We designed the data science learning path to be overarching. It goes through all the topics they may evaluate in data science job interviews so you can prepare using a single learning path.

Most of the courses within this learning path appear in other learning paths in Interview Query. For example, the SQL courses in the data science learning path also appear on the SQL interview questions learning path.

However, as you may have noticed already, every course tracks your progress - so feel free to skip any material you have already revised:

This learning path is organized into four modules:

We’ll begin by going through the coding skills required for data science:

• Easy SQL questions
• Medium SQL questions
• Hard SQL questions
• Python data structures
• Common DS Packages
• Python Questions: Hard

Then we’ll move to statistics & probability concepts and interview questions:

• Basic probability
• Discrete distributions
• Continuous distributions
• Multivariate distributions
• Sampling theorems
• Hypothesis testing
• Confidence intervals
• A/B testing & experiment design
• A/B testing common scenarios
• A/B testing tradeoffs
• Statistics

Later, the product & business applications will help you apply statistical concepts to business and product problems.

• Data analytics fundamentals: Causal Inference
• Diagnosing and investigating metrics
• Measuring success
• Feature change
• Metric tradeoffs

Finally, we’ll complete the learning path by going through the machine learning concepts and applications you might get tested on in a data science interview:

• Modeling case-study
• Data pre-processing
• Feature selection
• Model selection
• Machine learning algorithms
• Model evaluation
• Applied modeling
• Machine learning system design
• Generalized linear models and regression

The learning path has a practical approach designed to help you practice interview questions for all these topics.

Its structure follows the three groups of data science questions we presented during this course. However, because of its size, we divided the data science technical skills group into a *statistics and probability** module and a machine learning module.

We decided to present the machine learning module at the end so you could get a feel of practical business applications before diving into more abstract and complex topics.

We’ll introduce all the necessary concepts and skills along the learning path. However, as it focuses mainly on interview preparation, prior experience with the topics presented would be helpful.

Good luck!