best online course for data science

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Over the course of several years and 100+ hours watching course videos, engaging with quizzes and assignments, reading reviews on various aggregators and forums, I’ve narrowed down the best data science courses available to the list below.

This is a fairly long article with reviews of each course, so here’s the TL;DR:

8 Best Data Science Courses & Certifications for 2022:

  1. Data Science Specialization — JHU @ Coursera
  2. Introduction to Data Science — Metis
  3. Applied Data Science with Python Specialization — UMich @ Coursera
  4. Data Science MicroMasters — UC San Diego @ edX
  5. Dataquest
  6. Statistics and Data Science MicroMasters — MIT @ edX
  7. CS109 Data Science — Harvard
  8. Python for Data Science and Machine Learning Bootcamp — Udemy


The selections here are geared more towards individuals getting started in data science, so I’ve filtered courses based on the following criteria:

  • The course goes over the entire data science process
  • The course uses popular open-source programming tools and libraries
  • The instructors cover the basic, most popular machine learning algorithms
  • The course has a good combination of theory and application
  • The course needs to either be on-demand or available every month or so
  • There are hands-on assignments and projects
  • The instructors are engaging and personable
  • The course has excellent ratings – generally, greater than or equal to 4.5/5

There are a lot more data science courses than when I first started this page four years ago, and so there needs to now be a substantial filter to determine which courses are the best. I hope you feel confident that the courses below are truly worth your time and effort because it will take several months of learning and practice to be a data science practitioner.

In addition to the top general data science course picks, I have included a separate section for more specific data science interests, like Deep Learning, SQL, and other relevant topics. These are courses with a more specialized approach and don’t cover the whole data science process, but they are still the top choices for that topic. These extra picks are good for supplementing before, after, and during the main courses.

If you’re more interested in just learning machine learning, then check out my complementary article on the best machine learning courses for this year.

Resources you should use when learning

When learning data science online it’s important to not only get an intuitive understanding of what you’re actually doing but also to get sufficient practice using data science on unique problems.

In addition to the courses listed below, I would suggest reading two books:

  1. Introduction to Statistical Learning — available for Free — one of the most widely recommended books for beginners in data science. Explains the fundamentals of machine learning and how everything works behind the scenes
  2. Applied Predictive Modeling — a breakdown of the entire modeling process on real-world datasets with incredibly useful tips each step of the way

These two textbooks are incredibly valuable and provide a much better foundation than just taking courses alone. The first book is incredibly effective at teaching the intuition behind much of the data science process, and if you are able to understand almost everything in there, then you’re more well off than most entry-level data scientists.

Furthermore, since both of these books utilize R in their exercises and examples, a great learning experience would be to work through them in R and then convert them to Python.

1. Data Science Specialization — JHU @ Coursera

This course series is one of the most enrolled and highly rated course collections on this list. JHU did an incredible job with the balance of breadth and depth in the curriculum. One thing that’s included in this series that’s usually missing from many data science courses is a complete section on statistics, which is the backbone of data science.

Overall, the Data Science specialization is an ideal mix of theory and application using the R programming language. As far as prerequisites go, you should have some programming experience (doesn’t have to be R) and you have a good understanding of Algebra. Previous knowledge of Linear Algebra and/or Calculus isn’t necessary, but it is helpful.

Price – Free or $49/month for certificate and graded materials
Provider – Johns Hopkins University


  1. The Data Scientist’s Toolbox
  2. R Programming
  3. Getting and Cleaning Data
  4. Exploratory Data Analysis
  5. Reproducible Research
  6. Statistical Inference
  7. Regression Models
  8. Practical Machine Learning
  9. Developing Data Products
  10. Data Science Capstone

If you’re rusty with statistics and/or want to learn more R first, check out the Statistics with R Specialization as well.

2. Introduction to Data Science — Metis

An extremely highly rated course — 4.9/5 on SwichUp and 4.8/5 on CourseReport — which is taught live by a data scientist from a top company. This is a six-week-long data science course that covers everything in the entire data science process, and it’s the only live online course on this list. Furthermore, not only will you get a certificate upon completion, but since this course is also accredited, you’ll also receive continuing education units.

Two nights per week, you’ll join the instructor with other students to learn data science as if it was an online college course. Not only are you able to ask questions, but the instructor also spends extra time for office hours to further help those students that might be struggling.

Price — $750

The curriculum:

  1. Computer Science, Statistics, Linear Algebra Short Course
  2. Exploratory Data Analysis and Visualization
  3. Data Modeling: Supervised/Unsupervised Learning and Model Evaluation
  4. Data Modeling: Feature Selection, Engineering, and Data Pipelines
  5. Data Modeling: Advanced Supervised/Unsupervised Learning
  6. Data Modeling: Advanced Model Evaluation and Data Pipelines | Presentations

For prerequisites, you’ll need to know Python, some linear algebra, and some basic statistics. If you need to work on any of these areas, Metis also has Beginner Python and Math for Data Science, a separate live online course just for learning Python, Stats, Probability, Linear Algebra, and Calculus for data science. If you’re interested in taking a dedicated Python course, see my Python course article for the best offerings according to data analysis.

3. Applied Data Science with Python Specialization — UMich @ Coursera

The University of Michigan, which also launched an online data science Master’s degree, produce this fantastic specialization focused on the applied side of data science. This means you’ll get a strong introduction to commonly used data science Python libraries, like matplotlib, pandas, nltk, scikit-learn, and networkx, and learn how to use them on real data.

This series doesn’t include the statistics needed for data science or the derivations of various machine learning algorithms but does provide a comprehensive breakdown of how to use and evaluate those algorithms in Python. Because of this, I think this would be more appropriate for someone that already knows R and/or is learning the statistical concepts elsewhere.

If you’re rusty with statistics, consider the Statistics with Python Specialization first. You’ll learn many of the most important statistical skills needed for data science.

Price – Free or $49/month for certificate and graded materials
Provider – University of Michigan


  1. Introduction to Data Science in Python
  2. Applied Plotting, Charting & Data Representation in Python
  3. Applied Machine Learning in Python
  4. Applied Text Mining in Python
  5. Applied Social Network Analysis in Python

To take these courses, you’ll need to know some Python or programming in general, and there are actually a couple of great lectures in the first course dealing with some of the more advanced Python features you’ll need to process data effectively.

4. Data Science MicroMasters — UC San Diego @ edX

MicroMasters from edX are advanced, graduate-level courses that count towards a real Master’s at select institutions. In the case of this MicroMaster’s, completing the courses and receiving a certificate will count as 30% of the full Master of Science in Data Science degree from Rochester Institute of Technology (RIT).

Since these courses are geared towards prospective Master’s students, the prerequisites are higher than many of the other courses on this list. Since the first course in this series doesn’t spend any time teaching basic Python concepts, you should already be comfortable with programming. Spending some time going through a platform like Treehouse would probably get you up to speed for the first course.

Overall, I found this MicroMaster’s to be a perfect mix of theory and application. The lectures are comprehensive in scope and balanced superbly with real-world applications.

Price – Free or $1,260 for certificate and graded materials
Provider – UC San Diego


  1. Python for Data Science
  2. Probability and Statistics in Data Science using Python
  3. Machine Learning Fundamentals
  4. Big Data Analytics using Spark

The one downside of this MicroMaster’s, and many courses on edX, is that they aren’t offered as frequently as other platforms. If your schedule aligns with the start date of the first course, definitely consider jumping in.

5. Dataquest

Dataquest is a fantastic resource on its own, but even if you take other courses on this list, Dataquest serves as a superb complement to your online learning.

Dataquest foregoes video lessons and instead teaches through an interactive textbook of sorts. Every topic in the data science track is accompanied by several in-browser, interactive coding steps that guide you through applying the exact topic you’re learning.

Video-based learning is more “passive” — it’s very easy to think you understand a concept after watching a 2-hour long video, only to freeze up when you actually have to put what you’ve learned in action. — Dataquest FAQ

To me, Dataquest stands out from the rest of the interactive platforms because the curriculum is very well organized, you get to learn by working on full-fledged data science projects, and there’s a super active and helpful Slack community where you can ask questions.

The platform has one main data science learning curriculum for Python:

Data Scientist In Python Path
This track currently contains 31 courses, which cover everything from the very basics of Python, to Statistics, to math for Machine Learning, to Deep Learning, and more. The curriculum is constantly being improved and updated for a better learning experience.

Price – 1/3 of content is Free, 29/monthforBasic,49/month for Premium

Here’s a condensed version of the curriculum:

  1. Python – Basic to Advanced
  2. Python data science libraries – Pandas, NumPy, Matplotlib, and more
  3. Visualization and Storytelling
  4. Effective data cleaning and exploratory data analysis
  5. Command-line and Git for data science
  6. SQL – Basic to Advanced
  7. APIs and Web Scraping
  8. Probability and Statistics – Basic to Intermediate
  9. Math for Machine Learning – Linear Algebra and Calculus
  10. Machine Learning with Python – Regression, K-Means, Decision Trees, Deep Learning, and more
  11. Natural Language Processing
  12. Spark and Map-Reduce

Additionally, there are also entire data science projects scattered throughout the curriculum. Each project’s goal is to get you to apply everything you’ve learned up to that point and to get you familiar with what it’s like to work on an end-to-end data science strategy.

Lastly, if you’re more interested in learning data science with R, then definitely check out Dataquest’s new Data Analyst in R path. The Dataquest subscription gives you access to all paths on their platform, so you can learn R or Python (or both!).

6. Statistics and Data Science MicroMasters — MIT @ edX

The inclusion of probability and statistics courses makes this series from MIT a very well-rounded curriculum for being able to understand data intuitively. This MicroMaster’s from MIT dedicates more time towards statistical content than the UC San Diego MicroMaster’s mentioned earlier in the list.

Due to its advanced nature, you should have experience with single and multivariate calculus, as well as Python programming. There isn’t any introduction to Python or R like in some of the other courses in this list, so before starting the ML portion, they recommend taking Introduction to Computer Science and Programming Using Python to get familiar with Python. If you’d rather utilize an on-demand interactive platform to learn Python, check out Treehouse’s Python track.

Price – Free or $1,350 for certificate and graded materials
Provider – University of Michigan


  1. Probability – The Science of Uncertainty and Data
  2. Data Analysis in Social Science—Assessing Your Knowledge
  3. Fundamentals of Statistics
  4. Machine Learning with Python: from Linear Models to Deep Learning
  5. Capstone Exam in Statistics and Data Science

The ML course has several interesting projects you’ll work on, and at the end of the whole series, you’ll focus on one exam to wrap everything up.

7. CS109 Data Science — Harvard

With a great mix of theory and application, this course from Harvard is one of the best for getting started as a beginner. It’s not on an interactive platform, like Coursera or edX, and doesn’t offer any sort of certification, but it’s definitely worth your time and it’s totally free.


  • Web Scraping, Regular Expressions, Data Reshaping, Data Cleanup, Pandas
  • Exploratory Data Analysis
  • Pandas, SQL and the Grammar of Data
  • Statistical Models
  • Storytelling and Effective Communication
  • Bias and Regression
  • Classification, kNN, Cross-Validation, Dimensionality Reduction, PCA, MDS
  • SVM, Evaluation, Decision Trees and Random Forests, Ensemble Methods, Best Practices
  • Recommendations, MapReduce, Spark
  • Bayes Theorem, Bayesian Methods, Text Data
  • Clustering
  • Effective Presentations
  • Experimental Design
  • Deep Networks
  • Building Data Science

Python is used in this course, and there are many lectures going through the intricacies of the various data science libraries to work through real-world, interesting problems. This is one of the only data science courses around that actually touches on every part of the data science process.

8. Python for Data Science and Machine Learning Bootcamp — Udemy

Also available using R.

A very reasonably priced course for the value. The instructor does an outstanding job explaining the Python, visualization, and statistical learning concepts needed for all data science projects. A huge benefit to this course over other Udemy courses is the assignments. Throughout the course you’ll break away and work on Jupyter notebook workbooks to solidify your understanding, then the instructor follows up with a solutions video to thoroughly explain each part.


  • Python Crash Course
  • Python for Data Analysis – Numpy, Pandas
  • Python for Data Visualization – Matplotlib, Seaborn, Plotly, Cufflinks, Geographic plotting
  • Data Capstone Project
  • Machine learning – Regression, kNN, Trees and Forests, SVM, K-Means, PCA
  • Recommender Systems
  • Natural Language Processing
  • Big Data and Spark
  • Neural Nets and Deep Learning

This course focuses more on the applied side, and one thing missing is a section on statistics. If you plan on taking this course it would be a good idea to pair it with a separate statistics and probability course as well.

An honorary mention goes out to another Udemy course: Data Science A-Z. I do like Data Science A-Z quite a bit due to its complete coverage, but since it uses other tools outside of the Python/R ecosystem, I don’t think it fits the criteria as well as Python for Data Science and Machine Learning Bootcamp.

Other top data science courses for specific skills

Deep Learning Specialization — Coursera
Created by Andrew Ng, maker of the famous Stanford Machine Learning course, this is one of the highest-rated data science courses on the internet. This course series is for those interested in understanding and working with neural networks in Python.

Complete SQL Mastery — CodeWithMosh
Pair this with Coursera’s SQL for Data Science course for a very well-rounded introduction to SQL, an important and necessary skill for data science.

Computational Thinking using Python XSeries — edX
Although this series only runs once every several months, if you’re new to Computer Science and Python this a great series to jump into if you get the chance. I found the lecturers to be really passionate about what they teach, making it a pleasant experience taking the courses.

Mathematics for Machine Learning — Coursera
This is one of the most highly rated courses dedicated to the specific mathematics used in ML. Take this course if you’re uncomfortable with the linear algebra and calculus required for machine learning, and you’ll save some time over other, more generic math courses.

How to Win a Data Science Competition — Coursera
One of the courses in the Advanced Machine Learning Specialization. Even if you’re not looking to participate in data science competitions, this is still an excellent course for bringing together everything you’ve learned up to this point. This is more of an advanced course that teaches you the intuition behind why you should pick certain ML algorithms, and even goes over many of the algorithms that have been winning competitions lately.

Bayesian Statistics: From Concept to Data Analysis — Coursera
Bayesian, as opposed to Frequentist, statistics is an important subject to learn for data science. Many of us learned Frequentist statistics in college without even knowing it, and this course does a great job comparing and contrasting the two to make it easier to understand the Bayesian approach to data analysis.

Spark and Python for Big Data with PySpark — Udemy
From the same instructor as the Python for Data Science and Machine Learning Bootcamp in the list above, this course teaches you how to leverage Spark and Python to perform data analysis and machine learning on an AWS cluster. The instructor makes this course really fun and engaging by giving you mock consulting projects to work on, then going through a complete walkthrough of the solution.

Learning Guide

How to actually learn data science

When joining any of these courses you should make the same commitment to learning as you would towards a college course. One goal for learning data science online is to maximize mental discomfort. It’s easy to get caught in the habit of signing in to watch a few videos and feel like you’re learning, but you’re not really learning much unless it hurts your brain.

Vik Paruchuri (from Dataquest) produced this helpful video on how to approach learning data science effectively:

Essentially, it comes down to doing what you’re learning, i.e., when you take a course and learn a skill, apply it to a real project immediately. Working through real-world projects that you are genuinely interested in helps solidify your understanding and provides you with proof that you know what you’re doing.

One of the most uncomfortable things about learning data science online is that you never really know when you’ve learned enough. Unlike in a formal school environment, when learning online, you don’t have many good barometers for success, like passing or failing tests or entire courses. Projects help remediate this by first showing you what you don’t know and then serving as a record of knowledge when it’s done.

Overall, the project should be the main focus, and courses and books should supplement that.

When I first started learning data science and machine learning, I began (as a lot do) by trying to predict stocks. I found courses, books, and papers that taught the things I wanted to know, and then I applied them to my project as I was learning. I learned so much in such a short period of time that it seems like an improbable feat if laid out as a curriculum.

It turned out to be extremely powerful working on something I was passionate about. It was easy to work hard and learn nonstop because predicting the market was something I really wanted to accomplish.

Essential knowledge and skills

Source: Udacity

There’s a base skill set and level of knowledge that all data scientists must possess, regardless of what industry they’re in. For hard skills, you not only need to be proficient with the mathematics of data science, but you also need the skills and intuition to understand data.

The Mathematics you should be comfortable with:

  • Algebra
  • Statistics (Frequentist and Bayesian)
  • Probability
  • Linear Algebra
  • Basic calculus
  • Optimization

Furthermore, these are the basic programming skills you should be comfortable with:

  • Python or R,
  • SQL
  • Extracting data from various sources, like SQL databases, JSON, CSV, XML, and text files
  • Cleaning and transforming unstructured, messy data
  • Effective Data visualization
  • Machine learning – Regression, Clustering, kNN, SVM, Trees and Forests, Ensembles, Naive Bayes

Lastly, it’s not all about the hard skills; many critical soft skills aren’t taught in courses. These are:

  • Curiosity and creativity
  • Communication skills – speaking and presenting in front of groups and explaining complex topics to non-technical team members.
  • Problem-solving – coming up with analytical solutions for business problems.

Python vs. R

After going through the list, you might have noticed that each course is dedicated to one language: Python or R. So which one should you learn?

Short answer: just learn Python, or learn both.

Python is an incredibly versatile language, and it has a huge amount of support in data science, machine learning, and statistics. Not only that, but you can also do things like build web apps, automate tasks, scrape the web, create GUIs, build a blockchain, and create games.

Because Python can do so many things, I think it should be the language you choose. Ultimately, it doesn’t matter that much which language you choose for data science since you’ll find many jobs looking for either. So why not pick the language that can do almost anything?

However, learning R is also very useful in the long run since many statistics/ML textbooks use R for examples and exercises. In fact, both books I mentioned at the beginning use R, and unless someone translates everything to Python and posts it to Github, you won’t get the full benefit of the book. Once you learn Python, you’ll be able to learn R pretty easily.

Check out this StackExchange answer for a great breakdown of how the two languages differ in machine learning.

Are certificates worth it?

One big difference between Udemy and other platforms—like edX, Coursera, and Metis—is that the latter platforms offer certificates upon completion and are usually taught by instructors from universities.

Some certificates, like those from edX and Metis, even carry continuing education credits. Other than that, many real benefits, like accessing graded homework and tests, are only accessible if you upgrade. If you need to stay motivated to complete the entire course, committing to a certificate also puts money on the line, so you’ll be less likely to quit. There’s definitely personal value in certificates, but, unfortunately, not many employers value them that much.

Coursera and edX vs. Udemy

Udemy does not currently have a way to offer certificates, so I generally find Udemy courses to be good for more applied learning material. In contrast, Coursera and edX are usually better for theory and foundational material.

Whenever I’m looking for a course about a specific tool, whether Spark, Hadoop, Postgres, or Flask web apps, I search Udemy first since the courses favor an actionable, applied approach. Conversely, when I need an intuitive understanding of a subject, like NLP, Deep Learning, or Bayesian Statistics, I’ll search edX and Coursera first.

Wrapping Up

Data science is a vast, interesting, and rewarding field to study and be a part of. You’ll need many skills, a wide range of knowledge, and a passion for data to become an effective data scientist that companies want to hire, and it’ll take longer than the hyped-up YouTube videos claim.

If you’re more interested in the machine learning side of data science, check out the Top 5 Machine Learning Courses for 2022 as a supplement to this article. Also, if you’re just starting with Python programming, check out Best Python Courses According to Data Analysis.

If you have any questions or suggestions, feel free to leave them in the comments below.

Thanks for reading, and have fun learning!

Top 20 Data Science Certifications & Courses Online in 2022

Looking to start a career as data scientist? One of these online data science courses and certifications will work best to put you on the right track to mastering data science.

January 3, 2022, 1:59 am16.9kViews

Data scientist is one of the hottest jobs in the IT industry today. Data trends from Glassdoor clearly indicate that it is the best job anyone can get. The world needs 5 million data science professionals this year. With the exponential amount of data being produced and captured, it is logical to say that the demand for data analytics is only going to increase. More and more companies are going to continue to hire data scientists to find meaningful insights and develop business strategies.

A certification in data science can go a long way to boost employability. Whether you are a student looking for a sparkling start to your career or a professional looking to expand your employment opportunities, data science courses will help you develop the necessary skills that the recruiters are looking for.

Here is our list of Best Data Science Certifications, Courses & Programs for 2022 from accredited institutions. These include both free resources and paid data science certificate programs that are delivered online, are widely recognised and have benefited thousands of students and professionals.

1. Professional Certificate in Data Science from Harvard University (edX)

Harvard Online Courses

This Online Data Science Certificate Program is offered by Harvard University through leading e-learning platform edX. It prepares you with key data science skills like R programming, machine learning and others using real world case studies to give you a jumpstart in roles of a data scientist.

This is a very reputable and intensive 2 to 4 months long self-paced program. It includes 9 graduate-level courses that are taught by Harvard’s Professor of Biostatistics Rafael Irizarry and offered entirely online at a fraction of cost of traditional college, making it very accessible, affordable and flexible. The 9 courses that make up this data science program include R Basics, Visualization, Probability, Inference and Modeling, Productivity Tools, Wrangling, Linear Regression, Machine Learning and a Capstone project. Thus the courses begin with basic fundamentals and progress to culminate with a Capstone project where you apply the skills and knowledge acquired throughout the course series to a real world problem. By the end of the program you learn how to independently work on a data analysis project.

Upon completion students receive a Professional Certificate in Data Science that they can highlight to their potential employers.

Key Highlights

  • Foundational R programming skills (a required skill in over 65% data science jobs)
  • Learn Statistical concepts such as probability, Statistical tools such as inference and modeling and how to apply them in practice
  • Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
  • Learn how to use R to implement linear regression
  • Become familiar with essential productivity tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
  • Implement machine learning algorithms
  • Learn fundamental data science concepts through motivating real-world case studies

Duration: 9 courses, 2 to 8 weeks per course, 102 to 184 hours of total effort
Rating: 4.9
Sign Up here

2. Data Science Specialization from Johns Hopkins University (Coursera)

Online Courses by Johns Hopkins University

This Data Science Specialization is a 10-course introduction to concepts and tools that you’ll need throughout the data science pipeline and is taught by renowned professors of Johns Hopkins University on Coursera platform. It aims to develop capability of learners to ask the right kind of questions, manipulate data sets, make inferences and create visualizations to publish results.

There are 10 courses in this certification program with a Capstone project at the end. These courses cover tools that data analysts and data scientists work with like version control, markdown, git, GitHub, and RStudio, R Programming, Getting and Cleaning Data, Exploratory Data Analysis techniques for summarizing data, Reproducible Research, Statistical Inference, Regression Models, Machine Learning, Developing Data Products. The Capstone Project will be drawn from real-world problems and conducted with government, industry or academic partners. It will give the students an opportunity to demonstrate their data science skills to potential employers.

Beginner level experience in Python and some familiarity with Regression are listed as requirements for this course.

Key Highlights

  • Use R to clean, analyze, and visualize data
  • Navigate the entire data science pipeline from data acquisition to publication
  • Use GitHub to manage data science projects
  • Perform regression analysis, least squares and inference using regression models
  • Balance both the theory and practice of applied mathematics to analyze and handle large-scale data sets
  • Create models using formal techniques and methodologies of abstraction that can be automated to solve real-world problems

Duration: 10 courses, 8 months, 5 hours per week
Rating: 4.5
Sign Up here

3. IBM Data Science Professional Certificate (Coursera)

IBM Online Courses

This Data Science Certification Program has been developed by IBM and delivered through Coursera platform to help students or professionals get ready to pursue roles in data science. You will learn concepts of data science and machine learning with thorough hands-on and practical learning.

This is one of the best Data Science Programs and comprises of 9 courses that cover following data science topics in detail – fundamentals of data science, open source tools and libraries, data science methodology, Python programming, working knowledge of databases and SQL, data analysis and visualization with Python, basics of machine learning followed by applied data science capstone project to help you consolidate your learning and apply skills learned to a real life project.

Each of the 9 courses typically contains 3 to 6 modules that need an average effort of 2-4 hours per module. A complete beginner would take up to 2-3 months to complete the program. Upon completion of this data science training, you are awarded a Certificate and IBM open badge (in fact 9 IBM badges for each of the 9 courses included in the program) that demonstrate efficiency in data science.

Key Highlights

  • Learn open source tools used in data science like Jupyter Notebooks, Zepplin, RStudio, and IBM Watson.
  • Learn the basics of Python, Pandas, and NumPy
  • Build databases, collect and analyze data from them using Python
  • Use Python libraries to generate data visualizations
  • Well designed content and all the topics are covered elaborately
  • Graded Assignments with Peer Feedback
  • Assignments and projects that provide practical skills with applicability to real jobs that employers value – random album generator, predict housing prices, best classifier model, battle of neighbourhoods
  • No prior programming or computer science knowledge is required

Duration: 9 courses, approx 2 months, 12 hours per week
Rating: 4.7
Sign Up here

4. MicroMasters Program in Statistics and Data Science from MIT (edX)

MIT Online Courses

This is a stand-alone Data Science and Statistics Certification program designed by the MIT Institute for Data, Systems, and Society (IDSS) and delivered by edX. The goal of this Micromasters data science program is to master the foundations of data science, statistics and machine learning.

It is one of the top data science programs and comprises of 4 intensive online courses followed by a virtually proctored online exam to earn a certificate. These graduate–level courses include Probability, Data Analysis in Social Science, Fundamentals of Statistics, Machine Learning with Python, Capstone Exam in Data Science and Statistics. The Probability course offered in this program is essentially same as the introduction to probability course taught on MIT campus and refined for 50 years. All the courses are taught by MIT faculty with high quality and hands-on learning approach. It is suggested that you have grasp of single and multi-variable calculus and linear algebra, as well as mathematical reasoning and Python programming to take up the program.

Each course in this MIT Data Science Certificate program runs for between 13 to 16 weeks and one is expected to spend approximately 12-14 hours per week on each course. Learners earn an individual Verified Certificate for each course that they pass and learners who pass the capstone exam at the end of the program receive a MicroMasters Program Credential.

Key Highlights

  • Learn Data analysis techniques, machine learning algorithms and apply them to real world data sets
  • In-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects
  • Learn to analyze big data and make data-driven predictions through probabilistic modeling and statistical inference and apply appropriate methodologies for extraction of meaningful information to aid decision making.
  • Develop and Build machine learning algorithms to make sense of the unstructured data and gain relevant insights into it.
  • Work on popular unsupervised learning methods such as clustering methodologies and supervised methods such as deep neural networks.
  • Learners who successfully complete this MIT MicroMasters credential can apply to the MIT Doctoral Program in Social and Engineering Systems (SES) offered through the MIT IDSS and have this coursework recognized for credit.
  • Learners can apply for various job titles after the completion of this certification such as Data Scientist, Data Analyst, Systems Analyst, Business Intelligence Analyst, Data Engineer etc.

Duration: 5 courses, 2 to 16 weeks per course, 12 to 14 hours per week
Rating: 4.6
Sign Up here

5. Applied Data Science with Python Specialization by University of Michigan (Coursera)

Online Courses by University of Michigan

This Coursera Data Science program has been developed by 4 professors of University of Michigan. It aims to enable learners with a basic understanding of programming to effectively manipulate and gain insight into data. It comprises of 5 courses that delve into data science methods, techniques and skills using Python programming language. It is expected that learners have a basic working knowledge of Python or at least other programming background. This program focuses on the application of statistical analysis, machine learning, information visualization, text analysis and social network analysis. It teaches popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into data. Specifically the 5 courses are – Introduction to Data science in Python, Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python and Applied Social Network Analysis in Python. Learners need to complete all five courses to earn the specialization certificate.

Key Highlights

  • Analyze the connectivity of a social network
  • Conduct an inferential statistical analysis
  • Learn Visualization basics with a focus on reporting and charting using the matplotlib library
  • Discern whether a data visualization is good or bad and Develop best practices for creating basic visualizations and charts
  • Enhance a data analysis with applied machine learning
  • Learn Applied data mining such as clustering and classification
  • Learn to take tabular data, clean it, manipulate it, and run basic inferential statistical analysis on it
  • Learn models of network generation and the link prediction problem

Duration: 5 courses, 5 months, 7 hours per week
Rating: 4.6
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6. Deep Learning Specialization (Coursera)

Online Courses by

Deep Learning and Machine Learning skills are highly in demand. If you want to master them and build a career in AI, this Deep Learning Specialization course by is your best bet. Andrew Ng (CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain), a very reputed and respected name in AI industry has developed this program along with 2 professors of Standard university. This is one of the most sought after programs on deep learning.

Delivered as five courses, this data science specialization program teaches foundations of Deep Learning, how to build neural networks, and how to lead successful machine learning projects. It is a bottom-up approach to learning neural networks — powerful non-linearity learning algorithms, at a beginner-mid level. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. The 5 courses are namely, Neural Networks and Deep Learning, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Structuring Machine Learning Projects, Convolutional Neural Networks and Sequence Models.

The course curriculum has been very carefully designed with neatly timed videos and has a well-regulated pace. You need to have a basic knowledge of mathematics and machine learning and some programming background to take the course. Some experience in Python is an added advantage as the course is delivered using Python language.

Key Highlights

  • Understanding of how neural networks work, along with How and Why We Make Them Deep
  • Learn to Be able to build, train and apply fully connected deep neural networks
  • Learn TensorFlow and variety of optimization algorithms
  • Work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing
  • Interviews and Personal stories of heroes and top leaders in Deep Learning

Duration: 5 courses, 3 months, 11 hours per week
Rating: 4.9
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7. Machine Learning Certification by Stanford University (Coursera)

Online Courses by Stanford University

This Machine Learning Certification Course has been developed by world renowned AI expert Andrew Ng and provides details into most effective machine learning techniques and their implementation in real world. You not only learn the theory of machine learning and statistical pattern recognition but also gain the practical knowledge to quickly and powerfully apply these techniques to solve new problems. This course is recognised as one of the best data science courses available online.

Following topics are covered in this course – supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. Learners should have a basic knowledge of computer science principles and be familiar with basic linear algebra and basic probability theory.

The data science machine learning course is highly involving with multiple videos in each lecture, followed by quizzes and assignments. It is approximated that one would need 11 weeks to take the course spending around 5-7 hours a week.

Key Highlights

  • Work with large datasets from various fields and in different formats
  • Understand parametric and non-parametric algorithms, clustering (k-Means algorithm), dimensionality reduction, anomaly detection among other important topics
  • Programming assignments designed to help understand how to implement the learning algorithms in practice
  • Learn about Silicon Valley’s best practices in innovation as it pertains to machine learning and AI
  • Numerous case studies and applications to learn how to apply machine learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas

Duration: 55 hours
Rating: 4.9
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8. Data Science MicroMasters Certification by University of California, San Diego (edX)

Online Courses by University of California, San Diego

This MicroMasters program is a series of graduate level courses in data science, designed by professors of University of California, San Diego and delivered online via edX. It is a very immersive program that can help to gain critical skills needed to advance as a data scientist. It aims to develop an in-depth understanding of the mathematical and computational tools that form the basis of data science and usage of those tools to make data-driven business decisions.

This UCSD Data Science certification program very effectively encompasses 2 sides of data learning – the mathematical and the applied in the form of 4 courses. These courses are – Python for Data Science, Probability and Statistics in Data Science using Python, Machine Learning Fundamentals and Big Data Analytics using Spark. Learners are introduced to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, they learn how to use:

  • Python
  • Jupyter notebook environment
  • Numpy
  • Matplotlib
  • Git
  • Pandas
  • Scipy
  • Apache Spark

At each stage of completing a course learners earn a verified certificate for the course. After completing all four program courses, they earn the MicroMasters Program Certificate.

Key Highlights

  • Learn to load and clean real world data
  • Learn to analyse big data using popular open source software to perform large-scale data analysis and present your findings in a convincing, visual way
  • Learn to make reliable statistical inferences from noisy data
  • Use machine learning to learn models for data
  • Visualize complex data using tools covered in the lectures
  • Use Apache Spark to analyze data that does not fit within the memory of a single computer
  • Work on practical assignments and projects to enhance your portfolio and apply the knowledge covered in the courses
  • Learn to build data science tools, explore public datasets, and discuss evidence-based findings

Duration: 4 courses, 10 to 15 weeks per course, 8 to 10 hours per week
Rating: 4.6
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9. The Data Science Course 2020: Complete Data Science Bootcamp (Udemy)

Udemy Online Courses

The Complete Data Science Bootcamp program from Udemy provides the entire toolbox you need to become a data scientist. It progressively takes you from basics of mathematics and statistics to advanced statistics, machine learning and tableau and more. This course includes 27 hours of on-demand video, 88 articles, 144 downloadable resources and full lifetime access.

This Udemy data science course is the one of the most effective, time-efficient, and structured data science training available online. It covers following topics in detail – Basics of Data science, Mathematics (Calculus and Linear Algebra), Statistics, Python programming with NumPy, Pandas, Matplotlib and Seaborn, Tableau, Advanced Statistical Analysis, and Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow. It includes wide variety of animations, quizzes, exercises and bonus materials. One does not need any prior experience to take up this course, everything is taught from the scratch with each topic building on the previous ones so you are prepped to work as a data scientist, handle real-life business cases and can take up more advanced specializations.

Key Highlights

  • Understand the mathematics behind Machine Learning
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Learn how to pre-process data
  • Carry out cluster and factor analysis
  • Unfold the power of deep neural networks
  • Apply your skills to real-life business cases

Duration: 27 hours on-demand video, 88 articles, 144 downloadable resources
Rating: 4.5
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10. Machine Learning A-Z™: Hands-On Python & R In Data Science (Udemy)

Online Courses on Udemy

Machine Learning is a very broad subject and becoming an expert in this field can be very challenging. This Data Science Machine Learning course on Udemy provides a clear pathway into the world of machine learning so participants can learn complex theory, algorithms and coding libraries in a simple and effective way. The course provides instruction in both Python and R programming languages, which is very distinguishing in itself. It has around 100,000 5-star ratings and more than 665,000 students enrolled making it the most popular Udemy Data Science course.

The course is very detailed and dives deep into all aspects of machine learning with over 44 hours of video content spread across 290 lectures. It covers Regression, Classification, Clustering, Association Rule Learning, Reinforcement Learning, Natural Language Processing, Deep Learning, Dimensionality Reduction. For each of these branches of machine learning, the course discusses between 2-7 different algorithms and shows how to create and code each one of them in Python and R. There are also takeaway templates included (in both Python and R) that students can download and use on their own projects. Students have the option of going with either Python or R (and skip the other) or try out both languages to truly master their machine learning skills.

The course takes an applied approach and is lighter math-wise. It is packed with practical exercises that are based on real-life examples, so apart from learning theory students get hands-on practice building their own models. There are quizzes and homework challenges too. Additionally students are expected to post solutions to exercises via Q&A or PM to allow discussion and feedback by instructors and fellow students.

The course has been created by two professional data scientists Kirill Eremenko and Hadelin de Ponteves, both of whom have years of real world data science experience under their belt. They bring both academic knowledge and real-life experience to the students and are known for their ability to make complex topics simple and easy to grasp.

Key Highlights

  • Master the entire Machine Learning workflow in Python & R
  • Learn an impressive number of powerful Machine Learning models and know how to combine them to solve any problem
  • Understand how to make accurate predictions and do detailed analysis
  • Know which Machine Learning model to choose for each type of problem
  • Get access to comprehensive Q&A section that addresses most of the commonly encountered issues
  • Course is constantly updated and new materials added

Duration : 44 hours on-demand video
Rating : 4.5
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11. Data Science Nanodegree Courses (Udacity)

Online Courses on Udacity

Udacity offers world-class Nanodegree programs in its School of Data Science. No matter what the skills and experience level of an individual, these programs offer a point of entry into the world of Data. Whether one wants to master data science programming with Python, R and SQL or become a data analyst or learn business analytics, there is a program on offer to build the relevant skills.

The Nanodegree programs in the Udacity’s School of Data Science are organized around three main roles: Business Analyst, Data Analyst and Data Scientist. They prepare learners for these roles based on their career goals, skills and experience levels.

Following are some of the Nanodegree courses in the Data Science field:

  • Programming for Data Science with R – Learn the programming fundamentals required for a career in data science – R, SQL, Command Line, and Git.
  • Data Scientist – Covers machine Learning & deep learning. Through projects designed by industry experts, students learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud.
  • Data Visualization – Covers data visualization, tableau, dashboards etc. Students learn to combine data, visuals, and narrative to tell impactful stories and make data-driven decisions.
  • Data Analyst – Covers Data Wrangling, Matplotlib, Bootstrapping, Pandas & NumPy, Statistics. Students learn to use Python, SQL, and statistics to uncover insights, communicate critical findings, and create data-driven solutions.
  • Programming for Data Science with Python – Covers Python, Numpy & Pandas, SQL, Git & GitHub
  • Data Engineer – Covers Data Modeling, Data Pipelines, Data Lakes, Spark, Airflow
  • SQL – Covers SQL, PostgreSQL, JOINs, Subqueries, Window Functions, Partitions, Data Cleaning, DDL, DML, Relational and Non-Relational Databases

Udacity has partnered with industry leaders like Tableau, Kaggle, and IBM Watson, to ensure these programs include in-demand skills that industry recruiters look for. They also provide personalized career services to the students like coaching sessions, interview prep advice, resume and profile review etc.

Key Highlights

  • Powerful Data Science programs to jumpstart your career
  • Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science
  • Benefit from personalized mentorship, real-world projects and expert instruction
  • Get Practical tips and knowhow of industry best practices

Duration : Self-Paced
Rating : 4.6
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12. Python for Data Science and Machine Learning Bootcamp (Udemy)

Online Courses on Udemy

This Python Data Science course on Udemy is taught by Jose Portilla and is amongst the most sought after courses in data science and machine learning fields. It has more than 365,000 students enrolled and enjoys very high positive ratings. It is a highly immersive course with over 25 hours of video content that takes students through a Python Crash course followed by data analysis and data visualization and machine learning algorithms. This course has the most in-depth coverage of popular Python Data Science libraries like NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn and more.

The course is structured very well. It starts with a crash course in Python (which acts a refresher on important syntaxes and topics) and then moves to data analysis and data visualization using Python libraries. Lastly the course covers how to use Python in Machine Learning. It uses Jupyter Notebooks for the code written and executed.

The course focuses a lot on the applied learning. Assignments and exercises on the Jupyter notebook workbooks is a huge plus point of this course. Every section has a custom exercise meant to help the student internalise the concepts taught in the section, which is followed by a full solution walkthrough of the exercise questions. There is also a Capstone Project and over a dozen fully implemented Machine Learning portfolio projects. Real world data set is provided to the students for the different machine learning algorithms. Also the students are provided with means to get more data sets to sharpen their skills via resources like Kaggle.

This course is targeted at beginner and intermediate Data Scientists and touches just about everything to some degree, from Python basics to NLP to deep learning. The participants are expected to have some programming experience, preferably in Python.

Key Highlights

  • Learn to use Python for Data Science and Machine Learning
  • Learn to use Spark for Big Data Analysis
  • Learn to implement Machine Learning Algorithms
  • Learn to use Pandas for data analysis, NumPy for numerical data, Seaborn for statistical plots, Matplotlib for python plotting, Plotly for interactive dynamic visualizations and SciKit-Learn for machine learning
  • Explore Natural Language Processing and Spam Filters
  • Access to online community Q&A forums with thousands of data science students
  • Over 150 HD video lectures and fully written out code and notebooks for reference

Duration : 25 hours on-demand video
Rating : 4.6
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13. Data Science A-Z™: Real-Life Data Science (Udemy)

Udemy Online Courses

This Data Science Course is very comprehensive and teaches data science step-by-step through real world analytics. A very good balance of theory, practice, real world business problems, take away templates and home work exercises make it one of the best courses on data science available online. Regardless of your prior experience with data science, it will help you realize your potential to become a data scientist. This course is taught by Kirill Eremenko who has created 63 courses on Udemy and has taught over 900,000 students and is certainly one of the best tutors in the business.

This course includes over 21 hours of on-demand video and is split into 4 main parts (with several lectures in each part) representing steps in data science journey:

  1. First part covers visualization and in particular how to conduct data mining in Tableau.
  2. Second part teaches theory of modelling from ground up. You receive step-by-step blueprint to creating a data model and learn to implement those steps to build a robust customer segmentation model. You also learn to access your models and will be provided with associated templates.
  3. Third part focuses on data preparation. With realistic exercise it prepares you for challenges of the real world. You learn to clean data sets and load them up in databases. You also learn foundations of SQL and how to leverage it for data science projects.
  4. Last part focuses on importance of communication in data science presentations including tips and tricks to effectively present your findings.

Key Highlights

  • Develop good understanding of data science tools – SQL, SSIS, Tableau and Gretl
  • Create Simple Linear Regression, Multiple Linear Regression, Logistic Regression
  • Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
  • Operate with False Positives and False Negatives and know the difference
  • Understand multicollinearity
  • Build the CAP curve in Excel and derive insights
  • Apply three levels of model maintenance to prevent model deterioration
  • Clean data and look for anomalies

Duration: 21 hours on-demand video, 4 articles
Rating: 4.5
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14. Google Data Analytics Professional Certificate (Coursera)

Online Courses by Google

This Data Analytics certificate program by Google on Coursera provides learners all the skills they need to find an entry-level job in the field of data analytics. They learn how to collect, transform, and organize data in order to help draw new insights, make predictions and drive informed business decisions. The program also covers the platforms and day-to-day tools used by a data analyst such as, Spreadsheets like Excel or Google Sheets, SQL for data extraction, Tableau for data visualization, R programming, RStudio, and R packages including the Tidyverse package.

The curriculum of this data analytics certification program has been developed by subject-matter experts and senior practitioners at Google, along with input from top employers and industry leaders, like Tableau, Accenture, and Deloitte. It is a very practical program where learners are introduced to the world of data analytics through a series of 7 courses and an optional Capstone project. Following topics are covered in the courses:

  • Overview of data analysis process
  • Data types, formats and structures
  • Using data to solve problems
  • How to collect data for analysis
  • how to access databases and extract, filter, and sort the data they contain
  • Cleaning and transforming data
  • How to analyze data
  • Data storytelling with visualizations
  • Using R programming to supercharge your analysis

Apart from video lessons, the program includes a plethora of hands-on activities, assessments, quizzes and assignments. Capstone project provides opportunity to complete a case study that you can share with potential employers to showcase your new skill set. Those who complete the certificate program will have access to career resources and be connected directly with Google and over 130 partner employers hiring for open entry-level roles in data analytics.

Key Highlights

  • Learn the basics of being a data analyst, including the tools needed to master the day-to-day of an analyst
  • Learn the best practices for organizing data and keeping it secure
  • Explore the fundamental concepts associated with programming in R
  • Practice-based assessments that simulate real-world data analytics scenarios
  • Improve your interview technique and resume with access to Google career resources
  • Learn at a pace and schedule right for you

Duration : 6 months, 10 hours per week
Rating : 4.7
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15. Data Science: Foundations using R Specialization by Johns Hopkins (Coursera)

Online Courses by Johns Hopkins University

This foundational Data Science program is offered by Johns Hopkins University and is taught by 3 eminent professors Jeff Leek, Roger D Peng and Brian Caffo of the Johns Hopkins Bloomberg School of Public Health. It covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research.

The specialization has 5 courses that clock in around 70 hours of video content. These five courses are the same courses that make up the first half of the Johns Hopkins’ Data Science Specialization. It is intended for students who don’t have much prior experience but are looking to get started in Data Science and want to complete the foundational part of the subject first before progressing to the more advanced topics.

Following are the 5 courses that comprise this Data Science specialization:

  1. The Data Scientist’s Toolbox – Provides an overview of the data, questions, and tools that data analysts and data scientists work with like version control, markdown, git, GitHub, R, and RStudio
  2. R Programming – Discusses how to program in R and how to use R for effective data analysis
  3. Getting and Cleaning Data – Covers the basics needed for collecting, cleaning, and sharing data
  4. Exploratory Data Analysis – Covers the essential exploratory techniques for summarizing data
  5. Reproducible Research – Covers the concepts and tools behind reporting modern data analyses in a reproducible manner

Each course contains several working examples and culminates in a project that involves implementing the concepts and skills covered in the course like installing tools, programming in R, cleaning data, performing analyses, as well as peer review assignments.

Key Highlights

  • Gain foundational knowledge and prepare to study advanced topics of Data Science and Machine Learning
  • Best fit for students or professionals with minimal experience looking to enter the field of Data Science
  • Learn how to read data into R, access R packages, write R functions, debug, profile R code, and organize R code
  • Explore the plotting systems in R as well as some of the basic principles of constructing data graphics
  • Learn common multivariate statistical techniques used to visualize high-dimensional data
  • Learn about the core tools for developing reproducible documents

Duration : 5 months, 8 hours per week
Rating : 4.6
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16. Introduction to Data Science Specialization by IBM (Coursera)

Online Courses by IBM

This is a very well-rounded foundational course in Data Science designed by IBM and offered on Coursera platform. It is intended for learners with little or no prior experience who wish to make a career in data science and prepare them for further advanced learning in this field.

This introductory Data Science program consists of 4 courses that build foundational data science skills. It starts with an understanding of what Data Science is and the various kinds of activities that a Data Scientist performs. Then it moves to some of the most popular data science tools, their features, and how to use them (like Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience). After that it teaches students about methodology involved in tackling data science problems. The specialization also introduces students to relational database concepts and the use of SQL to query databases.

Several projects and hands-on labs are included to allow students to practice and test the concepts taught in the courses. They are provided real-world data sets and several exercises that require querying these data sets using SQL from Jupyter notebooks.

After completing all the 4 courses and projects in the specialization, learners receive an IBM Badge as a Specialist in Data Science Foundations along with a certificate of completion.

Key Highlights

  • Best fit for learners wanting to build foundational skills in Data Science
  • Explore various open source tools used by Data Scientists, like Jupyter notebooks, Zeppelin, R Studio and Watson Studio
  • Create and access a database instance on cloud
  • Learn advanced SQL concepts like filter, sort, group results, use built-in functions, access multiple tables
  • Work with real databases, real data science tools and real-world datasets
  • Learn to access databases from Jupyter using Python

Duration : 3-4 months, 3-4 hours per week
Rating : 4.6
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17. Machine Learning Specialization by University of Washington (Coursera)

Online Courses by University of Washington

This Specialization in Machine Learning has been created by the leading researchers at the University of Washington for scientists and software developers who want to expand their skills into data science and machine learning. There are 4 courses in this program that delve into major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. Students learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

The instructors Emily Fox and Carlos Guestrin are both Amazon Professors of Machine Learning at University of Washington. They explain the concepts clearly and follow them with a worked out example for a better grasp.

This specialization is a good blend of theoretical and practical. Every module has conceptual quizzes as well as one or two Jupyter Notebook assignments. The quizzes and exercises do an excellent job of reinforcing the concepts from the video instruction. The application assignments offer good insights into the common data science problems.

The program assumes some knowledge of Python and data structures as most assignments use Python. But pretty much anyone with knowledge of basic math and some experience in computer programming can take up this specialization to learn the fundamental concepts of machine learning and how to derive intelligence from data.

Key Highlights

  • Learn to use machine learning techniques to solve complex real-world problems
  • Series of practical case studies to gain hands-on experience with machine learning
  • Learn to build an end-to-end application that uses machine learning at its core
  • Learn to apply regression, classification, clustering, retrieval, recommender systems, and deep learning
  • Includes lectures dedicated to working with Graphlab Create library
  • Learn to assess and improve an algorithm’s performance
  • Real world datasets are used for machine learning algorithms throughout each course

Duration : 7 months, 3 hours per week
Rating : 4.7
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18. SQL for Data Science by UC Davis (Coursera)

Online Courses by UC Davis

Being able to retrieve and work with data is a very important skill for a good Data Scientist. This means one has to be well versed in SQL, which is the standard language for communicating with database systems. This course created by University of California, Davis and hosted on the Coursera platform, aims to give learners a primer in the fundamentals of SQL and working with data so that they can begin analyzing it for data science purposes.

This course is for beginners and does not assume any prior knowledge of SQL. It therefore starts with the basics and gradually builds on that foundation. In no time students are able to write both simple and complex SQL queries to select data from database. The following topics are covered:

  • Differences between one-to-one, one-to-many, and many-to-many relationships within databases
  • Different types of data like strings and numbers
  • Create new tables and move data into them
  • Common SQL operators and how to combine the data
  • Basic math operators, as well as aggregate functions like AVERAGE, COUNT, MAX, MIN, and others that are used to analyse the data
  • Subqueries and Joins in SQL
  • Methods to filter and pare down query results
  • Case statements and concepts like data governance and profiling

Apart from pre-recorded video lectures, there are auto-graded and peer-reviewed assignments. Students also get access to community discussion forums. The course is self-paced and designed to teach one SQL skills fast.

Key Highlights

  • Learn to interpret the structure, meaning, and relationships in source data and use SQL as a professional to shape the data for targeted analysis purposes
  • Learn tips and tricks to apply SQL in a data science context
  • Learn to use SQL commands to filter, sort, and summarize data
  • Practice using real-world programming assignments

Duration : Approx. 14 hours
Rating : 4.6
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19. Mathematics for Machine Learning Specialization by Imperial College London (Coursera)

Online Courses by Imperial College London

Mathematics is one of the most important foundational blocks of Machine Learning. A good base in mathematics helps learners to understand better the concepts underlying various algorithms and APIs. Most often than not, working professionals lose touch with basic mathematical concepts and therefore struggle to relate to how they’re used in Data Science. This specialization offered by Imperial College of London aims to bridge that gap and get learners up to speed in the underlying mathematics. It builds an intuitive understanding of mathematical concepts, and how they relate to Machine Learning and Data Science, thus preparing learners for several higher level courses in Machine Learning and Data Science.

There are 3 courses in this specialization –

  1. Linear Algebra – It discusses linear algebra and how it relates to vectors and matrices. It also looks at what vectors and matrices are and how to work with them and how to use them to solve problems
  2. Multivariate Calculus – It is an introduction to the multivariate calculus required to build many common machine learning techniques.
  3. Dimensionality Reduction with Principal Component Analysis – It introduces the mathematical foundations to derive PCA, a fundamental dimensionality reduction technique. This course is of intermediate difficulty and requires some Python and numpy knowledge.

There are exercises and quizzes in each course that give you more insight in the concepts learnt and help to solidify the learning. As part of assignments of this specialisation, students are required to produce mini-projects with Python on interactive notebooks. This helps them to apply the skills learnt to real world problems. Since this program is aimed at maths underlying data driven applications, one can get a lot of working knowledge out of it.

Key Highlights

  • Gain the prerequisite mathematical knowledge to take more advanced courses in machine learning
  • Implement mathematical concepts using real-world data
  • Understand important mathematical concepts to be able to implement PCA all by yourself
  • Learn how calculus is applied in linear regression models and in the training of neural networks
  • Understand how orthogonal projections work
  • Derive PCA from a projection perspective

Duration : 4 months, 4 hours per week
Rating : 4.5
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20. Microsoft Professional Program in Data Science (edX)

Microsoft Online Courses

The Microsoft Professional Program in Data Science has been developed by Microsoft in collaboration with leading universities and employers and is available on online learning platform edX. In this program you will learn data science fundamentals, key tools and programming languages from industry experts.

This Microsoft data science certification comprises of 3 units and a final capstone project taught over 10 courses. Learners need to complete all 10 courses and achieve a 70% pass rate to earn MPP (Microsoft Professional Program) Data Science certificate. Some courses give learners the option to choose between different technologies. For example in Unit 1 (Fundamentals), you could choose between Analyzing and Visualizing Data with Excel or with Power BI. Similarly in Unit 3 (Applied Data Science), one has a choice between learning R or Python for programming course. Though you could take both courses, only one must be completed to satisfy the requirements for graduation.

The program courses include Analyzing and Visualizing Data, Querying Data with Transact-SQL, ethics and Law in Data & Analytics, Creating data models using MS Excel or Power BI, Machine Learning with R or Python,Data Science Research Methods, Developing Big Data Solutions with Azure Machine Learning, Implementing Predictive Analytics with Spark in Azure HDInsight, Applying statistical methods to data. In all courses equal emphasis is placed on theoretical and practical lessons.

Key Highlights

  • Use Microsoft Excel to explore data
  • Use Transact-SQL to query a relational database
  • Create data models and visualize data using Excel or Power BI
  • Apply statistical methods to data
  • Use R or Python to explore and transform data
  • Follow a data science methodology
  • Create and validate machine learning models with Azure Machine Learning
  • Create an Azure SQL Server Database
  • Write R or Python code to build machine learning models
  • Apply data science techniques to common scenarios
  • Implement a machine learning solution for a given data problem

Duration: 10 courses + Final Capstone Project, 16 to 32 hours per course
Rating: 4.5
Sign Up here

More Data Science Courses Online

21. Online Data Science Masters Degrees (Coursera)

Online Courses on Coursera

Coursera hosts a brilliant option of Online Master’s Degree in Data Science on its platform. These degree programs are offered by the top global data science schools and are taught by the same professors that teach degree courses on campus. An important upside being that these online degrees cost less than half the cost of their on-campus counterparts.

An online Data Science Master’s degree is a specially designed graduate program that combines core concepts from mathematics, computer science, statistics, and information science to leverage insights and help data scientists improve operational and business processes. It is a great fit for professionals who are interested in furthering their data science career, or interested in building or expanding skills in machine learning, cluster analysis, databases, data visualization, statistics, data mining and more.

Some of the degree programs one can opt for are:

  • Master of Applied Data Science
    By University of Michigan
  • Master of Computer Science in Data Science
    By University of Illinois at Urbana-Champaign
  • MSc in Machine Learning
    By Imperial College London
  • Master of Science in Data Science
    By University of Colorado Boulder
  • Master of Data Science
    By National Research University Higher School of Economics

These Data Science degrees on Coursera include applied projects that use same programming environments that data scientists use professionally every day, thus students are better prepared to take on problems in the real world. Most of these degree programs can be completed in as little as two to three years time and participants can continue their job while pursuing them instead of having to take time off from their job.

Key Highlights

  • Designed for aspiring data scientists to learn and apply skills through hands-on projects
  • Content developed by world-class faculty at top-ranked universities of the world
  • Focussed on applied real-world learning
  • Programs led by the same top-ranked professors that lecture on campus
  • Hands-on learning approach with excellent peer-to-peer support
  • Work with real data sets from top companies and build a work portfolio that showcases your skills
  • Complete flexibility to pursue your data science education on your own time

Duration : Self-Paced
Rating : 4.6
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22. Data Scientist Career Path for Beginners (Codecademy)

Codecademy Online Courses

This Data Science program from Codecademy helps you master the skills you need to become a data scientist. You will learn to analyse data with SQL and Python and build machine learning algorithms. You will also learn NumPy, pandas, matplotlib, scikit-learn and more. No prior experience in data science is needed to take up this course.

Beginners are welcome to enrol in the program as everything is taught from scratch. This Codecademy Data Scientist course is comprised of 13 lessons that are estimated to take 35 weeks of small part-time effort for a beginner.

Key Highlights

  • Learn SQL to talk to databases and manipulate tables
  • Learn key statistics and analysis techniques
  • Use Python for statistical analysis and create data visualizations to see the big picture
  • Discover how to use supervised learning techniques, in which algorithms learn from many examples of past outcomes
  • Learn how to perform learning on a dataset when we don’t have any of the answers to begin with
  • How to create charts and graphs to illustrate your findings
  • Learn Data Visualization on real world datasets
  • Practice Sublime Lime’s line graphs

Duration: 35 weeks
Rating: 4.5
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23. Free Coursera Data Science Courses (Coursera)

Online Courses on Coursera

Coursera offers data science courses, specializations, professional certificate programs, and online degrees from top universities and data science schools all over the world. It has also partnered with industry leaders like IBM to offer programs that impart required data science and machine learning skills to prepare learners for real world. Their catalogue includes a wide range of popular online courses in subjects ranging from foundations like Python and R programming to advanced deep learning and artificial intelligence applications.

Some top choices include courses and specializations in Data Science from Johns Hopkins University, Introduction to Data Science, Applied Data Science and Applied AI by IBM, Machine Learning by Stanford University, Deep Learning by Andrew Ng, Applied Data Science with Python and Python Data Structures by University of Michigan, Business Analytics by University of Pennsylvania, Duke University’s Excel to MySQL: Analytic Techniques for Business and Excel Skills for Business by Macquarie University.

Coursera also offers data science degrees from top-ranked colleges like University of Illinois, Imperial College London, University of Michigan, University of Colorado Boulder, and National Research University Higher School of Economics.

Key Highlights

  • Several options to learn important skills in Data Science like Python programming, R programming, Data Visualization, Analytics, Statistics, Big Data etc.
  • Opportunities to collaborate with other learners from all around the world
  • Courses from the world’s best instructors and universities
  • Courses include recorded auto-graded and peer-reviewed assignments, video lectures, and community discussion forums
  • Capstone projects to showcase your expertise to potential employers
  • Flexible learning at your own pace
  • Certificate of Completion upon completing the lectures and hands-on projects
  • Audit the courses for free

Duration : Self-Paced
Rating : 4.6
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24. Free edX Data Science Courses (edX)

Online Courses on edX

edX offers a wide variety of Data Science programs that can help to accelerate your learning in this field. Their library includes more than 200 professional certificates, micromasters programs, master’s degree programs and individual courses from top-ranked colleges and universities in the world. There are courses for all branches of data science like Machine Learning, Python programming, R programming, SQL, Data Analysis, Excel and Business Analytics, Probability and Statistics etc.

There are programs for complete beginners as well as those for advanced level users. For those who have a background in statistics and computer science and want to expand their skills in data science can choose from following notable options:

  • MicroMasters in Statistics and Data Science by MIT
  • Professional Certificate in Data Science by Harvard
  • Professional Certificate in Data Science by IBM
  • Data Science Fundamentals by Microsoft
  • MicroMasters in Data Science by University of California, San Diego
  • MicroMasters in Analytics:Essential Tools and Methods by Georgia Tech

There are several introductory courses in Python and R for data science as well as foundational courses in probability and statistics. One can even opt for Master’s Degree in Analytics from Georgia Tech.

Key Highlights

  • Real college courses from Harvard, MIT, and more of the world’s leading schools and universities
  • Beginner, intermediate and advanced data science programs to match individual current and goal skill levels
  • Learn from assignments and hands-on projects created to reflect real world challenges
  • Option to audit the courses for free and add a verified certificate at a small fee
  • Flexible learning at one’s own pace and comfort

Duration : Self-Paced
Rating : 4.6
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25. Udemy Data Science Courses (Udemy)

Online Courses on Udemy

Udemy offers highly-rated data science certification courses created by industry experts and professionals. Whatever be your experience or skill level, you can find a course that suits your requirements from the vast catalogue of data science courses on Udemy. There are courses for every topic and aspect of data science from machine learning to data analysis and visualization to python programming, R programming, artificial intelligence and statistics.

Some of the most popular courses include:

  • Machine Learning A-Z™: Hands-On Python & R In Data Science
    By Kirill Eremenko, Hadelin de Ponteves
  • Python for Data Science and Machine Learning Bootcamp
    By Jose Portilla
  • The Data Science Course 2020: Complete Data Science Bootcamp
    By 365 Careers
  • R Programming A-Z™: R For Data Science With Real Exercises
    By Kirill Eremenko
  • Machine Learning, Data Science and Deep Learning with Python
    By Frank Kane
  • Statistics for Data Science and Business Analysis
    By 365 Careers
  • Data Science and Machine Learning Bootcamp with R
    By Jose Portilla
  • SQL & Database Design A-Z™: Learn MS SQL Server + PostgreSQL
    By Kirill Eremenko, Ilya Eremenko
  • Data Science Career Guide – Interview Preparation
    By Jose Portilla

Udemy also offers many free data science courses on various topics that are worth considering.

Key Highlights

  • Learn from top instructors and experts in the Data Science domain
  • Choose from variety of courses that cover every aspect and branch of Data Science like machine learning, data analysis, data mining, deep learning and more
  • Get lifetime access to all course materials and any future updates
  • Courses include video lessons, quizzes, exercises, downloadable resources and supplemental material
  • All courses come with a 30 day Udemy money back guarantee

Duration : Self-Paced
Rating : 4.5
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