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.
Udemy
Data Science in General
Dataquest
You can approach learning on Dataquest in two ways: 1. you can choose one of three tracks for a more directed study, or you can pick any particular course and begin learning that topic. Dataquest focuses on teaching Data Science using Python, and the first lesson in each course is free.
Price: $35/month (for both tracks and courses)
Tracks:
Data Scientist
Steps:
 Python Introduction
 Data Analysis and Visualization
 Statistics and Linear Algebra
 Machine Learning
 Advanced Python and Computer Science
 Advanced Topics in Data Science
Data Analyst
Steps:
 Introduction to Python
 Python Applications
 Intermediate Python and Pandas
 Probability and Statistics
Data Engineer (Coming Soon)
Standalone Courses:
DataCamp
At only $25 per month for access to all courses, Datacamp is a very affordable way to get started with Data Science. Datacamp has four different tutorial blocks that take you through many different chapters.Price: $25/month
Curriculum:
Introduction to R
 Introduction to R
 Intermediate R
 Data Manipulation with dplyr
 Data Analysis the data.tabl Way
 Data Visualization with ggvis
 Reporting with R Markdown
 A HandsOn Introduction to Statistics with R
 Introduction to Machine Learning – Coming soon
 Big Data Analysis with Revolution R Enterprise
 R for SAS, SPSS and STATA users – Elective
 How to work with Quandl and R – Elective
 Kaggle: R tutorial on Machine Learning – Elective
Data Analysis and Statistical Inference
 Introduction to R
 Introduction to data
 Probability
 Foundations for inference: Sampling distributions
 Foundations for inference: Confidence intervals
 Inference for numerical data
 Inference for categorical data
 Introduction to linear regression
 Multiple linear regression
Intro to Computational Finance with R
 Return calculations
 Random variables and probability distributions
 Bivariate distributions
 Simulating time series data
 Analyzing stock returns
 Constant expected return model
 Introduction to portfolio theory
 Computing efficient portfolios using matrix algebra
R for Pharmaceutical Analysis (Coming soon)
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