What are tracks, and how do I choose the best one?

Tracks are a program of courses, designed to guide you in your Data Science journey and help you build complementary skills progressively. There are two types of Tracks: Career Tracks and Skill Tracks, available in Python and R (if you need help choosing between R and Python, this infographic will prove helpful). You can access them by clicking on the Learn menu:

Career Tracks

Career Tracks are the most complete Tracks, in the sense that they begin with introductory, beginner courses teaching the basics (the syntax of the language), and end with advanced topics (machine learning, network analysis...).

Both Python and R have three similar Tracks:

1. Programmer (introduction to the syntax, data visualization and data manipulation)
2. Data Analyst (advanced data visualization, data manipulation and statistics)
3. Data Scientist (Machine Learning)

R also has a Quantitative Analyst Track, focused on finance, which will soon be available in Python.

The available Career Tracks in Python are the following:

• the Python Programmer Track contains 10 courses
• the Data Analyst with Python Track contains 13 courses
• the Data Scientist with Python Track contains 20 courses

The available Career Tracks in R are the following:

• the R Programmer Track contains 10 courses
• the Data Analyst with R Track contains 16 courses
• the Data Scientist with R Track contains 23 courses
• the Quantitative Analyst with R Track contains 15 courses

The courses in the Programmer Track are also part of the Analyst Track, and the courses in the Analyst Track are also part of the Scientist Track.

In other words, after completing the Python Programmer Track, you would only have to finish 3 more courses to complete the Data Analyst with Python track, and after completing the Data Analyst with Python track, you would only have to finish 7 more course to complete the Data Scientist with Python Track.

Similarly, after completing the R Programmer Track, you would only have to finish 6 more courses to complete the Data Analyst with R Track, and after completing the Data Analyst with R Track, you would only have to finish 7 more course to complete the Data Scientist with R Track.

It is strongly suggested to begin with one of these three Tracks to acquire solid foundations, and make your learning curve smoother. Once you're done with these Tracks, you will have enough knowledge and skills to cherry pick which topics you want to learn next. If you want to strengthen statistical methods, you can take advanced statistics courses, if you want to learn Deep Learning, you will choose the Machine Learning courses that correspond, etc.

It is not possible to enroll in more than one Track at a time, but it is possible to complete the courses of another Track without being enrolled in it. If you realize that you completed all the courses in a Track, but don't have a certificate available, then you can enroll in the Track and the certificate should appear after 24 hours.

Skill Tracks

Skill Tracks are shorter than Career Tracks. Most of them contain 4 courses, although some may have 5 or 6. While Career Tracks are designed to provide you with a package of complementary skills, Skill Tracks are focused on one specific topic only. These topics may or may not be part of Career Tracks. If you're already an experienced programmer, familiar with the R or Python syntax and the different libraries used in Data Science, but want to learn a new particular skill, like Cleaning Data or Machine Learning, then Skill Tracks are probably the best choice.

The available Skill Tracks in Python are the following:

• Python Programming (4 courses)
• Importing and Cleaning Data with Python (4 courses)
• Data Manipulation with Python (4 courses)
• Machine Learning with Python (4 courses)

The available Skill Tracks in R are the following:

• R Programming (4 courses)
• Importing and Cleaning Data with R (4 courses)
• Data Manipulation with R (4 courses)
• Statistics 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 with R (4 courses)