# 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 Hands-On 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