Mission
Build the technical judgment and communication skills needed to succeed in Data Analyst, Data Engineer, and Business Intelligence interviews. The sequence moves from core analysis skills through data modeling, pipelines, BI systems, and realistic interview practice.
Ace Data Analyst/Engineer / Business Intelligence interview
~29 hours · 8 courses
Course 1 · ~240 min
Write and explain SQL queries using joins, CTEs, window functions, conditional aggregation, and time-based analysis while identifying correctness and performance issues.
SQL is a core screening skill across analyst, BI, and data engineering roles, and strong query reasoning unlocks later work with metrics, models, and pipelines.
Course 2 · ~210 min
Use Python and pandas to transform datasets, handle missing and inconsistent values, calculate grouped metrics, analyze distributions, and explain implementation choices.
Python complements SQL in take-home and live interviews, especially when problems require flexible data transformation, automation, or reasoning beyond database queries.
Course 3 · ~240 min
Select and interpret appropriate statistical methods, explain uncertainty, evaluate A/B test results, and communicate whether evidence supports a business conclusion.
Analyst and BI interviews test whether you can turn metrics into defensible conclusions rather than merely calculate them.
Course 4 · ~180 min
Translate ambiguous business questions into measurable metrics, design an analysis plan, diagnose changes in performance, and present a recommendation supported by evidence.
Case interviews reveal whether you can connect technical analysis to business outcomes and choose the right questions before writing code.
Course 5 · ~210 min
Design and critique analytical schemas by defining grain, relationships, dimensions, facts, keys, and appropriate modeling trade-offs.
Data modeling connects SQL analysis to reliable BI and engineering systems and is frequently tested in data engineer and BI interviews.
Course 6 · ~240 min
Explain how to design dependable batch data pipelines, choose between ETL and ELT, implement incremental processing concepts, and diagnose common pipeline failures.
Pipeline reasoning is essential for data engineering interviews and helps BI candidates explain how trustworthy reporting data is produced.
Course 7 · ~180 min
Design a clear dashboard specification, choose effective visualizations, define trustworthy KPIs, and explain analytical findings to technical and nontechnical audiences.
BI interviews assess both technical dashboard judgment and the ability to make insights understandable and actionable.
Course 8 · ~240 min
Solve representative interview questions under time constraints, explain assumptions and trade-offs, recover from mistakes, and deliver concise structured answers.
Final-round performance depends on combining technical accuracy with clear reasoning, effective clarification, and confident communication.