Python for Data Analytics & Data Science [2026]

In this course, we aim to provide you with a focused and efficient approach to preparing for data science tasks through practical questions. I understand that your time is valuable, so I have carefully curated the content to cut out any unnecessary noise and provide you with the most relevant materials.

First section of the course will prepare you for fundamentals of Python.

Moving beyond theory, the course will dive into a wide range of practical data science questions. These questions have been carefully selected to represent the types of problems frequently encountered in real-world data science roles. By practicing these questions, you will develop the skills and intuition necessary to tackle similar problems during interviews.

Throughout the course, we have filtered out any extraneous materials and focused solely on the core topics and questions that are most likely to come up in data science interviews. This approach will save you time and allow you to focus your efforts on what truly matters.

Index:

• Missing values and how to handle them? (Python)

• What are categorical variables and how to include them into model (Python)

• What is a Correlation Matrix's role? (Python)

• How to check relationship between variables? (Python)

• How to interpret the regression analysis? (Python)

• How to improve the regression model results with logarithmic transformation? (Python)

• How to use polynomial model? (Python)

• What is an overfitting? How to prevent it? (Theory)

• Supervised vs Unsupervised Learning (Theory)

• Parametric and Non-parametric model (Theory)