Data Analytics & BI Academy
SQL · Python · BI · Engineering
Structured learning with practice questions, guides, and preparation material.
👤 Who This Is For
📋 Prerequisites
🎯 What You'll Be Able to Do
📚 Explore Topics
Data Analysis
Analyse and interpret data effectively
Business Intelligence
BI tools, dashboards, and reporting
Microsoft BI — DAX, data modelling, star schema, RLS, publish
Tableau Desktop and Server — calculated fields, LOD expressions, dashboards
Visualization principles — choosing the right chart, avoiding misleading graphs, storytelling with data
Designing effective dashboards — KPI selection, layout, drill-downs, refresh strategy across BI tools
Data Engineering
Pipelines, warehouses, and big data
SQL from SELECT to window functions, CTEs, performance tuning
Analytics engineering with dbt — models, tests, macros, incremental builds, documentation
Workflow orchestration — DAGs, operators, scheduling, sensors, production pipeline patterns
Distributed data processing — RDDs, DataFrames, partitioning, cluster tuning basics
Cloud data warehousing across vendors — BigQuery, Redshift, Synapse, Snowflake compared, not one vendor only
MIS Bridge Path
An alternate route into Excel, SQL, NumPy, Pandas, and Power BI, framed for Excel-fluent MIS professionals transitioning to Python/SQL — not a replacement for the dedicated Excel/Pandas/Power BI/SQL technologies above, which cover each tool in more depth on its own terms.
Orientation for this bridge path — how Excel, SQL, NumPy, Pandas, and Power BI fit together as one stack, and why an MIS-background learner would take this route instead of the standalone technologies
Python and SQL taught by mapping directly onto Excel intuition — VLOOKUP/XLOOKUP to pandas merge, pivot tables to groupby, for an Excel-fluent learner transitioning to code
Automation workflows for MIS professionals — scheduled reporting scripts, SQLAlchemy, Plotly dashboards, and a full Windows Task Scheduler automation pipeline