DataCamp's Curriculum Team has designed course programs (called 'Tracks') to guide your data science journey and incrementally build complementary skills. We currently offer two different types of Track -- 'Career' and 'Skill' -- in a variety of programming languages, accessible via our 'Learn' menu:

Note: While you can only enroll in one Track at a time, courses within Tracks can be completed at any time -- enrolled or otherwise -- and apply to your enrolled Tracks at any future date.

Once completed, you need to be enrolled in a Track in order to download that Track's Statement of Accomplishment.

Career Tracks

Career Tracks are our most thorough option for Python and R, beginning with introductory courses that cover basic skills (i.e. the syntax of the language) and ending with more focused, advanced topics (i.e. machine learning, network analysis, etc.).

Career Tracks in Python:

• Python Programmer (13 courses)
• Data Analyst with Python (16 courses)
• Data Scientist with Python (26 courses)
• Machine Learning Scientist with Python (23 courses)
• Data Engineering with Python (18 courses)

Career Tracks in R:

• R Programmer (9 courses)
• Data Analyst with R (19 courses)
• Data Scientist with R (22 courses)
• Quantitative Analyst with R (15 courses)
• Statistician with R (14 courses)
• Machine Learning Scientist with R (15 courses)

Note: Courses in the Programmer Tracks also appear in the Data Analyst Tracks, and courses in the Data Analyst Tracks also appear in the Data Scientist Tracks.

Skill Tracks

Skill Tracks range from 3 - 7 courses, focusing on specific complementary skills in various programming languages. (For Python and R, these courses and skills may also be included in Career Tracks).

DataCamp's Curriculum Team has curated Skill Tracks for intermediate-to-advanced programmers seeking particular skills, i.e Cleaning Data or Machine Learning.

Skill Tracks in Python:

• Python Programming (4 courses)
• Importing and Cleaning Data with Python (4 courses)
• Data Manipulation with Python (4 courses)
• Machine Learning Fundamentals with Python (5 courses)
• Time Series with Python (5 courses)
• Statistic Fundamentals with Python (5 courses)
• Image Processing with Python (3 courses)
• Data Visualization with Python (5 courses)
• Python Toolbox (5 courses)
• Big Data with PySpark (6 courses)
• Coding Best Practices with Python (7 courses)

Skill Tracks in R:

• R Programming (4 courses)
• Importing and Cleaning Data with R (4 courses)
• Data Manipulation with R (4 courses)
• Statistics Fundamentals with R (5 courses)
• Data Visualization with R (5 courses)
• Time Series with R (6 courses)
• Applied Finance with R (4 courses)
• Finance Basics with R (4 courses)
• Machine Learning Fundamentals with R (4 courses)
• Text Mining with R (4 courses)
• Spatial Data with R (4 courses)
• Shiny Fundamentals with R (3 courses)
• Big Data with R (5 courses)
• Tidyverse Fundamentals with R (5 courses)
• Network Analysis with R (4 courses)
• Supervised Machine Learning in R (5 courses)
• Interactive Data Visualization in R (5 courses)
• Unsupervised Machine Learning with R (4 courses)
• Marketing Analytics with R (4 courses)
• Statistical Inference with R (4 courses)
• Probability and Distributions with R (4 courses)
• Intermediate Tidyverse Toolbox (4 courses)
• Analyzing Next-Generation Sequencing Data in R (5 courses)

Skill Tracks in SQL:

• SQL Fundamentals (4 courses)
• SQL Server Fundamentals (4 courses)
• SQL Server Toolbox (4 courses)

Skill Tracks in Spreadsheets:

• Spreadsheet Fundamentals (4 courses)