Data Science & ML Interview Mastery
Welcome, champion! 🔥
You've mastered 0 of 183 lessons. Start your first lesson free — no signup needed.
About this course
Data Science & ML Interview Mastery is a guided course on Abekus for candidates preparing for data science and machine learning interviews in India. It takes you from bias and variance and logistic regression through ensembles, imbalanced data, causal inference, pandas and SQL coding rounds, and end-to-end case studies. The course has 183 lessons and about 23 hours of content. Each lesson is a short reading card with narration and diagrams, followed by MCQ practice with an explanation for every answer.
Who this data science and ML course is for
The course suits final-year students and freshers aiming at data analyst, data scientist and ML engineer roles, and working analysts who want to move into modelling. If you already write Python and SQL comfortably, you will spend more time on the concept and case-study sections. Beginners should follow the lessons in order, because later modules assume the metric and model basics from the early topics, such as precision, recall, train-validation splits and cross-validation. Candidates preparing for product analytics roles will find the experimentation and metric-selection lessons especially useful.
How MCQ practice works on Abekus
Every lesson opens with a reading card that explains one idea at a time, for example why ROC-AUC can look strong on a heavily imbalanced dataset, or when gradient boosting beats a random forest. Practice follows straight after, with one multiple-choice question at a time. Each answer comes with an explanation, including why the tempting wrong options fail. Lessons unlock in order, so the course is guided and self-paced: you move forward when the current lesson is solid, and the gaps you leave behind show up in the practice rather than in the interview room.
MCQ practice vs video courses for data science
Udemy and Coursera are video-based lecture platforms. They suit learners who want a long walkthrough of a model before they practise, and their catalogues cover the full data science curriculum in lecture form. Kaggle Learn is the right choice if you want hands-on notebooks on real datasets. This course is MCQ-based active recall built for screening rounds, where interviewers describe a short scenario and ask you to choose the right metric, test or model and defend the choice.
Best way to prepare for data science interviews
Active recall beats re-reading. Work through a lesson, answer its practice cold, then read the explanations for every miss. The approach that works best for most candidates has three habits:
- Explain each concept in your own words before opening the explanation.
- Retry missed questions after two or three days, not the same afternoon.
- Practise case-study lessons out loud, stating the metric, the trade-off and the rollout plan.
Keep a one-page notebook of the metrics and models you have covered, and update it as you go. If your Python is still shaky, start with Python Fundamentals before the coding rounds, and build the probability base in Statistics and Probability for Data Roles. Both help the pandas and experimentation lessons land faster.