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Abstract

— literally anyone can start to learn Python with no prior experience.</p><h2 id="9d3e">IBM Data Science from IBM</h2><p id="dae7">The program consists of 9 online courses that will provide you with the <b>latest job-ready tools and skills</b>, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.</p><p id="dcfd">Upon successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.</p><p id="70b3">In addition to earning a Professional Certificate from Coursera, you’ll also receive a <b>digital badge from IBM</b> recognizing your proficiency in data science.</p><p id="c0a0">Perfect after some preliminary reading on Python, or taking Python for Everybody course described above.</p><h2 id="3ddf">Machine Learning from Stanford University</h2><p id="2e1a">This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).</p><p id="a4ef">The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.</p><p id="4ee1">This course by Andrew Ng kickstarted Coursera.</p><h2 id="bca0">Deep Learning from DeepLearning.AI</h2><p id="be41">In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.</p><h2 id="ede4">Applied Data Science with Python from University of Michigan</h2><p id="765e">The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.</p><h2 id="a533">DeepLearning.AI TensorFlow Developer from DeepLearning.AI</h2><p id="0d8e">In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the <a href="https://www.tensorflow.org/certificate"><b>Google TensorFlow Certificate exam</b></a> and bring you one step closer to achieving the Google TensorFlow Certificate.</p><h2 id="8262">Data Science from Johns Hopkins University</h2><p id="16ae">This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At

Options

completion, students will have a portfolio demonstrating their mastery of the material.</p><h2 id="e841">Learn SQL Basics for Data Science from University of California, Davis</h2><p id="1113">This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets.</p><h2 id="251e">Business Analytics from University of Pennsylvania</h2><p id="697a">This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and you’ll develop basic data literacy and an analytic mindset that will help you make strategic decisions based on data. In the final Capstone Project, you’ll apply your skills to interpret a real-world data set and make appropriate business strategy recommendations.</p><h2 id="eaec">Mathematics for Machine Learning from Imperial College London</h2><p id="43e1">For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics — stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.</p><h2 id="17ab">Natural Language Processing from DeepLearning.AI</h2><p id="251d">By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.</p><h1 id="9928">Which Data Science course on Coursera should I take?</h1><p id="5627">If you’re thinking where to start, here’s a short guide. Everything depends on your proficiency.</p><p id="0db3">If you’re a total beginner, never coded in your life or don’t remember anything, just start with Python for Everybody.</p><p id="42a6">If you know a bit of Python, or other programming language, then go directly for IBM Data Science.</p><p id="1d61">These courses make it easy to start, they are self-contained and allow you to practice coding within your browser, not leaving the course.</p><p id="d666">If you’re a bit more experienced, you can with the classic: Machine Learning from Stanford. It’s more theoretical, but very well taught. For a necessary refresher of mathematics, try Mathematics for Machine Learning before it.</p><p id="ef95">If you want to learn more about courses on Coursera have a look <a href="https://readmedium.com/top-8-courses-on-coursera-in-2021-251cd0d15995">here</a>. For reading materials, go <a href="https://readmedium.com/data-science-books-you-should-start-reading-in-2021-73b40a76aad9">here for Data Science</a> and <a href="https://readmedium.com/top-machine-learning-books-to-read-in-2021-8d86de3d0788">here for Machine Learning</a>.</p><p id="0bc1">Happy learning!</p><p id="9d0e"><i>P.S. This text was originally posted on our <a href="https://datasciencerush.com/2021/04/28/top-11-data-science-courses-on-coursera-in-2021/">Data Science Rush blog</a>.</i></p><p id="38bb"><i>P.P.S. links are affiliate, thank you for your support.</i></p></article></body>

Top 11 Data Science Courses on Coursera in 2021

How to become a Data Scientist by learning online.

Data Science is ubiquitous in any business and the only way to make sense of huge datasets businesses collect each day.

Becoming a data scientist takes time and effort, but it’s definitely worth it. Data science skills are growingly useful across all business domains (and you can always analyse data for your own hobby!).

This text describes top 11 data science courses available on Coursera right now and ranked by popularity.

Top 11 Data Science Courses on Coursera in 2021

Most popular Data Science Courses in 2021

Let’s start with the list of all courses first:

Let’s now discuss them briefly one by one. If you wonder where to start, scroll to the bottom. I’ll give my advice on how to approach it below.

Python for Everybody from University of Michigan

This Specialization will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language.

In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

No prerequisites — literally anyone can start to learn Python with no prior experience.

IBM Data Science from IBM

The program consists of 9 online courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.

Upon successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.

In addition to earning a Professional Certificate from Coursera, you’ll also receive a digital badge from IBM recognizing your proficiency in data science.

Perfect after some preliminary reading on Python, or taking Python for Everybody course described above.

Machine Learning from Stanford University

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

This course by Andrew Ng kickstarted Coursera.

Deep Learning from DeepLearning.AI

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

Applied Data Science with Python from University of Michigan

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

DeepLearning.AI TensorFlow Developer from DeepLearning.AI

In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate.

Data Science from Johns Hopkins University

This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

Learn SQL Basics for Data Science from University of California, Davis

This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets.

Business Analytics from University of Pennsylvania

This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and you’ll develop basic data literacy and an analytic mindset that will help you make strategic decisions based on data. In the final Capstone Project, you’ll apply your skills to interpret a real-world data set and make appropriate business strategy recommendations.

Mathematics for Machine Learning from Imperial College London

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics — stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

Natural Language Processing from DeepLearning.AI

By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.

Which Data Science course on Coursera should I take?

If you’re thinking where to start, here’s a short guide. Everything depends on your proficiency.

If you’re a total beginner, never coded in your life or don’t remember anything, just start with Python for Everybody.

If you know a bit of Python, or other programming language, then go directly for IBM Data Science.

These courses make it easy to start, they are self-contained and allow you to practice coding within your browser, not leaving the course.

If you’re a bit more experienced, you can with the classic: Machine Learning from Stanford. It’s more theoretical, but very well taught. For a necessary refresher of mathematics, try Mathematics for Machine Learning before it.

If you want to learn more about courses on Coursera have a look here. For reading materials, go here for Data Science and here for Machine Learning.

Happy learning!

P.S. This text was originally posted on our Data Science Rush blog.

P.P.S. links are affiliate, thank you for your support.