# 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.

Contents

- 1 Coursera
- 1.1 Specializations:
- 1.1.1 Data Science Specialization
- 1.1.2 Machine Learning Specialization
- 1.1.3 Applied Data Science with Python Specialization
- 1.1.4 Statistics with R Specialization
- 1.1.5 Recommender Systems Specialization
- 1.1.6 Genomic Data Science Specialization
- 1.1.7 Big Data Specialization
- 1.1.8 Data Mining Specialization
- 1.1.9 Data Analysis and Interpretation Specialization

- 1.2 Other Coursera Data Science Courses:

- 1.1 Specializations:
- 2 DataCamp
- 3 Springboard
- 4 edX
- 5 Udacity
- 6 Udemy
- 7 Dataquest
- 8 O'Reilly
- 9 Data Origami
- 10 YouTube

## Coursera

**Specializations:**

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:**

- The Data Scientist's Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Inference
- Regression Models
- Practical Machine Learning
- Developing Data Products
- 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:**

__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:**

__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:**

__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:**

__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:**

- Introduction to Genomic Technologies
- Genomic Data Science with Galaxy
- Python for Genomic Data Science
- Command Line Tools for Genomic Data Science
- Algorithms for DNA Sequencing
- Bioconductor for Genomic Data Science
- Statistics for Genomic Data Science
- 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:**

- Introduction to Big Data
- Big Data Modeling and Management Systems
- Big Data Integration and Processing
- Machine Learning with Big Data
- Graph Analytics for Big Data
- 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:**

__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:**

**Other Coursera Data Science Courses:**

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

## DataCamp

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__

__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)**

**Intro to Python for Data Science****Intermediate Python for Data Science****Python Data Science Toolbox (Part 1)****Python Data Science Toolbox (Part 2)****Importing Data in Python (Part 1)****Importing Data in Python (Part 2)****Cleaning Data in Python****pandas Foundations****Manipulating DataFrames with pandas****Merging DataFrames with pandas****Introduction to Databases in Python****Introduction to Data Visualization with Python****Interactive Data Visualization with Bokeh****Statistical Thinking in Python (Part 1)****Statistical Thinking in Python (Part 2)****Supervised Learning with scikit-learn****Unsupervised Learning in Python****Network Analysis in Python (Part 1)****Machine Learning with the Experts: School Budgets**

__Data Analyst with Python__

__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)**

**Intro to Python for Data Science****Intermediate Python for Data Science****Python Data Science Toolbox (Part 1)****Importing Data in Python (Part 1)****Importing Data in Python (Part 2)****Cleaning Data in Python****pandas Foundations****Manipulating DataFrames with pandas****Merging DataFrames with pandas****Introduction to Databases in Python****Introduction to Data Visualization with Python****Statistical Thinking in Python (Part 1)****Statistical Thinking in Python (Part 2)**

__Data Scientist with R__

__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)**

**Introduction to R****Intermediate R****Intermediate R – Practice****Importing Data in R (Part 1)****Importing Data in R (Part 2)****Cleaning Data in R****Importing & Cleaning Data in R: Case Studies****Writing Functions in R****Data Manipulation in R with dplyr****Joining Data in R with dplyr****Data Visualization in R****Data Visualization with ggplot2 (Part 1)****Data Visualization with ggplot2 (Part 2)****Data Visualization with ggplot2 (Part 3)****Introduction to Data****Exploratory Data Analysis****Exploratory Data Analysis in R: Case Study****Correlation and Regression****Foundations of Inference****Machine Learning Toolbox****Machine Learning Toolbox****Text Mining: Bag of Words****Reporting with R Markdown**

__Data Analyst with R__

__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.**

**Introduction to R****Intermediate R****Intermediate R – Practice****Importing Data in R (Part 1)****Importing Data in R (Part 2)****Cleaning Data in R****Importing & Cleaning Data in R: Case Studies****Data Manipulation in R with dplyr****Joining Data in R with dplyr****Data Visualization in R****Data Visualization with ggplot2 (Part 1)****Introduction to Data****Exploratory Data Analysis****Exploratory Data Analysis in R: Case Study****Correlation and Regression****Reporting with R Markdown**

__Quantitative Analyst with R__

__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)**

**Introduction to R for Finance****Intermediate R for Finance***(Coming soon)***Manipulating Time Series Data in R with xts & zoo****Importing and Managing Financial Data in R****Introduction to Time Series Analysis****ARIMA Modeling with R****Manipulating Time Series Data in R: Case Studies****Introduction to Portfolio Analysis in R****Intermediate Portfolio Analysis in R****Bond Valuation and Analysis in R****Credit Risk Modeling in R****Financial Trading in R**

** **

**Springboard**

**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__

__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.**

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

**Curriculum:**

**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__

__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*

**Curriculum:**

**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

** **

**edX**

**XSeries**

**XSeries**

**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__

__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:**

** **

__Data Science and Engineering with Apache® Spark™__

__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:**

** **

**Professional Certificates**

**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__

__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:**

**Unit 1 – Fundamentals****Data Science Orientation****Querying Data with Transact-SQL****Analyzing and Visualizing Data with Excel OR Analyzing and Visualizing Data with Power BI****Statistical Thinking for Data Science and Analytics****Unit 2 – Core Data Science****Introduction to R for Data Science Course OR Introduction to Python for Data Science****Data Science Essentials****Principles of Machine Learning****Unit 3 – Applied Data Science****Programming with R for Data Science OR Programming with Python for Data Science****Applied Machine Learning OR Developing Intelligent Apps****Implementing Predictive Solutions with Spark in Azure HDInsight****Unit 4 – Capstone Project – Cortana Intelligence Competition**

** **

**Other edX Data Science Courses:**

**Other edX Data Science Courses:**

**Data Science and Machine Learning Essentials****Introduction to Computational Thinking and Data Science****Introduction to R Programming****Introduction to Computer Science and Programming Using Python****Data Analysis: Take It to the MAX()****Text Mining and Analytics****Data, Analytics and Learning****Implementing Real-Time Analytics with Hadoop in Azure HDInsight****Big Data in Education****Statistics and R for the Life Sciences****Explore Statistics with R****Text Mining and Analytics****Introduction to Linear Models and Matrix Algebra****Applications of Linear Algebra Part 1****Applications of Linear Algebra Part 2****The Analytics Edge****CS For All: Introduction to Computer Science and Python Programming**

** **

**Udacity**

**Nanodegree:**

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**

- Intro to Computer Science: Build a Search Engine & a Social Network (
**Free**, or $199/month for projects with reviews, coaches, and a verified certificate) - A/B Testing: Online Experiment Design and Analysis (
*Free**)* - Data Visualization and D3.js: Communicating with Data (
*Free**)* - Intro to Machine Learning: Pattern Recognition for Fun and Profit (
*Free**)* - Intro to Hadoop and MapReduce How to Process Big Data (
**Free**, or $199/month for projects with reviews, coaches, and a verified certificate) - Real-Time Analytics with Apache Storm: The “Hadoop of Real-Time” (
*Free**)* - Intro to Data Science: Learn What It Takes to Become a Data Scientist
*(***Free**, or $199/month for projects with reviews, coaches, and a verified certificate) - Data Analysis with R: Visually Analyze and Summarize Data Sets (
*Free**)* - Intro to Statistics: Making Decisions Based on Data (
*Free**)* - Intro to Descriptive Statistics: Mathematics for Understanding Data (
*Free**)* - Intro to Inferential Statistics: Making Predictions from Data (
*Free**)* - Data Wrangling with MongoDB: Data Manipulation and Retrieval (
*Free**)* - Model Building and Validation: Advanced Techniques for Analyzing Data (
*Free**)*

## Udemy

**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.).

- Data Science A-Z™: Real-Life Data Science Exercises Included
- Data Science and Machine Learning with Python – Hands On!
- Python for Data Science and Machine Learning Bootcamp
- Data Science and Machine Learning Bootcamp with R
- Introduction to Data Science
- Applied Data Science with R

**Python**

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

- Complete Python Bootcamp: Go from zero to hero in Python
- The Python Mega Course: Build 10 Real World Applications
- The Python Bible | Everything You Need to Program in Python
- Complete Python Masterclass
- The Complete Python & PostgreSQL Developer Course
- Become a Professional Python Programmer
- 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.

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

**SQL**

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.

- The Complete SQL Bootcamp
- The Complete Oracle SQL Certification Course
- Microsoft SQL for Beginners
- SQL for Newbs: Beginner Data Analysis
- SQL Tutorial: Learn SQL with MySQL Database -Beginner2Expert
- SQL : Master class for SQL data analytics
- 200+ SQL Interview Questions
- A beginners guide to writing SQL Functions

**NoSQL**

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

## Dataquest

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*

**Tracks:**

#### Data Scientist

**Steps:**

- Python Introduction
- Data Analysis and Visualization
- Working with Data Sources
- Statistics and Linear Algebra
- Machine Learning
- Advanced Python and Computer Science
- Advanced Topics in Data Science
- Working with Large Datasets
- Learning R

#### Data Analyst

**Steps:**

- Introduction to Python
- Python Applications
- Intermediate Python and Pandas
- Data Manipulation
- Working with Data Sources
- Probability and Statistics
- Learning R

#### Data Engineer (Coming Soon)

**Other Dataquest Data Science Courses:**

## O'Reilly

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

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
*

**Lessons:**

- 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

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
*

**Lessons:**

- 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

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
*

**Lessons:**

- 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)

#### 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
*

**Lessons:**

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

#### 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
*

**Lessons:**

- 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

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
*

**Lessons:**

- 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 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.

**Screencasts:**

**A/B Testing Conversion Rates**(*$9*)**Bayesian Beta-Binomial Model**(*$9*)**Bayesian Modelling (Car Arrival Problem)**(*$9*)**Create Markov Chains Using Your Chrome Browsing History**(*$9*)**Estimating the Hazard Function**(*$9*)**Estimating the Survival Function**(*$9*)**Sorting Colours using PCA**(*Free*)**Intro to PCA**(*$9*)**Sampling from Discrete Distributions**(*$9*)**Scraping the Web using Pandas**(*$9*)**Survival Analysis Bundle Pack**(*$19*)**Using Patsy for Categorical Data**(*$9*)**Visualizing PCA's Information Loss**(*$9*)**Why Should I Be Interested in Survival Analysis?**(*$9*)**Determining Ages using First Name Data**(*$9*)

## YouTube

### Channels

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?

**13**

Leave a Reply11 Comments on "Top Data Science Online Courses in 2017"Sort by: newest | oldest | most votedPradeepHey! 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 Pythonhttps://www.udemy.com/data-science-linear-regression-in-python

Data Science: Logistic Regression in Pythonhttps://www.udemy.com/data-science-logistic-regression-in-python

Data Science: Deep Learning in Pythonhttps://www.udemy.com/data-science-deep-learning-in-python

Vote Up2Vote Down1 year 3 months agoBrendanThat’s great, Pradeep. The course creators on Udemy are always pumping out new stuff!I’ll be sure to add these on the next update.

Vote Up0Vote Down1 year 2 months agoTobiasBest 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!Vote Up1Vote Down11 months 7 days agoBrendanYou’re very welcome, Tobias!Vote Up1Vote Down10 months 18 days agoSmyrnaThanks 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.Vote Up2Vote Down10 months 8 days agoBrendanI totally agree. That’s definitely something I’m working on for the next update.Vote Up0Vote Down1 month 2 days agoJoshI 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.Vote Up0Vote Down6 months 28 days agoAndrew ObrigewitschI’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.Vote Up1Vote Down1 month 5 days agoAndrewHere are some more courses to add:https://www.coursera.org/learn/linear-models

https://www.coursera.org/learn/linear-models-2

Vote Up1Vote Down1 month 5 days agoBrendanWill add them next update. Thanks!Vote Up0Vote Down1 month 2 days agopaoloUseful post, thank you.Vote Up1Vote Down15 days 2 hours ago