Top Data Science Online Courses in 2018

A comprehensive, hand-curated list of some of the best data science courses available online.

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.


Coursera courses last approximately four to ten weeks, with one to two hours of video lectures a week. These courses provide quizzes, weekly exercises, peer-graded assignments, and sometimes a final project or exam. Courses are also provided on-demand, in which case users can take their time in completing the course with all of the material available at once


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: $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:

  1. Introduction to Data Science
  2. Process Mining: Data science in Action
  3. Genomic Data Science and Clustering (Bioinformatics V)
  4. Big Data Science with the BD2K-LINCS Data Coordination and Integration Center
  5. Computational Methods for Data Analysis
  6. Data Analysis and Statistical Inference
  7. Statistics: Making Sense of Data
  8. Mining Massive Data Sets


edX hosts online university-level courses in a wide range of disciplines to a worldwide student body, including some courses at no charge

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:


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

Data Scientist with Python

You can be a complete beginner in Python and still take this track. DataCamp starts you off by digging into the basics of Python, and works all the way to advanced data manipulation features and ML libraries. You'll learn how to import, clean, and manipulate and store data, then how to visualize and perform both supervised and unsupervised machine learning techniques.

19 Courses (67 hours)

  1. Intro to Python for Data Science
  2. Intermediate Python for Data Science
  3. Python Data Science Toolbox (Part 1)
  4. Python Data Science Toolbox (Part 2)
  5. Importing Data in Python (Part 1)
  6. Importing Data in Python (Part 2)
  7. Cleaning Data in Python
  8. pandas Foundations
  9. Manipulating DataFrames with pandas
  10. Merging DataFrames with pandas
  11. Introduction to Databases in Python
  12. Introduction to Data Visualization with Python
  13. Interactive Data Visualization with Bokeh
  14. Statistical Thinking in Python (Part 1)
  15. Statistical Thinking in Python (Part 2)
  16. Supervised Learning with scikit-learn
  17. Unsupervised Learning in Python
  18. Network Analysis in Python (Part 1)
  19. Machine Learning with the Experts: School Budgets

Data Analyst with Python

This track is essentially a shorter version of the Data Scientist track, and it stops at Statistical Thinking instead of going on to machine learning topics.

13 Courses (47 hours)

  1. Intro to Python for Data Science
  2. Intermediate Python for Data Science
  3. Python Data Science Toolbox (Part 1)
  4. Importing Data in Python (Part 1)
  5. Importing Data in Python (Part 2)
  6. Cleaning Data in Python
  7. pandas Foundations
  8. Manipulating DataFrames with pandas
  9. Merging DataFrames with pandas
  10. Introduction to Databases in Python
  11. Introduction to Data Visualization with Python
  12. Statistical Thinking in Python (Part 1)
  13. Statistical Thinking in Python (Part 2)

Data Scientist with R

This track takes you from absolutely no knowledge of R to to effective machine learning with the language. You'll start with R basics, advanced to intermediate level, then begin working with data fairly quickly.

Learning how to import, clean, manipulate, store, and visualize data is all included. Then, you'll get a statistics refresher and begin working with machine learning in R.

23 Courses (95 hours)

  1. Introduction to R
  2. Intermediate R
  3. Intermediate R – Practice
  4. Importing Data in R (Part 1)
  5. Importing Data in R (Part 2)
  6. Cleaning Data in R
  7. Importing & Cleaning Data in R: Case Studies
  8. Writing Functions in R
  9. Data Manipulation in R with dplyr
  10. Joining Data in R with dplyr
  11. Data Visualization in R
  12. Data Visualization with ggplot2 (Part 1)
  13. Data Visualization with ggplot2 (Part 2)
  14. Data Visualization with ggplot2 (Part 3)
  15. Introduction to Data
  16. Exploratory Data Analysis
  17. Exploratory Data Analysis in R: Case Study
  18. Correlation and Regression
  19. Foundations of Inference
  20. Machine Learning Toolbox
  21. Machine Learning Toolbox
  22. Text Mining: Bag of Words
  23. Reporting with R Markdown

Data Analyst with R

16 Courses (64 hours)

This track is basically a shorter version of the data scientist with R track where you'll stop just before the more advanced machine learning courses.

  1. Introduction to R
  2. Intermediate R
  3. Intermediate R – Practice
  4. Importing Data in R (Part 1)
  5. Importing Data in R (Part 2)
  6. Cleaning Data in R
  7. Importing & Cleaning Data in R: Case Studies
  8. Data Manipulation in R with dplyr
  9. Joining Data in R with dplyr
  10. Data Visualization in R
  11. Data Visualization with ggplot2 (Part 1)
  12. Introduction to Data
  13. Exploratory Data Analysis
  14. Exploratory Data Analysis in R: Case Study
  15. Correlation and Regression
  16. Reporting with R Markdown

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: $1490/month for 6 months, or $7500one-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


Udemy serves as a platform that allows instructors to build online courses on topics of their choosing. Instructors can also engage and interact with users via online discussion boards.

Tons of data science courses have been popping up on Udemy recently. The course instructors work extremely hard keeping their courses updated, sometimes putting in a complete overhaul of the course material with no additional cost to the student. So in this post, I want to touch on those courses that have not only been extremely helpful for data science students, but also have spent a lot of time staying up-to-date.

Udemy courses are inherently well-defined for specific topics, and most of the time the instructors take time to go through real-world examples you can follow along with and add to your portfolio. Learning by creating something real is much more fun and the lessons tend to stick better.

As a side note, Udemy offers a generous 30-day money back guarantee on all of their courses, so 100% guaranteed to love the course or get your money back (some employers will reimburse the fee too).

If you're interested in learning more about ALL of the courses I've come across to date, definitely check out this giant data science course list.

Now let's get to the list! Below you’ll find the most highly rated and recommended data science courses on Udemy for 2017.

Complete Intro 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.).

  1. Data Science A-Z™: Real-Life Data Science Exercises Included
  2. Data Science and Machine Learning with Python – Hands On!
  3. Python for Data Science and Machine Learning Bootcamp
  4. Data Science and Machine Learning Bootcamp with R
  5. Introduction to Data Science
  6. Applied Data Science with R


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

  1. Complete Python Bootcamp: Go from zero to hero in Python
  2. The Python Mega Course: Build 10 Real World Applications
  3. The Python Bible | Everything You Need to Program in Python
  4. Complete Python Masterclass
  5. The Complete Python & PostgreSQL Developer Course
  6. Become a Professional Python Programmer
  7. Python A-Z: Python For Data Science With Real Exercises!

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.

  1. R Programming A-Z: R For Data Science With Real Exercises!
  2. Learn R By Intensive Practice
  3. R Level 1 – Data Analytics with R
  4. Regression, Data Mining, Text Mining, Forecasting using R
  5. Statistics with R – Beginner Level
  6. Learn By Example: Statistics and Data Science in R


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.

  1. The Complete SQL Bootcamp
  2. The Complete Oracle SQL Certification Course
  3. Microsoft SQL for Beginners
  4. SQL for Newbs: Beginner Data Analysis
  5. SQL : Master class for SQL data analytics
  6. 200+ SQL Interview Questions
  7. A beginners guide to writing SQL Functions


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

  1. Learn How Python Works with NoSql Database MongoDB: PyMongo
  2. NoSQL: Neo4j and Cypher (Part: 1-Beginners)


Udacity is built with topic specializations called "Nanodegrees," and each of these tracks are in collaboration with big companies and ML projects, like Amazon, Google, IBM Watson, etc.

Udacity is a good platform overall, and they do a great job helping students build a portfolio during each program.

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.



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 gives the learner full access to all courses and projects

The professional plan at $199/month gives the learner all that plus 2 hours of 1-on-1 time per month.


  • $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

Data Engineer (Coming Soon)

Other Dataquest Data Science Courses



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

Be notified when we release new material

Join over 3,500 data science enthusiasts.