Level 1 — Power BI Fundamentals

Desktop, Service, Reports, Dashboards, Workspaces, Data sources, Import mode, DirectQuery, Semantic models

Level 2 — Power Query

Data cleaning, transformations, merge, append, group, pivot/unpivot, conditional/custom columns, data types

🧮 STEP 8 — DAX

SUM, COUNT, COUNTROWS, DISTINCTCOUNT, AVERAGE, MIN, MAX

CALCULATE, FILTER, ALL, ALLSELECTED, REMOVEFILTERS, VALUES, SELECTEDVALUE

SUMX, AVERAGEX, COUNTX, MINX, MAXX

Time Intelligence: TOTALYTD, TOTALMTD, TOTALQTD, SAMEPERIODLASTYEAR, DATEADD, DATESYTD, DATESMTD

Measures: YTD, MTD, QTD, Previous Year, YoY %, Running Total, Rolling 12M, Market Share, Contribution %

🏗️ STEP 9 — Data Modeling

Fact tables, Dimension tables, Star schema, Snowflake schema, Relationships, Cardinality, Cross-filter direction, Active/Inactive relationships, Role-playing dimensions, Date tables

🎨 STEP 10 — Power BI Visualization

Cards, Tables, Matrix, Bar, Column, Line, Area, Scatter, Map, Treemap, Waterfall, KPI, Decomposition Tree, Drill-through, Tooltips, Bookmarks, Buttons, Slicers

Data storytelling: What happened? Why? Where? Who/What? What next?

🐍 STEP 11 — Python for Data Analysis

⏱️ Time: 3–4 weeks

Basics: Variables, Data Types, Lists, Tuples, Sets, Dicts, If/Else, Loops, Functions, Lambda, Exception Handling

NumPy: Arrays, Indexing, Vectorization, Math operations

Pandas: DataFrame, Series, read_csv(), read_excel(), head(), info(), describe(), loc[], iloc[], groupby(), merge(), concat(), pivot_table(), sort_values(), drop_duplicates(), fillna(), dropna(), apply()

Visualization: Matplotlib, Seaborn: Bar, Line, Histogram, Scatter, Box, Heatmap

🎯 Python Project

Customer Sales & Churn Analysis: Cleaning, EDA, Segmentation, Revenue analysis, Churn patterns, Visuals, Recommendations

🧹 STEP 12 — Data Cleaning

Missing values, duplicates, wrong data types, outliers, inconsistent categories, invalid dates, bad formats, negative values, duplicate transactions, data integrity

Practice in: Excel → Power Query → SQL → Python

🏢 STEP 13 — Business & Domain Knowledge

Sales: Revenue, AOV, Conversion Rate, Growth, Gross Margin

Marketing: CAC, CTR, CPC, ROAS, Retention

Product: DAU, MAU, Retention, Churn, Activation, Engagement

Finance: Revenue, Profit, EBITDA, Cost, Margin, Budget vs Actual, Forecast

Operations: SLA, Productivity, Turnaround Time, Error Rate, Capacity, Utilization

🤖 STEP 14 — AI for Data Analysts in 2026

Use AI for: SQL help, DAX help, Excel formulas, Python debugging, Data cleaning, Documentation, Storytelling, Root-cause analysis, Hypotheses, Analysis plans

Limitations: Hallucinations, Incorrect SQL, Wrong assumptions, Data privacy, Poor context

Mindset: AI augments analysts, doesn't replace thinking

☁️ STEP 15 — Cloud & Data Platforms

Azure, AWS, Google Cloud, Databricks, Snowflake

Concepts: Data warehouse, Data lake, Lakehouse, ETL, ELT, Pipelines, Batch processing, APIs