL or Structured Query Language is essential for a data analyst as one can easily manage massive datasets. Knowledge of machine learning and software languages like R or Python is also beneficial when dealing with big data. After getting the desired results from analytics, demonstrating the idea in a simple but engaging way is crucial. Good presentation, data visualization, and critical thinking skills are required to share your perspective with the target audience.
The terms data science and data analytics sound like synonyms to most people, but that is not the case. Data science focuses on understanding the purpose of the dataset and forming the questions the dataset can solve. In contrast, data analytics is a constituent of data science that uses the learning from processed data to answer questions and make decisions. Let us understand the main difference between data science and data analytics:
Data Science vs. Data Analytics: Fundamentals Objective
Data science mainly concerns cross-checking hypotheses, connecting the dots, and forming questions to uncover new patterns that might have gone unnoticed by others. Data scientists collect, process, and explore vast datasets to reach conclusions that solve various problems. They understand data from past, present, and future perspectives via techniques like data mining, analyses, and machine learning. Data scientists can gain significant insights by leveraging unsupervised learning like clustering, deep learning, principal component analysis, neural networks, etc., and supervised learning such as regression, classification, etc. Their main objective is to find a new approach and give a fresh perspective to produce insights from the gathered data.
However, data analytics involves answering questions to create profitable business decisions. With the help of existing information, it concentrates on particular areas with a specific objective. It is a more well-defined subpart of data science and focuses mainly on analyzing the data rather than processing or predicting it. Data analytics aims to use big data and find an intelligent solution that can give better results quickly when implemented. It fundamentally converts statistical data into a representable form like charts, tables, and spreadsheets to obtain meaningful insights.
Data Science vs. Data Analytics: Tools used
Handling zettabytes of raw data to discover valuable outcomes requires specific data science tools that have predefined workflows, functions, and algorithms. For example, Statistical Analysis System (SAS) is an advanced analytics tool that deals with various statistical operations such as data mining, econometrics, time series analysis, etc. Distributed process of big data is managed via Apache Hadoop, while TensorFlow for deep learning, machine learning, and artificial intelligence. Even programming languages like Python and R have a comprehensive collection of libraries, such as Seaborn, Numpy, etc., for various phases in the data science lifecycle.
For data analytics, you can use Microsoft Excel, Google Analytics, Power BI, Apache Spark, etc. Excel is one of the most used tools for data analysis to find meaningful insights from the data. The features enable easy real-time collaboration and uploading of data via photos; however, you need to subscribe for advanced features. Tableau, on the other hand, is a free and effective business intelligence tool where you can spend your time on data analysis rather than on data wrangling. Power BI or Google Analytics is a good choice if you have no technical knowledge.
Data Science vs. Data Analytics: Job role
Data science has five main job roles: data scientist, data analyst, data engineer, business intelligence specialist, and data architecture.
A career path in data science has a different education requirement than that for data analytics. Most data analytics jobs do not require a degree in data analysis; a bachelor’s in a similar field is valid; however, you would need more advanced college degrees or specializations in data science with data science courses.
A computer science or mathematics major is preferable for data science jobs. On the other hand, if you have a degree in a related field and want to switch to a career in data science, then data analytics is a good stepping stone. While switching fields, working on personal projects, or getting certification is a great way to display domain knowledge.
The Data Scientist job profile consists of processing, cleaning, and verifying data integrity using machine learning workflows. They should understand artificial intelligence, cloud platforms, and data science concepts. By combining computer science, statistics, mathematics, and modeling skills, data scientists design and build workflows for analyzing the data.
The job role of a data analyst involves exploratory data analysis, data cleaning, finding new patterns, and developing easy-to-understand visualizations. A data analyst identifies, collects, cleans, researches, and interprets business data to produce essential insights to make better business judgments. Besides dealing with data, they must collaborate with business leaders, understand the problem, and provide effective solutions. Data analysts should recommend new techniques and strategies to enhance the marking campaigns. They need to ensure that KPIs are reviewed and published regularly. Another critical task is keeping track of the company’s performance and finding improvement areas.
What Should you Choose?
Before jumping into which path is more suited for you, let us reflect on the difference between the two. Data science mainly deals with data collection, storage, and optimization. Advance technical aspects such as machine learning, deep learning, neural networks, and statistics are involved in this field. Data science is a broad subject where data analytics is a part of the data science domain. Data analytics answers questions by analyzing and finding insights from existing data.
Now that you have understood the difference between data science and data analytics, you must be confused about the right career path. You can decide which course is more fitting depending on your interests and skills. For instance, if you want a more technical and mathematical inclined role, then data science is a good choice. While you are creative, a problem solver, and fond of discovering new insights from data, you will enjoy working as a data analyst. If your goal is to become a data scientist, start in the data analytics domain and work your way up or take professional courses to get the required qualification.
Conclusion
There is a fine distinction between data science and data analytics, even though both deal with data. Data science is a broad domain that deals with various processes regarding data, from collecting to storing and filtering it to finding a purpose. Meanwhile, data analytics identifies problem areas and improves them via insights derived from already present data. Data science and data analytics are both in demand nowadays; Therefore, do proper research, understand your interests and work on enhancing your skills to land a job in these fields.
Industries today are slowly moving towards chatbots as it helps automate tasks like customer support, sales, marketing, human resources, and more. This results in building more virtual assistant that reduces costs and improves the customer experience simultaneously. Many companies in India widely adopt chatbots. This article talks about companies that are creating customized top AI-based chatbots in India for business purposes.
Haptik
Developed in 2013, Haptik is a top AI-based conversational chatbot commerce platform to interact with customers on their preferred channel. It focuses on three main sectors — e-commerce, marketing, and customer care. Haptik works for enterprises to build Intelligent Virtual Assistants, improving their customer experience. Their services are provided across different businesses such as e-commerce, insurance, telecom, mortgage, gaming industries, and more. Many leading brands like KFC, Whirlpool, Reliance Jio, CEAT, OLA, Zurich Insurance, and more are official partners and successful clients of Haptik.
With Haptik’s commerce platform, conversational commerce can be digitalized, scaled, and deployed across all the messaging platforms. It provides organizations with providing on-spot recommendations to their customers in their preferred channels.
Haptik strengthens businesses’ marketing strategies by leveraging conversational ads and answering customer queries to generate leads instantly and ultimately increase the return on investment. It leverages proactive messaging to engage customers with personalized discounts, pending carts reminders, back-in-stock alerts, and more.
Maruti Techlabs
Maruti Techlabs was developed in 2009 as a digital product development company that guides businesses on their digital transformation journey with services like AI-based chatbots development, Analytics, and Product engineering. It guides firms right from materializing their ideas with rapid application development and using AI to streamline their customer support via chatbots. Maruti Techlabs provides organizations with a chatbot called WotNot, which creates intelligent, iterative, and customized bots. Organizations can acquire, engage, and retain more customers with such chatbots.
Bot development services of Maruti Techlabs assist businesses in handling mission-critical tasks, automating business growth at low maintenance costs, gaining higher ROIs, and integrating tools and systems seamlessly. With chatbot integration, Maruti Techlabs allows their clients to integrate with different channels like Messenger, Slack, Whatsapp, and more. As a result, chatbots can perform natural, relatable, and contextual conversations while fetching accurate information to drive business intelligence.
Maruti Techlabs has more than 11 years of experience and has clients from more than 30 countries. It works with brands like IKEA, Symphony Limited, Deloitte, Zydus Group, and more as a digital transformation and innovation partner.
Matellio
Developed in 2012, Matellio is a software engineering industry helping startups, entrepreneurs, and large enterprises in digital partnership. It is one of the leading companies among the top AI chatbot companies in India. Matellio includes solutions for organizations in AI, Blockchain development, Cloud integration, Embedded, Enterprise, Location-based, Mobile, IoT, Machine Learning, Staff Augmentation, and more. It is used by industries like banking, education, healthcare, entertainment, and real estate.
Matellio enables businesses to enrich customer experiences by using intelligent and cost-effective AI chatbot services for sales, marketing, and customer operations. This chatbot service comprises features like NLP, analysis, and machine learning that provide the most accurate customer recommendations for their products and services.
With an extensive experience of more than ten years, Matellio has worked for leading brands like AirFusion, Brideside, Goshow, Nervve, PTGi, and more. Matellio has served its services to more than 50 countries and has delivered more than 300 mobile apps and 600 web apps.
Quytech
Quytech is a top mobile app development company developed in 2010 that creates custom apps using AI, Android, iOS, Blockchain, Gaming, Blockchain, and VR/AR features. It comprises a wide range of services, including Android app development, strategic mobile consultancy, technology outsourcing, custom CRM development, product engineering services, offshore development center, AI development, and more. Quytech starts its mobile application development service with strategic mobile consulting, helping startups and enterprises choose the right platform for app development.
It provides organizations with AI-based chatbot development services for transforming ways to interact with customers. Chatbots built-in Quytech can work in e-commerce, entertainment, customer support, health care, delivery services, and more. With NLP and machine learning algorithms, it answers common customer queries, promotes specific products, conducts surveys, and collects customer data.
Quytech has more than 11 years of experience in serving industries with their services. It has global clients like the U.S., U.K., Canada, UAE, Europe, and more. Quytech has delivered more than a thousand projects to data and worked with more than 500 satisfied clients, including Mahindra First Choice, KMPG, Honda, Deloitte, Gabriel, Lemon Tree, Godfrey Philips, and more.
Yugasa software Labs
Yugasa software Labs is India’s best web and mobile app development company. It is one of the leading companies among the top AI chatbot companies in India. Yugasa software provides custom software solutions by building apps for organizations around technology like AI, IoT, and Blockchain. It has expertise in businesses such as Food, e-commerce, Dating, Real Estate, Social apps, Education, Travel, Sports, Taxi Booking, Medical, Pets, and more. Yugasa works on various technologies such as Node.js, PHP, UI/UX, HTML/CSS, MySQL, MongoDB, Angular JS, Meta apps, AI-based chatbots, Magneto, WooCommerce, and more.
It assists enterprises in automating their business communication with AI and NLP-enabled chatbot, YugasaBot. YugasaBot does not require any prior coding and can easily integrate with any website or mobile application for receiving customer queries and interacting with them. It performs tasks such as appointment booking, seat reservations, HR operations, and more.
Many leading companies like Pathstore, Stumpel, ABRA, WWF, PSB academy, Azure power, Mobil, and even the Indian army are using Yugasa for developing AI-based solutions. With more than 600 hundred projects, Yugasa has served various industries in more than 20 countries.
Developed in 2008, Trigma is a leading IT company that delivers innovative web and mobile application development solutions for clients across the USA and India. It provides services like custom software, content management systems, social media marketing, brand strategy consulting, online reputation management, quality assurance, digital advertising, SEO, IoT, Cloud, and AI/ML.
Trigma enables businesses to create AI-based customized chatbots using technologies like machine learning and artificial intelligence that can respond to verbal or typed commands. It provides an expert team of designers, programmers, and data scientists who can deliver profit-making chatbots. Trigma can build different chatbots, including NLP/deep learning-based chatbots, flow-based chatbots, back-end development chatbots, and IBM Watson Framework-based chatbots.
Trigma has clients from leading companies such as Samsung, Whirlpool, DisNep, Suzuki, Hero, Shell, British Council, Walmart, the Government of India, and more. With more than 12 years of experience, Trigma has believed in building long-lasting relationships with its customers by providing technical certified tech experts, an R&D team, and other resources to scale globally.
Hidden Brains
Hidden Brains was developed in 2008 and is a Software Development, IT Consulting firm servicing customers across the globe. It is one of the leading companies among the top AI chatbot companies in India. Hidden Brains comprises various services, including Chatbots, AI/ML, IoT, Blockchain, Web and App development, Front-end development, Backend development, Product prototyping, Cloud services, and more.
With Hidden Brains, businesses can create highly sophisticated and intelligent custom chatbots for interacting with customers. Along with AI, NLP, and Machine Learning technologies, Hidden Brains offers complete chatbot services for Facebook, Twitter, Kik, Slack, Microsoft, and more. Businesses can also create chatbots like conversion bots, IVR bots, online chatbots, texts bots, and more by using other chatbot frameworks such as Mircosoft chatbot, IBM Watson, Dialogflow, Facebook Bot, Chatfuel, and Amazon Lex. Hidden Brains have created chatbots for industries like healthcare, e-commerce, insurance, banking, travel, hospitality, and more.
Hidden Brains have more than 18 years of extensive experience delivering IT solutions and services to more than 2400 clients across the globe. It consists of more than 500 teams of technical and expert professionals.
Teplar Solutions
Developed in 2017, Teplar Solutions provide IT solutions and custom software development services on technologies such as business intelligence, artificial intelligence, machine learning, blockchain, the IoT, and more. With its robust technical experts and incredible experience in custom software development, Templar provides quality and cost-effective solutions.
AI technology mainly focuses on helping organizations assist in automating their day-by-day activities and therefore overcoming complex challenges that come their way. As a result, Teplar assists organizations in leveraging the potential of AI, ML, and NLP to build customized chatbots. Besides AI chatbots, Teplar also provides custom AI software development, advanced business analytics, boosted business automation, and more.
Developed in 2013, Squareboat is helping startups and MNCs build scalable digital products. The primary services of Squarboat include front-end development, backend development, app development, web design services, chatbot services, DevOps services, growth hacking services, QA testing services, data engineering, branding, and more.
Squareboat is one of the leading chatbot companies in India that offers professional services with estimated deadlines and user-friendly prices. It believes in building AI-based chatbots of varying complexity for organizations that can reach customers on every platform. Sqaureboat has several chatbot services such as Google Assistant Actions Development, Alexa Skills Development, Voice-based Chatbots, Customer support chatbots, Virtual assistant chatbots, NLP/AI chatbots, and more.
With more than ten years of experience, Squareboat has served leading brands like Paisabazar, PVR, Star, Elevation, SiSO, Dhruva, NeoPay, Juggernaut, LBB, and more.
Talentica
Talentica was developed in 2003 and is an innovative product development company with a track record of building more than 170 technology products for startups. It is one of the leading companies among the top AI chatbot companies in India. Talentica provides services in technologies like AI, machine learning, blockchain, IoT, connected devices, big data, augmented reality, DevOps and infrastructure, UX/UI, mobile services, and more.
Talentica uses the potential of AI technology and NLP to create custom chatbots that would help businesses understand, identify the questions in customer chats, and improve the interaction. Talentica also uses machine learning to recommend its services to customers.
Talentica has more than 18 years of experience helping customers transform their ideas into successful products across nations like the USA, Europe, and India. Many leading startups such as TALA, AlphaSense, Rupeek, Talentpool, Rubix, Opera, Citrus, and more have worked with Talentica. It has a successful team of technology experts who helps transform innovative ideas into reality.
With approximately 2.5 quintillion bytes generated daily, the need to understand and manage big data is also increasing. Companies collect data, process it, and derive meaningful insights on areas they can improve. Due to this, data science is one of the most popular skills in the present technology-driven world. 97.2% of the organization are currently investing in big data and data science, having data science skills becomes an advantage. Here is a list of seven free data science courses to master data science concepts:
An Introduction to Data Science – Udemy
An Introduction to Data Science is one of the best free data science courses you can take if you are a beginner and want to start your journey in data science. Created by Kumar Rajmain Bapat, this course helps you visualize, play, and manage data efficiently. Through this Udemy course, you will understand the primary data science concepts. The course will go through the history behind data science. This course will give you a detailed and easy-to-follow road map to mastering data science and becoming a full-fledged data scientist. You will also learn about the applications of data science in various fields and the skills required to build a career in the data domain.
The course primarily focuses on essential aspects of data science like the difference between noise and data, types of graphs for data visualization, and more. As this is an introductory course, there are no prerequisites. The total duration of the course is 43 minutes, which is suitable for anyone interested in data science.
IBM Data Science Professional Certificate – Coursera
Offered by IBM, the Data Science Professional Certificate has ten data science free courses that will boost your career in data science and machine learning (ML). This certification will guide you in building basic data science skills such as understanding Python and Structured Query Language (SQL) to create advanced machine learning models. There is no prior experience or degree requirement for this course.
With this professional certification, you will be able to learn various data science skills that are the minimum requirements needed for a job in the data science field. You will work on multiple projects to create an impressive portfolio of your skills to showcase to employers.
This course is entirely online and will take approximately 11 months if you have a pace of four hours per week to complete. You can start instantly and learn concepts at your own pace and schedule; all the deadlines are flexible. Coursera courses are free to audit, but if you want access to graded assignments or gain a course completion certificate, you will have to pay.
You will study these modules, data science tools, python programming for data science, databases, SQL, data analysis, data visualization, machine learning, and artificial intelligence (AI) development using python. At the end of the certification course, you will have an applied data science capstone project to give you real-life experience of the role of a data scientist.
SkillUp by Simplilearn provides free data science courses for beginners to help them understand the basics of data science and how to become a data scientist in today’s world. The “Introduction to Data Science” course will guide you through the essential data science workflows, tools, and techniques you would need to start your career as a data science professional. Upon completion of the course, you will be able to do exploratory data analysis, descriptive statistics, model building, fine-tuning inferential statistics, ensemble learning, supervised and unsupervised machine learning algorithms.
The prerequisites for this course are a basic understanding of mathematics and programming concepts. However, at the beginning of this course, you will get briefed about the top five python libraries used for data science. The course will also provide details on data science job roles, required skills, salary range, and more. It will guide you on how to make an impressive data science engineer resume and prepare you for an interview.
This course is seven hours long, but you will have only 90 days of access to your free lesson. On completion, you will receive a completion certificate which you can include in your resume to stand out from your competitors.
Created by Mattew Renze, Intro to Data for Data Science is another good course for absolute beginners in data science. This course will provide insights into data and its importance. You will learn about the data lifecycle, from data collection to processing and analysis. The course is approximately one-hour-long; hence it is excellent for those who want a small introduction to data science to check if this is the right course for them.
There are no requirements for this lesson as it is an introductory course for data science beginners. This lesson will cover nominal, ordinal, interval, and ratio data. With examples, you will learn scalar and composite data types. The course will conclude with tabular data and related concepts like relationships, queries, variables, and observations.
Note that you will only be given the online video content and will not have access to the instructor Q&A. You cannot directly message the instructor if you want to clear your doubts. Once you have completed the course, you will not receive any certificate from Udemy.
For those without coding experience, this course provided by DataCamp is a good choice. The Data Science for Everyone course is approximately two hours long and will answer all the questions you were afraid to ask about data science. Without writing one line of code, you will get hands-on exercises and go through concepts like A/B testing, machine learning workflows, and time series analysis.
The four chapters in this course will help you learn how data science quickly solves many real-world problems. In the first chapter, you will understand the different data science lifecycle processes and job roles in the data science domain, the workflow, and the data lifecycle. The second chapter will teach data collection, storage, and data pipeline automation. The next lesson is on preparation, exploration, and visualization of data and how to diagnose problems with your data. The last lesson consists of data experimentation, prediction, A/B testing, and forecasting. You will be briefed about machine learning workflows, clustering, and supervised learning.
Among the different software languages, R is extensively used for data science. If you want to kick-start your career in data science, then learning R is one of the essential requirements. Data Science: R basics by edX help build a strong foundation in R. Upon completing the course, you will be able to manage, analyze and visualize data efficiently. This introductory course is the first part of their professional certification program in data science. If you dedicate one or two hours a week, you should be able to complete this course approximately in eight weeks.
The course is created by Rafael Irizarry, professor of Biostatistics at Harvard University. This course teaches basic coding features like data types, vectors, indexing, arithmetic, and loop commands to sort, manage and analyze data. Further in the series, you will learn topics like probability, regression, inference, and machine learning algorithms. With R, you will use dplyr for data wrangling, ggplot1 for visualization, and UNIX/Linux for file organization.
LinkedIn Learning is one of the best platforms for free learning resources on any domain. The data science free course has twelve sessions and consists of 20 hours of content. Learn about all the fundamentals of data science, from statistics to system engineering to data mining and machine learning. With these lessons, you will build a strong foundation in statistics and math, which is essential for any data science-related domain. Through graphs, you will be able to source, explore and interpret data easily.
The twelve courses cater to the non-technical skills and the technical skills you will have to develop to master data science. The lessons cover various fundamental topics of statistics and mathematics like standard deviation, probability distribution, central tendency, variability, and more. You will also learn about the relationship between big data and AI, the Internet of Things (IoT), data science, and social media. One of the free data science courses is dedicated to lessons learned from data scientists, which will give you insights and advice from current data scientists.
All these free data science courses are beginner-friendly and easy to follow. You can check out Kaggle, a data science and analysis website, for more resources. It has over 50,000 public datasets which you can practice and analyze. It even has short courses on essential data science skills like python, machine learning, pandas, NumPy, deep learning, and more. If you want to learn more about data science after completing an introductory course, you should do a professional certification course to enhance your knowledge.
Vauld, a Singapore-based cryptocurrency lending and exchange start-up, recently announced that it had suspended withdrawals, deposits, and trading on its platform with immediate effect. The three-year-old start-up cited ‘navigating through financial challenges amid the market downturn’ as the reason for suspension.
Vauld is a Singapore-based crypto platform that enables customers to lend, borrow, and trade crypto assets with Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and other significant cryptocurrencies from one unified platform. The company counts Valar Ventures, Coinbase Ventures, and Pantera Capital among its backers.
In a blog post on the company’s website, Darshan Bathija, Vauld’s co-founder and CEO, talked about the financial difficulties of business partners and customer withdrawals. He explained how the circumstances had prompted customer withdrawals of about $198 million since the 12th of June as the cryptocurrency market declined after the collapse of Terraform Lab’s UST stablecoin, followed by Celsius Network pausing withdrawals and Three Arrows Capital defaulting on loans.
Bathija said the start-up is considering restructuring options and has reached out to Kroll for financial advice. The company has also consulted Rajah & Tann and Cyril Amarchand Mangaldas for legal advice in Singapore and India, respectively. The start-up has expressed intentions to apply for a moratorium at the Singapore courts.
While announcing the hiatus, Bhatija said that the company is seeking the understanding of its customers on the Vauld platform as it is not in a position to process any new or further requests. Certain arrangements will be made for customer deposits as necessary to meet margin calls in connection with collateralized loans. The announcement was followed by Vauld cutting its workforce by 30% about two weeks ago.
While the reason for the suspension is apparent, the move on behalf of Vauld does come as a surprise to the industry. On the 16th of June, after crypto lending platform Celsius announced increasing financial challenges, Bathija assured Vauld’s customers in a tweet that the platform was not headed toward the predicament of Celsius. He also affirmed that Vauld is far from the fate of Three Arrows Capital, another one of the high-profile cryptocurrency platforms that filed for bankruptcy.
Bhatija had stated earlier that Vauld would remain liquid despite market conditions. He also informed all withdrawals were processed as usual over the last few days and will continue to be the same in the future. However, the withdrawal announcement after such an affirmative statement might be confusing for some to wrap around their heads.
Recently, FTX’s US-based subsidiary signed an agreement with another financially strained crypto lender BlockFi. The deal gives FTX the option to buy the startup for up to $240 million based on its performance. BlockFi was among those firms that liquidated some positions held by Three Arrows Capital. It was valued at $3 billion in a financing round.
According to the company website, Vauld enables customers to earn the industry’s highest interest rates on major cryptocurrencies. The site says it offers 6.7% annual yields on staking Bitcoin and Ethereum tokens and 12.68% yearly yields on stablecoins such as USDC and BUSD. It also allows customers to borrow against their tokens and several other trading services. Vauld says, on its website, that it offers users the ability to borrow up to a Loan To Value (LTV) of 66.67% against their tokens.
Many crypto tokens like ethereum, binance coin, and tether have fallen by over 50% in value in the past six months, like several tech stocks. The major reason for the fall is the panic sell-off by investors, especially whales, amid the increasing fear of inflation. In a recent podcast, Binance founder and chief executive Changpeng Zhao said that Binance has engaged with over fifty firms to strategize funding and bailing out opportunities for companies in recent weeks. Several crypto experts have warned in recent weeks that many more decentralized finance (DeFi) platforms are on the verge of facing a collapse as financial constraints cripple the businesses, same as Vauld.
While this might seem like an endgame for Vauld, this temporary hiatus is not expected to be the end of this cryptocurrency startup. Recently in a tweet, Bhatija expressed his confidence in the fact that with the advice of their financial and legal advisors, Vauld will be able to reach a solution that will best protect the interests of its stakeholders and customers.
Reddit is introducing a brand-new NFT-based avatar marketplace that lets you pay a fixed price to buy blockchain-based profile photos. These NFTs will be hosted on Polygon’s blockchain. According to the company, you don’t need a cryptocurrency wallet to buy them; using your credit or debit card should be sufficient. You can also use Reddit’s wallet product to hold them.
According to Reddit’s announcement, 90 different NFT designs are available in the “tens of thousands” during this early-access phase. NFT avatars will initially only be accessible to subscribers of the invite-only r/CollectibleAvatars subreddit, with quoted costs being $9.99, $24.99, $49.99, $74.99, or $99.99.
The company stated that if you buy one of its limited-edition NFTs, you will be granted permission to use it as an avatar both on and off Reddit. These privileges do not equate to those that come with owning an NFT from the Bored Ape Yacht Club collection of Yuga Labs, which permits you to create products or TV shows based on the bored ape you own. These avatars’ appearances can be customized using the avatar builder’s goods. Additionally, user avatars will get a “glow-like effect next to their comments in communities.”
In the upcoming weeks, everyone will be able to purchase these collectible avatars on Reddit’s avatar builder page. Community members will get unique Ask Me Anythings (AMAs) from artists, behind-the-scenes posts regarding the one-off profile pictures, and instructions for setting up a wallet in the meantime.
But only local currencies, like US dollars, will be accepted for the purchase of Collectible Avatars. Neither a cryptocurrency option nor an auction on a secondary market like OpenSea is currently available for them. Reddit also explains that the marketplace is based on Polygon’s blockchain because of its commitment to sustainability and low-cost transactions. Reddit will introduce a Vault, an Ethereum-compatible wallet, along with the Polygon marketplace.
Reddit NFT Marketplace isn’t Reddit’s first stint into NFTs. In January, Reddit began testing a feature that lets users choose any Ethereum-based NFT as their profile picture. This came after Twitter introduced a feature that allowed users to set their NFTs as profit pictures. With this feature, users could set photos of their NFTs that, when clicked, would reveal information about the NFT and would stand out from the default Twitter profile picture by being hexagon-shaped. These social media companies are aware of the demand for crypto-related features like the NFT profile image from users looking to incorporate them as a status indicator of their digital presence.
Many social media companies are working to make their platforms NFT-enabled. Recently, it was revealed that YouTube and Instagram are also exploring NFTs, while Meta Inc. intends to create a new NFT marketplace.
Reddit revealed in a statement, that in the future, blockchain will bring more empowerment and independence to communities on Reddit. The company prides itself on always being a model for what decentralization could look like online. “Our communities are self-built and run, and as part of our mission to better empower them, we are exploring tools to help them be even more self-sustaining and self-governed,” it added. Based on its mascot “Snoo,” Reddit released limited-edition NFTs in 2021 under the name CryptoSnoos.
IBM has recently announced its acquisition of Databand.ai, a Tel-Aviv-based data observability start-up company. Databand.ai provides services that alleviate data errors, poor data quality, and pipeline failures to prevent a company’s bottom line from being impacted.
IBM hopes to ensure data security at all times by acquiring Databand.ai. The tech giant expects to strengthen its software portfolio across artificial intelligence, automation, and data.
Data observability is a newly emerging prime solution that helps engineers and companies understand the status of their data and efficiently troubleshoot and address issues as they arise.
Databand.ai employs an extendable and open approach that enables data engineering teams to integrate and gain observability in their data infrastructure. Through this partnership with IBM, Databand.ai will be able to expand its data integration capabilities to meet the needs of customers with commercial data solutions.
IBM will also benefit from this bilateral acquisition. Databand.ai has created a unified data pipeline observability solution for data engineers. This software will partner with IBM Watson Studio and IBM Observability by Instana APM to address the full spectrum of observability across information technologies.
With the acquisition of Databand.ai, IBM can offer the most comprehensive set of observability capabilities for IT across machine learning, applications, and data. IBM continues to provide its partners and clients with the technology to deliver reliable data and artificial intelligence at scale.
FIFA hopes to enhance one of Soccer’s most disputed rules with artificial intelligence (AI). In the upcoming World Cup, the organization will roll out the application of AI to improve offside decisions and alert the team of video referees to make accurate judgments.
ESPN FC Editor Dale Johnson elaborated on the need for improved offside decision-making and that to be done with AI. He explained that offside is vital in every penalty, goal, and attacking move. Every time a player goals (or touches the ball), three defensive players must be in front of them. If that is not the case, the player is at an advantage they should not have, leading to incorrect goals.
FIFA hopes that AI will make the calls for the appropriateness of a goal in a much lesser time. A straightforward offside decision takes around 70 seconds, FIFA says. This duration will now be reduced to 25 seconds with AI., so the decision-making course is cut by almost two-thirds. This also implies that people on the ground and those witnessing the match at home will probably not notice any difference.
It was not until 2017-18 that FIFA accepted technological assistance in the game. A few that existed before, like the goal-line technology, were massively criticized. Now, the organization seems to have taken the efficacy of technology and AI to produce a 3D animation of whether the ball is over the line or not. It is now open to fixing offside issues with AI assistance.
Johnson said, “So this won’t be something that is just for the World Cup. We will see offside improve much more in the other leagues next year in 2023. So this is FIFA’s big idea. It’s to improve the game and drive it forward by introducing this AI technology.”
Language is not just a communication tool but an expression of different cultures, societies, and opinions across the globe. Nonetheless, language is also the barrier separating them. Thanks to translation technologies and artificial intelligence (AI) taking over the linguistic world, people can now read in their preferred languages. The world would lose a significant portion of its cultural treasures if it weren’t for translation and, more recently, technologies for translation.
Like other technological developments, translation technologies have evolved too. Currently, the most frequently used method of translation is via machines, Machine Translation (MT). Other methods like Computer-Assisted Translation (CAT) Technology are also prominently used. These technologies have undoubtedly offered seamless communication capabilities that people have wanted for ages. Likewise, they still have undeniable limitations.
All tools and technologies used for translation work on different principles and consequently deliver different results. Some offer more accurate results, while others are compatible with a more significant number of languages. Moreover, all high-end translation tools are not accessible to billions of people and are incompatible with hundreds of languages. People cannot openly participate in online conversations and communities in their regional/native languages.
To remove some of these barriers and make people a part of the future metaverse, AI researchers at Meta have created ‘No Language Left Behind-200’ or NLLB-200, an AI model to enhance machine translation capabilities for most of the world’s languages. The company claims that the model translates 200 languages with higher accuracy by an average of 44%. These languages include lesser-known African languages like Kamba and Lao (55 in total) and languages from other parts of the world. Such languages are incompatible with other existing translation tools.
No Language Left behind (NLLB) is a part of Meta’s long-term efforts to build language and machine translation tools. Launched in February 2022, the project builds advanced AI models to learn and decipher languages based on fewer examples.
The NLLB-200 is made to truly serve everyone, as other AI systems are not designed to cater to hundreds of local languages and provide a real-time speech-to-speech translation. Covering 200 languages is a step forward in overcoming data scarcity and acquiring more training data in local/regional languages. The new AI model also aims to overcome some modeling challenges of expansion faced by the company in previous years.
It is not the first time that Meta has developed a translation model. It released the 100-language M2M-100 translation model in 2020 with improved architectures and data acquiring practices. The AI company has now scaled to another 100 languages in NLLB-200. It can be used to advance other technologies, developing assistants for languages like Uzbek and creating subtitles for movies in Oromo/Swahili. There are endless possibilities to extend its application and democratize access for people in virtual worlds.
Meta trained NLLB-200 on FLORES-200, a dataset that enables AI’s performance assessment in 40,000 different language directions. The dataset measured NLLB-200’s performance in each of the 200 languages to be highly accurate.
Adding to the upsides, Meta is open-sourcing the model and the FLORES-200 dataset to all developers. It has also open-sourced the model training code. The company has also provided a demo to show the application of this open-source translator. The sole reason behind providing open-source access is to help researchers improve their work and translation capabilities via machines. Since inaccessibility is a major drawback of other language translation technologies/tools, Meta’s AI would make technology accessible to ordinary people.
Further, NLLB-200 will aid in promoting native languages and enabling people to read things without an intermediary language. Languages like Mandarin, English, and Spanish dominate the language webspace. Many people from other countries or regions cannot get the sentiments or context of things written in languages other than their own. NLLB-200 will bridge this gap and add meaning to the text, as people can now read in their preferred language.
As an incentive to use the AI model impactfully, Meta is awarding up to US$200,000 grants to researchers and nonprofit organizations. These researchers/organizations are invited to use NLLB-200 to translate underrepresented languages.
Meta has also collaborated with Wikimedia Foundation, a nonprofit organization, to offer translation services on Wikipedia. The model would help reduce the disparity between English publications on the website and those in other languages, especially those spoken outside of America and Europe. For instance, there are only 3,260 Wiki articles in Lingala, a native language spoken by 45M people in the Democratic Republic of Congo, against 2.5M Wiki articles in a language like Swedish, spoken in Sweden and Finland by much lesser people.
Even though the AI model has enhanced accuracy and meaningful translation of more languages than before, there is an endless scope for improvement. 200 languages cannot cover the entire language space. Additionally, the company faced several challenges in expanding the model from 100 to 200 languages. Since many of these languages are regional, the challenge is to acquire data from low-resource datasets. The model starts overfitting if trained for extended periods due to data scarcity. Such challenges would only scale as the number of languages increases. Long story short, there is a long road ahead for translation technologies, but NLLB-200 takes us one step forward in the right direction. Meta plans to strive for a more inclusive and connected world by breaking down linguistic and technological barriers and empowering people.
