Learning Paths
Explore all of our learning paths across data science, analytics, and machine learning interview questions.
These courses are created by experts at top technology companies across many different industries.
8 of 257 Completed
Data Science
Jay
Published
30 Courses
Data science helps businesses make smarter decisions by combining programming, statistics, modeling, and machine learning skills. Learn how to ace data science job interviews with a well-structured approach.
18 of 84 Completed
Data Analytics
Jay
Published
8 Courses
Data Analytics is the applied use of data to drive desision making. This learning path will help you understand and analyze data to develop insights, make better decisions and ultimately improve the company you work for!
10 of 73 Completed
Data Engineering
Jay
Published
8 Courses
Data engineers develop the tools and frameworks needed for data teams to function: they build pipelines, manage ETL, and build scalable data infrastructures. Learn the skills evaluated in data engineering job interviews.
0 of 46 Completed
Product Metrics
Jay
Published
6 Courses
Learn how to use product analytics to solve problems and empower business decisions.
6 of 63 Completed
Modeling & Machine Learning
Jay
Published
11 Courses
Understanding of how to build, deploy, and test machine learning models and tackle business problems while applying modeling concepts.
4 of 56 Completed
SQL
Jay
Published
6 Courses
SQL tests your analytical sense, ability to pull data for building models, and interpretation of datasets and metrics.
7 of 68 Completed
Statistics & AB Testing
Jay
Published
8 Courses
Statistics and A/B testing are the underlying parts of the data science knowledge base. Knowing how to run an A/B or multivariate test is table stakes.
8 of 49 Completed
Probability
Jay
Published
6 Courses
Probability theory underlies much of what we do as data scientists. Learn the basics and then move on to intermediate applications.
6 of 30 Completed
Python
Jay
Published
4 Courses
Other than SQL, Python is the most common language used in data science, due to its easy-to-read syntax and its robust collection of packages created specifically for data scientists.