Best Data Science Online Courses
The number of online data science courses have exploded in recent years and there courses for any needs. Here is a extensive list of free and paid courses from Coursera, DataCamp, Dataquest, edX, Udacity, Udemy, and other major providers.
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.
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.
- 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)
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.
- 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)
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.
- 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)
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.
- Learning Apache Hadoop (~7.5 hours)
- Hadoop Fundamentals for Data Scientists (~6 hours)
- Architectural Considerations for Hadoop Applications (~2.5 hours)
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.
- Introduction to Python (~3.5 hours)
- Learning iPython Notebook (~3 hours)
- Working with Algorithms in Python (~8.5 hours)
- Python Data Structures (~4 hours)
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.
- 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)
SpringBoard (formerly Sliderule)
Unlike many other paid Data Science course programs, SpringBoard offers 1-on-1 mentorship each week. SpringBoard doesn’t offer any free options, and is actually more expensive than other options if you take too long to complete it.
The foundations track focuses on R and is geared towards everyone starting from the ground up in Data Science.
- Probability & Statistics
- R Basics
- Exploratory Data Analysis
- Data Visualization
- Data Wrangling
- Analytics Techniques
- Capstone Project
Click here to download Sliderule’s Foundations of Data Science syllabus
The Intensive track is focused on using Python for Data Science and the course setup is more for people that already have backgrounds in mathematics and computer science.
- Programming Tools (Python)
- Data Wrangling
- Data Story
- Inferential Statistics
- Machine Learning
- Capstone Project
- Career Resources
Click here to download Sliderule’s Data Science Intensive syllabus
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.
- A/B Testing Conversion Rates
- Bayesian Beta-Binomial Model
- Bayesian Modelling (Car Arrival Problem)
- Create Markov Chains Using Your Chrome Browsing History
- Estimating the Hazard Function
- Estimating the Survival Function
- Sorting Colours using PCA
- Intro to PCA
- Sampling from Discrete Distributions
- Scraping the Web using Pandas
- Survival Analysis Bundle Pack
- Using Patsy for Categorical Data
- Visualizing PCA’s Information Loss
- Why Should I Be Interested in Survival Analysis?
- Determining Ages using First Name Data
- 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?
Bio: Brendan Martin is a Partner at Mint Design Company, and Content Writer at LearnDataSci.
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