Condensed Placement Preparation Syllabus
Python · SQL · Statistics · EDA · Visualization · Storytelling
A concise, placement-focused syllabus — covers every key concept without overloading.
Module 1
Module 2
Module 3
Module 4
Module 5
Module 6
Module 7
Module 8
Learn recurring patterns, not one-off tricks — the fastest path to solving new problems.
Every module ends with an assessment or drill mapped to the way rounds are actually run.
Unlimited AI Tutor, Mock Interviews and Resume Review are included with your subscription.
Data science for analyst and data-role interviews: descriptive statistics, probability distributions, hypothesis testing and confidence intervals, correlation versus causation, then the practical pipeline — data cleaning, missing values, outliers, exploratory analysis and visualisation choices.
The applied half covers SQL for analytics, Python with pandas and NumPy, feature engineering, an introduction to modelling and evaluation, and dashboard and reporting thinking: how to turn a query result into a recommendation a business will act on. Case-style questions are included, since analyst interviews are largely case-driven.
Analyst hiring tests statistics reasoning and SQL far more heavily than model building, and it almost always includes a case question. Candidates who can explain a metric change or design an A/B test clear these rounds ahead of candidates with longer tool lists.
Free
Premium
SQL querying, statistics and hypothesis testing, Excel or pandas data manipulation, visualisation judgement, and a business case discussion.
Statistics and SQL first. They are prerequisites for ML interviews and are the bulk of analyst hiring.
Clarify the metric and time window, segment by dimension, separate a data issue from a real change, form a hypothesis, then say what you would measure next.