Top Data Science Online Courses in 2017

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Data Science Online Courses: A Comprehensive List for 2017

The following is an extensive list of Data Science courses and resources that give you the skills needed to become a data scientist.

Choose a full specialization or course series, like those from Coursera, edX, and Udacity, or learn individual topics, like machine learning, deep learning, artificial intelligence, data mining, data analytics, data cleaning, data visualization, web scraping, and much more with standalone courses, like those from Udemy (for a dedicated post on Udemy courses, check out Top Udemy Data Science Courses).

To accelerate your learning even more, be sure to supplement these courses by reading one of the many books on our giant list of the best, free data science books. There should be something in there for every subject!

We would like you to know that some of the links to courses here are affiliate links. By going through us to gain access to a course, LearnDataSci may receive a commission. Thank you in advance to anyone that purchases a course from here, we greatly appreciate the support.





Being able to pay for each course as you go or all at once makes Coursera's specializations very attractive. Whether you're unsure about data science and just want to audit a course for free, or you're looking to purchase the specialization certificate for your CV and LinkedIn, Coursera's paths are great for getting totally new learners off the ground.

The one big benefit of buying the certificate is that it gives you access to their graded materials and student forums, which are extremely helpful with the more complex subject matter. If you have a question about a lecture, or if you're stuck on homework and need a hint, a lot of the time it's been covered in the forums. Also, you'll be less likely to abandon your progress if there's money on the line!

Each specialization contains a handful of courses, and usually a project (or capstone) at the end to sum up the course. As of this writing, you aren't able to enroll in a capstone project without taking the specialization, but every other course is available individually through their catalog. I have provided a link to each individual course below as well, so if something sounds interesting, just hop in!


Data Science Specialization

The Coursera Data Science Specialization will give you a fundamental understanding of data science with the R programming language.

It's recommended that you have some programming experience (doesn't have to be R) and that you have a good understanding of Algebra. Previous knowledge of Linear Algebra and/or Calculus isn't necessary.

Institution: Johns Hopkins University
Instructors: Brian Caffo, Jeff Leek, Roger D. Peng
Price: Free, or $49/month for specialization certificate
Courses Included in Specialization:

  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


Machine Learning Specialization

In Coursera's Machine Learning Specialization, you'll gain the ability to utilize machine learning techniques on real-world problems.

You'll find out how to pick the best method for your task, apply algorithms, optimize the algorithms, and deploy your solution.

Python is the preferred language of choice in these courses. Since this specialization is geared towards Scientists and Software Developers wanting to branch into data science, you're expected to have programming experience and maths skills (basic calculus and linear algebra) already.

One thing to keep in mind about this specialization before you start is that the courses use GraphLab Create and SFrames for ML and data manipulation instead of the more popular scikit-learn and Pandas libraries. GraphLab Create is free for one year for educational use, but if you ever want to use it in a commercial application, you'll have to buy a license.

Institution: University of Washington
Instructors: Emily Fox, Carlos Guestrin
Price: Free, or $79/month for specialization certificate
Courses Included in Specialization:

  1. Machine Learning Foundations: A Case Study Approach
  2. Regression
  3. Classification
  4. Clustering & Retrieval


Applied Data Science with Python Specialization

This specialization focuses primarily on working with machine learning through Python, and it gives a strong introduction to commonly used ML toolkits, like matplotlib, pandas, nltk, scikit-learn, and networkx.

To take these courses, you'll need to already be familiar with Python or programming in general. There are some great lectures in the first course dealing with some of the more advanced Python features you'll need to process data effectively.

Institution: University of Michigan
Instructors: Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Price: Free, or $79/month for specialization certificate
Courses Included in Specialization:

  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


Statistics with R Specialization

If you've wanted to dive into data analysis, but are a little fuzzy on the statistics required, then this set of courses are an excellent place to start. Just basic math is recommended, and they'll be guiding you through using R, so no programming experience is needed.

You'll be learning how use statistical inference, modeling, and visualization techniques in R to create analysis reports. By the end, you'll have a few portfolio projects to showcase your new data analytics skills.

Institution: Duke University
Instructors: Merlise Clyde, Colin Rundel, David Banks, Mine Çetinkaya-Rundel
Price: Free, or $49/month for specialization certificate
Courses Included in Specialization:

  1. Introduction to Probability and Data
  2. Inferential Statistics
  3. Linear Regression and Modelings
  4. Bayesian Statistics
  5. Statistics with R Capstone


Recommender Systems Specialization

Recommender Systems used to be a course in the Machine Learning Specialization, but now it's broken off into its own master series. You're going to learn both how to apply the most popular recommender algorithms and also what metrics to look at when deciding which algorithm to use.

If you have some basic statistics and college algebra under your belt, you'll be good to go for this program. If you want to do the honors track, though, you'll need to know Java.

Institution: University of Minnesota
Instructors: Joseph A Konstan, Michael D. Ekstrand
Price: Free, or $79/month for specialization certificate
Courses Included in Specialization:

  1. Introduction to Recommender Systems: Non-Personalized and Content-Based
  2. Nearest Neighbor Collaborative Filtering
  3. Recommender Systems: Evaluation and Metrics
  4. Matrix Factorization and Advanced Techniques
  5. Recommender Systems Capstone


Genomic Data Science Specialization

There's not much really required for this specialization, but it's helpful to have some statistics, biology, and/or computer science experience.

You'll be using both Python and R in this series, and by the end you'll be able to interpret and analyze data from “next generation sequencing experiments.

Institution: Johns Hopkins University
Instructors: Liliana Florea, Kasper Daniel Hansen, Ben Langmead, Jeff Leek, Mihaela Pertea, Steven Salzberg, James Taylor
Price: Free, or $49/month for specialization certificate
Courses Included in Specialization:

  1. Introduction to Genomic Technologies
  2. Genomic Data Science with Galaxy
  3. Python for Genomic Data Science
  4. Command Line Tools for Genomic Data Science
  5. Algorithms for DNA Sequencing
  6. Bioconductor for Genomic Data Science
  7. Statistics for Genomic Data Science
  8. Genomic Data Science Capstone


Big Data Specialization

The Big Data Specialization will show you how to process, analyze, and interpret large amounts of complex data using the most recent big data tech, such as Hadoop and Spark.

There's no programming experience needed here, just a passion for data!

Institution: UC San Diego
Instructors: Paul Rodriguez, Amarnath Gupta, Andrea Zonca, Mahidhar Tatineni, Natasha Balac
Price: Free, or $59/month for specialization certificate
Courses Included in Specialization:

  1. Introduction to Big Data
  2. Big Data Modeling and Management Systems
  3. Big Data Integration and Processing
  4. Machine Learning with Big Data
  5. Graph Analytics for Big Data
  6. Big Data Capstone


Data Mining Specialization

By the end of the Data Mining Specialization, you will be able to recognize data patterns, retrieve and visualize data, and use/apply algorithms to structured and unstructured data.

Knowledge of more than one programming language and basic Statistics/Probability is highly recommended.

Institution: University of Illinois at Urbana-Champaign
Instructors: Jiawei Han, John C. Hart, ChengXiang Zhai
Price: Free, or $79/month for specialization certificate
Courses Included in Specialization:

  1. Data Visualization
  2. Text Retrieval and Search Engines
  3. Text Mining and Analytics
  4. Pattern Discovery in Data Mining
  5. Cluster Analysis in Data Mining
  6. Data Mining Capstone


Data Analysis and Interpretation Specialization

This specialization focuses on bringing complete data beginners from knowing almost nothing to being able to answer questions about datasets using machine learning and predictive algorithms. The courses here are project-based, so by the end you'll have a few example data solutions under your belt that you can showcase in your portfolio.

The really interesting part about this series is that there are two paths you can take: data analysis with SAS, or data analysis with Python. You can pick whichever you're more interested in learning, and they will provide the necessary lectures and material for each.

There's no background required to start this specialization, but linear algebra and programming experience would be helpful.

Institution: Wesleyan University
Instructors: Lisa Dierker, Jen Rose
Price: Free, or $79/month for specialization certificate
Courses Included in Specialization:

  1. Data Management and Visualization
  2. Data Analysis Tools
  3. Regression Modeling in Practice
  4. Machine Learning for Data Analysis
  5. Data Analysis and Interpretation Capstone


Other Coursera Data Science Courses:




At only $29 per month for full access to all courses, Datacamp is an extremely affordable way to get started with Data Science in both Python and R.

DataCamp offers Career Tracks, which develops and organizes courses based on what each career requires. They also put together Skill Tracks if you are more interested in just a specific skill. Each track has hours upon hours of video content that you'll get complete access to for one, fairly small monthly membership fee.

I've listed all of the major career tracks below, but if you wish to see all of the available tracks, go here.

Price: Free trial then $29/month

Career Tracks

Quantitative Analyst with R

This is a cool career track because you'll get to learn how to apply R to financial data. Just like in the data scientist with R course, you'll get the same great intro to intermediate R courses, but then it starts branching off into financial-specific topics, like bond and portfolio analysis, credit risk modeling, and even signal-based trading with R and quantstrat.

12 Courses (51 hours)

  1. Introduction to R for Finance
  2. Intermediate R for Finance (Coming soon)
  3. Manipulating Time Series Data in R with xts & zoo
  4. Importing and Managing Financial Data in R
  5. Introduction to Time Series Analysis
  6. ARIMA Modeling with R
  7. Manipulating Time Series Data in R: Case Studies
  8. Introduction to Portfolio Analysis in R
  9. Intermediate Portfolio Analysis in R
  10. Bond Valuation and Analysis in R
  11. Credit Risk Modeling in R
  12. Financial Trading in R




Springboard is unlike many of the other platforms in this list. One of the biggest differences is that Springboard offers 1-on-1 mentorship each week from industry experts that hold (or held) data scientist positions at companies like Uber and Facebook.

Springboard offers two types of programs: Career Tracks and Workshops.

Data Science Career Track

The career track is an all-in-one bootcamp with one goal in mind: to get you a job as a data scientist.

With the career track, you get a job guarantee, so if you don't get a qualifying job offer within 6-months of graduation, your tuition is refunded. How many universities offer that kind of guarantee?

You're also getting personalized career coaching, interview prep, twice the amount of curriculum when compared to their workshops, and access to special employer partnerships.

To enter the data science career track, you'll need to have college-level statistics and some programming experience. Go here to read my full review of Springboard's Data Science Career Track.

Price: $1000/month for 6 months, or $4800 one-time

  • Programming Tools (Python) – iPython Notebooks, Matplotlib, Pandas, Git/Github
  • Data Wrangling – Pandas deep dive, Data files and Databases
  • Data Story
  • Inferential Statistics – Theory and application, Correlation and Regression, A/B Testing
  • Machine Learning – Scitkit-learn, Supervised/unsupervised learning, Naive Bayes, SVM, Decision Trees, Clustering, Recommender Systems and more
  • Advanced Data Visualization – D3.js, Seaborn, Bokeh, Plotly
  • Big Data – MapReduce, Spark, MLib, NoSQL
  • Capstone Project
  • Career Resources – Job search strategies, how to build a network and land interviews, interview coaching with mock interviews, negotiation tips

Foundations of Data Science Workshop

This workshop is built for those that may not have the skills (stats and programming) needed for the career track above, but still want to begin their journey.

In Foundations of Data Science workshop, you'll be using the R language, and you'll be following a similar curriculum layout as the career track.

This biggest differences between this workshop and the career track is that you will not have a job guarantee, which is great for those that are just looking to learn data science for fun and have no intention of getting a job.

You'll still be getting 1-on-1 mentor calls and project reviews each week for as long as you're enrolled.

Price: $499/month

  • Programming in R
  • Data Wrangling – dplyr, tidyr, split-apply-combine paradigm, regex
  • Probability & Statistics – Random variables and Distributions, Regression, Hypothesis testing
  • Exploratory Data Analysis – EDA vs Classical Bayesian, all kinds of plots
  • Data Story
  • Data Analysis in Depth – Feature engineering, Linear/Logistic Regression, Clustering, Model Evaluation, Cross-Validation, Trees, Text Analytics
  • Data Visualization (elective) – Advanced viz techniques, like index charts, horizon graphs, parallel coordinates, and more
  • Capstone Project
  • Career Resource – Interview resources, building data products, portfolio advice





Like Coursera, edX also has courses bundled together to form a knowledge set, called Xseries. You can take these courses for free, or purchase verified certificates to complete the bundled track.

Data Science and Analytics in Context

Institution: Columbia University
Instructors: 21 different instructors across the series
Price:Free, or $347 for certificate
Courses Included in this XSeries:

  1. Statistical Thinking for Data Science and Analytics
  2. Machine Learning for Data Science and Analytics
  3. Enabling Technologies for Data Science and Analytics: The Internet of Things


Data Science and Engineering with Apache® Spark™

Institution: UC Berkeley
Instructors: Anthony D. Joseph, Ameet Talwalker, Jon Bates
Price:Free, or $197 for certificate
Courses Included in this XSeries:

  1. Introduction to Apache Spark
  2. Big Data Analysis with Apache Spark
  3. Distributed Machine Learning with Apache Spark


Professional Certificates

So far, there's only one Professional Certificate that pertains to data science, but since it has a capstone, it makes it more like Coursera's specializations than the XSeries. The one defining difference between the Pro Certs and the XSeries is the length and breadth of the material. Here, there's more courses and information for aspiring data scientists to get a taste of the field.

Microsoft Professional Program Certificate in Data Science

The Microsoft Professional Program contains more courses than any other track from edX or Coursera currently. They give a choice of track whether you would rather use R or Python by providing separate courses for each.

In addition, you'll be learning all of the fundamentals, from data exploration in Excel, to SQL databases, to Azure Machine Learning with Spark.

Institution: Microsoft, Columbia University
Instructors: A lot!
Price:Free, or $49-$99/course for certificate
Courses Included in the certificate:


Other edX Data Science Courses:





Udacity only has one track, or what they call a Nanodegree, that is relevant to Data Science, and that's the Data Analyst Nanodegree. The great difference between Udacity's track and either Coursera's or edX's is that you get more interaction from the staff, such as feedback on your project and career advice.

Also note that the Nanodegree programs are not exactly course based, but instead project based. Udacity has a list of courses that it recommends to complete on its platform before embarking on the Nanodegree projects.

To pursue the Nanodegree, you'll need to set aside $200 per month for 9-12 months, but Udacity provides an amazing benefit where you'll get half of your tuition back if you graduate in less than 12 months.

Data Analyst Nanodegree
Institution: Udacity
Instructors: Cheng-Han Lee and Miriam Swords Kalk
Price:$200/month for 9-12 months (or $100/month if you graduate in less than 12 months)

Recommended Courses for Data Analyst Nanodegree




Complete Intros to Data Science

The courses below walk you through most of the data science pipeline, and get you up-to-speed on using the techniques required for the job. Most of the instructors are presently in the field, so they'll be covering what they think are the most useful and important skills and topics. After any of these courses, you'll be set to dive deeper into other, more specific topics in data science (e.g. deep learning, more advanced ML techniques, etc.).


Although there's a few Python courses in the Intro Data Science courses, here's a few more focused on learning Python itself rather than data science.

There's quite a lot of Python courses on Udemy, so I've narrowed it down to the ones with the best rating profiles

R Language

The Intro Data Sci courses section above includes a few that go through R, but here's a few more if you're looking to build a better base. These are mostly beginner courses, so you won't need any previous knowledge of R. That said, there are much more advanced R courses on Udemy for anyone looking to max their R skillset.


Working knowledge of SQL is a must if you plan on going into any interviews. You can learn a lot by learning SQL on-the-fly when programming in Python or R, but here's some courses if you want a better understanding.


Sometimes NoSQL is a better choice for big data, so here's Udemy's best courses on the subject.




You can approach learning on Dataquest in two ways: 1) you can choose one of three tracks for a more directed study, or 2) you can pick any particular course and begin learning that topic.

Unlike a lot of other platforms, Dataquest uses interactive code shells in the browser to make for a very engaging learning experience. You'll be using real datasets and real, portfolio-building projects.

Dataquest has courses on using both Python and R, as well as Apache Spark. The first lesson in each course is free, but to progress further, Dataquest offers two types of paid plans. The premium plan at $49/month is gives the learner full access to all courses and projects, whereas the professional plan at $199/month gives the learner all that plus 2 hours of 1-on-1 time per month.

Price:$49/month for premium, $199/month for professional


Data Scientist


  1. Python Introduction
  2. Data Analysis and Visualization
  3. Working with Data Sources
  4. Statistics and Linear Algebra
  5. Machine Learning
  6. Advanced Python and Computer Science
  7. Advanced Topics in Data Science
  8. Working with Large Datasets
  9. Learning R

Data Analyst


  1. Introduction to Python
  2. Python Applications
  3. Intermediate Python and Pandas
  4. Data Manipulation
  5. Working with Data Sources
  6. Probability and Statistics
  7. Learning R




O'Reilly offers over 150 hours of exclusive training videos under its data oriented learning paths. Unlike many of the other course routes listed here, O'Reilly's paths are pure video content, but they have several projects for you to do scattered throughout the lessons. O'Reilly allows anyone to see several of the videos in any path for free, so click on any of the path titles below to check them out.

Learning Paths:

Data Science with R

Learning Path: Data Science with R
This path is 24 hours long and takes you from beginner to an advanced level in R. You'll begin at the very start with installation of R, and go from statistical models, to visualizing data, to machine learning, to working with Microsoft Azure and R together.
Price: $459.99

  • Learning to Program with R (~4 hours)
  • Introduction to Data Science with R (~8.5 hours)
  • Expert Data Wrangling with R (~4 hours)
  • Writing Great R Code (~1 hour)
  • Data Science with Microsoft Azure and R (~7 hours)


Machine Learning

Learning Path: Machine Learning
The Machine Learning path is 23 hours long, and will take you through 6 courses, which includes several hours of video training on deep learning, algorithms, and data structures.
Price: $529.99

  • An Introduction to Machine Learning with Web Data (~3 hours)
  • Advanced Machine Learning (~2 hours)
  • Deep Learning (~2 hours)
  • Hardcore Data Science NYC 2014 (~5 hours)
  • Hardcore Data Science California 2015 (~6 hours)


Data Visualization

Learning Path: Data Visualization
At 14 hours of training, you'll not only learn all about visualizing data with D3.js, but also how to effectively communicate what your data is saying.
Price: $309.99

  • An Introduction to d3.js: From Scattered to Scatterplot (~3 hours)
  • Learning to Visualize Data with D3.js (~4 hours)
  • Using Storytelling to Effectively Communicate Data (1.5 hours)
  • Effective Data Visualization (~3 hours)
  • Intermediate D3.js (~4.5 hours)



Learning Path: Hadoop
The Hadoop video training is 16 hours long, and in it you'll get a good intro to Apache Hadoop and other technologies in the Hadoop ecosystem, like HDFS, MapReduce, Hive, Pig, and Impala. By the end you'll understand how to work with Hadoop and large datasets and perform analytical procedures.
Price: $319.99

  • Learning Apache Hadoop (~7.5 hours)
  • Hadoop Fundamentals for Data Scientists (~6 hours)
  • Architectural Considerations for Hadoop Applications (~2.5 hours)


Python for Data

Learning Path: Python for Data
This learning path is 19 hours long, and has an excellent intro to Python with lots of examples and exercises. You will also get a tutorial on iPython Notebook, which is an amazing tool to discover if you've never used it before. Lastly, you'll receive a copious amount of content on algorithms and data structures in Python.
Price: $429.99

  • Introduction to Python (~3.5 hours)
  • Learning iPython Notebook (~3 hours)
  • Working with Algorithms in Python (~8.5 hours)
  • Python Data Structures (~4 hours)


SQL and Relational Databases

Learning Path: SQL and Relational Databases
At 62 hours of video training, the SQL and Relational Databases course is the longest learning path that O'Reilly offers. This series is incredibly thorough, and the instructors, one of whom is a cofounder of relational database theory, will take you from a total beginner to an advanced SQL and relational database practitioner.
Price: $1299.99

  • Learning SQL (~3.5 hours)
  • Learning SQL For Oracle (~9 hours)
  • Relational Theory for Computer Professionals (~10 hours)
  • SQL: Beyond the Basics (~4 hours)
  • Learning Data Modeling (~8 hours)
  • Time and Relational Theory (~12 hours)
  • Nullology (~1 hour)
  • The Closed World Assumption (~1.5 hours)
  • An Introduction to Set Theory (~1 hour)
  • Nulls, Three-Valued Logic, and Missing Information (1 hour)
  • View Updating (~1 hour)
  • Normal Forms and All That Jazz Master Class (~10 hours)


Data Origami

Data Origami Logo

Data Origami offers screencasts that range in difficulty from beginner to advanced. Since the creator, Cameron Davidson-Pilon is also the author of the open source book Bayesian Methods for Hackers, you can expect some very interesting videos on useful statistics for Data Science.






Data School – Data science for beginners! | Data Science

edureka! | Data Science

Zipfian Academy | Data Science

David Langer | Data Science with R

Derek Kane | Data Science

MarinStatsLectures | Statistics

LearnR | R programming

Christoph Scherber | Statistics

Brandon Foltz | Statistics

statisticsfun | Statistics

Java and R Tutorials | R programming

bigdata simplified | All things big data

Derek Banas | Playlists on SQL and Python


This list far from comprehensive and there are many other great courses, classes, websites, eBooks, YouTube channels and individual videos on Data Science and the skills needed. We would love to add more content to this list. If you know of any, definitely let us know!

We would love to hear back from you.
Have you taken any of these courses?
How was it, what did you like about it, and how could have been better?



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30 Comments on "Top Data Science Online Courses in 2017"

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Hey! Just thought I would help you update this list 🙂

Here are a few more data science courses that I have found useful in my studies:

Data Science: Linear Regression in Python

Data Science: Logistic Regression in Python

Data Science: Deep Learning in Python


Best collection of data science courses i saw so far.

Actually i am in week 6 of the Coursera course “Data Science” and i wasnt aware of all the alternatives. Thank you very much!


Thanks for the extension list.

Feedback and/or ratings would be helpful in deciding where to start. It would take a lot of time for someone to go thru even a handful of the options.


I am looking for a certification in data science and wanted to showcase it as a skillset to get a job in analytics. Which certification would you recommend which will add weight to my resume and will be recognized in US job market?Appreciate your response.

Andrew Obrigewitsch

I’m currently doing the Statistics with R Specialization on Coursera, I’ve only done the first two parts (i.e. one half of it) and so far I’ve learned more than my calculus based college stats course.


Useful post, thank you.


Hi, this article is very helpful to all newbie in data science.


Just the information I was looking for. As mentioned by one of the guest, would be great if you can grade them on various parameters…coverage, depth of subject, content style- ease of understanding, relative certificate weightage give by prospective employers, etc.

Saeed Abdul Rahim
Saeed Abdul Rahim

I have taken multiple courses on Udemy.
And you show me there is a professional certificate! Is it worth it? Should I be taking that course?


which cource give placement


This is a great list on data science. As I plan to explore these, it is helping me look specifically.

Do you have something similar for big data, no sql?

pinak dutta
pinak dutta

Can some one help me in choosing one course out of the big list. I am already a data analyst and working on SAS. But topic wise i am new to statistics. I am pretty good ground on data mining, data cleaning. So the core analysis with Machine learning and Predictive modelling is basic objective.


Thanks for this list! Good reviews, and got me thinking about springboard.

What do you think of pluralsight? It seems to be a bit of a catalog of different courses, but I’ve liked it a lot. I also enjoyed datacamp quite a bit.


Hey Louie,
I’ve had my eye on Pluralsight for a little while and they seem like a great platform.
Once I’ve gone through some of their material, I’ll add them in on the next update of this page.

Thanks for reading!


Super helpful list. Any thoughts / experience with Thinkful and their Data Scientist offering?

Brian Wojciechowski
Brian Wojciechowski

What would be a good course for a beginner? I have taken programming and stats classes in the past but it has been a while.

I am looking for a career that I can work anywhere in the world.

Open to suggestions


Hey Brian,
Your best bet would probably be a combination of DataCamp, Andrew Ng’s ML Course, and Khan Academy’s Stats and Probability section to brush up on those topics.

These will give you a good starting point, after which you can dig down into other areas that might interest you. The key will be to start working on a project as soon as possible to hone your skills. So think of a project that would interest you and start working on it.


Great list! What do you think about Data Science MicroMaster from UC San Diego on Edx?


I like the way UCSD and edX has laid out the DS MicroMasters (link). The curriculum looks very well thought out, it’s focused on Python (which I like), and each course gets close to 5/5 stars. You’ll need undergrad calculus and linear algebra, but if you’ve got those down, it’s a great series to get started on.


What would be a good course for me? I have a master in computer science but it has been a while. I work as a teacher and looking for a career that I can work as data scientist

Open to suggestions

Bola Owoade
Bola Owoade

Edx also has Foundation of data analytics 1 and 2, data analytics for life sciences and the Analytics edge. Microsoft has an edx x series course in Excel called Data Analytics in Excel


Thank you so much for your great list! I wasn’t sure where to start my studies and I was worried about spending too much money, but these lists just brighten my path to become a data scientist. I will help to add more courses as I continue with my studies.


Hi Brendan…thanks for a very comprehensive work being conducted and especially for beginners. I am a professional engineer with 15 yrs experience and would now like to use data science in my day today data analysis due to huge amount of data and related correlations in my industry. What would you suggest be the best way to give myself hands-on on playing with data and analysis of trends, correlations, graphs…visualizations …etc.

Hi , brendan .. i am a complete beginner to this subject . I want to start a fresh career in data science . I have no language in programming and statistics. It has been few years i dropped out . Is there any hope left for me ? can i still pick up . i Can work really hard and give 10-12 hours/day of learning. Please give me a career path to follow to become a good data scientist and have a good job as well. Where should i start , what statistics and programming courses and what data… Read more »

Here is another one that offers mentors:


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