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12-Week Program

Become a Data Analyst
by August

A structured roadmap covering the 6 core tools every data analyst needs — from spreadsheets to code.

6
Tools to Master
202
Total Tasks
80%
Hands-on Practice
12
Weeks
Your Learning Path
📊

Excel

Foundation of data work — formulas, pivot tables, charts, and data cleaning basics.

Week 1–2
32 tasks
📗

Google Sheets

Cloud-based collaboration, IMPORTRANGE, QUERY function, and real-time dashboards.

Week 3–4
21 tasks
🗄️

SQL

Query, filter, join, and aggregate data from databases — the backbone of data analysis.

Week 5–6
45 tasks
📈

Tableau

Interactive dashboards and visual storytelling — connect data and drag-and-drop insights.

Week 7–8
37 tasks
⚡

Power BI

Microsoft's BI tool — DAX measures, data models, and enterprise-grade reporting.

Week 9–10
26 tasks
🐍

Python

Automate analysis, handle big data, and build reproducible pipelines with pandas & matplotlib.

Week 11–12
41 tasks
📊

Excel

Week 1–2 · 32 tasks · Foundation for small-to-medium data

Core Topics
01

Essential Formulas

SUM, AVERAGE, IF, COUNTIF, VLOOKUP, INDEX/MATCH

02

Pivot Tables

Summarize thousands of rows instantly with drag-and-drop grouping

03

Data Cleaning

Remove duplicates, TRIM, TEXT functions, find & replace patterns

04

Charts & Visualization

Bar, line, pie, scatter — choosing the right chart for your data

05

Advanced Excel (Advance)

XLOOKUP, dynamic arrays, LAMBDA, Power Query basics

06

Conditional Formatting

Highlight cells, heat maps, data bars, icon sets

Example — VLOOKUP vs INDEX/MATCH
-- Old way: VLOOKUP (column number is fragile) =VLOOKUP(A2, Products!A:D, 3, 0) -- Better: INDEX/MATCH (column by name, not position) =INDEX(Products!C:C, MATCH(A2, Products!A:A, 0)) -- Modern Excel: XLOOKUP (cleanest, handles not-found) =XLOOKUP(A2, Products!A:A, Products!C:C, "Not found")
Practice Checklist
Build a sales report with SUM, AVERAGE, and IF formulas
Create a pivot table summarizing revenue by region and month
Use VLOOKUP then rewrite it with INDEX/MATCH
Clean a messy dataset: remove duplicates, fix spacing with TRIM
Build a dashboard with 3 charts linked to one pivot table
Apply conditional formatting to a KPI scorecard
💡

Pro tip: Learn keyboard shortcuts early. Ctrl+Shift+L (filter), Ctrl+T (table), Alt+Enter (new line in cell). Analysts who use shortcuts work 30% faster.

📗

Google Sheets

Week 3–4 · 21 tasks · Collaboration & cloud-native data work

Core Topics
01

QUERY Function

Write SQL-like queries directly inside a cell — the Sheets superpower

02

IMPORTRANGE

Pull data from other spreadsheets in real-time across your organization

03

Collaboration Features

Comments, version history, sharing permissions, and protect ranges

04

ARRAYFORMULA

Apply formulas to entire columns without copying down — auto-expanding

Example — QUERY Function
-- Pull rows where Sales > 1000, sorted by date =QUERY( SalesData!A:E, "SELECT A, B, C, D WHERE D > 1000 ORDER BY A DESC LIMIT 100", 1 -- 1 = first row is header ) -- Pull from another sheet with IMPORTRANGE =QUERY( IMPORTRANGE("spreadsheet_url", "Sheet1!A:E"), "SELECT Col1, Col3 WHERE Col4 > 500" )
Practice Checklist
Use QUERY to filter and sort a dataset without pivot tables
Connect two sheets with IMPORTRANGE and build a live summary
Set up sharing with edit/view-only access for different teammates
Build a dynamic dashboard using ARRAYFORMULA + charts
💡

Key difference from Excel: Google Sheets recalculates in real-time and is built for teams. Use it when multiple people need to see live data — use Excel when you need advanced formulas or huge datasets.

🗄️

SQL

Week 5–6 · 45 tasks · Build and query databases easily

Core Topics
01

SELECT & Filtering

SELECT, WHERE, LIKE, IN, BETWEEN, IS NULL

02

Aggregations

COUNT, SUM, AVG, MIN, MAX with GROUP BY and HAVING

03

JOINs

INNER JOIN, LEFT JOIN, RIGHT JOIN — connecting tables by key

04

Subqueries & CTEs

WITH clauses to break complex queries into readable steps

05

Window Functions

ROW_NUMBER, RANK, LAG/LEAD, running totals

06

Database Design

CREATE TABLE, primary/foreign keys, indexes, normalization

Example — Sales Analysis Query
-- Monthly revenue with running total WITH monthly_sales AS ( SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS revenue FROM orders JOIN customers ON orders.customer_id = customers.id WHERE status = 'completed' GROUP BY 1 ) SELECT month, revenue, SUM(revenue) OVER (ORDER BY month) AS running_total FROM monthly_sales ORDER BY month;
Practice Checklist
Query a sample database: filter customers by country and status
Aggregate sales by product category with GROUP BY + HAVING
Join 3 tables: orders + customers + products
Write a CTE to simplify a multi-step analysis
Use ROW_NUMBER to rank top 10 customers per region
Design and create a simple 3-table database schema
💡

Free practice: Use SQLiteOnline.com or Mode Analytics SQL Tutorial — no setup needed. Start with SELECT → WHERE → GROUP BY → JOIN in that order. Don't skip JOINs; they're used in 80% of real analyst work.

📈

Tableau

Week 7–8 · 37 tasks · Fast, popular dashboard tool for storytelling

Core Topics
01

Connecting Data

Live vs extract, connecting Excel, CSV, databases, Google Sheets

02

Dimensions vs Measures

Understanding the blue/green pill system that drives every chart

03

Chart Types

Bar, line, scatter, map, treemap, heat map — when to use each

04

Calculated Fields

Write Tableau formulas: IF, LOD expressions, date math

05

Filters & Parameters

Context filters, dashboard filters, dynamic parameters for interactivity

06

Publishing

Tableau Public for sharing, Tableau Server/Cloud for organizations

Key Calculated Field Examples
// Profit Ratio calculated field SUM([Profit]) / SUM([Sales]) // Classify customers by spend tier IF SUM([Sales]) > 10000 THEN "High Value" ELSEIF SUM([Sales]) > 1000 THEN "Mid Value" ELSE "Low Value" END // LOD — average sales per customer (ignores viz level) { FIXED [Customer ID] : SUM([Sales]) }
Practice Checklist
Connect the Superstore sample dataset and build 5 basic charts
Create a dual-axis chart combining bar and line
Write a calculated field for profit margin %
Build a dashboard with 3 charts + filter actions between them
Publish a workbook to Tableau Public
💡

Start here: Download Tableau Public (free forever). Use the built-in Superstore dataset for all practice. The "Show Me" panel suggests chart types — use it to learn, then graduate to building from scratch.

⚡

Power BI

Week 9–10 · 26 tasks · Professional data presentation, easy to understand

Core Topics
01

Power Query (M Language)

ETL inside Power BI — reshape, clean, and merge data before it loads

02

Data Modeling

Star schema, relationships, fact vs dimension tables

03

DAX Basics

CALCULATE, SUMX, FILTER — the formula language for measures

04

Visuals & Reports

Cards, slicers, matrix, drill-through, bookmarks, page navigation

05

Row-Level Security

Restrict what different users see — critical for enterprise deployment

06

Publishing & Service

Publish to Power BI Service, schedule refresh, share with stakeholders

DAX Measure Examples
-- Basic measure Total Revenue = SUM(Sales[Amount]) -- CALCULATE to change filter context Revenue Last Year = CALCULATE( [Total Revenue], SAMEPERIODLASTYEAR(Dates[Date]) ) -- YoY Growth % YoY Growth % = DIVIDE( [Total Revenue] - [Revenue Last Year], [Revenue Last Year], 0 )
Practice Checklist
Load a CSV with Power Query and clean it (rename columns, remove nulls)
Build a star schema: link fact table to 3 dimension tables
Write DAX measures: Total Sales, Avg Order Value, YoY Growth
Build a 1-page executive dashboard with KPI cards + charts
Publish and share the report via Power BI Service
💡

Power BI vs Tableau: If your company uses Microsoft 365, Power BI integrates tightly and is more cost-effective. Tableau has a better UX for exploratory analysis. Many analysts know both — start with whichever your employer uses.

🐍

Python

Week 11–12 · 41 tasks · Automate and scale your data work systematically

Core Topics
01

Python Fundamentals

Variables, lists, dicts, loops, functions — just enough to work with data

02

pandas

DataFrames, filtering, groupby, merge, apply — Python's Excel equivalent

03

Data Visualization

matplotlib for basics, seaborn for statistical plots, plotly for interactive

04

File Handling

Read/write CSV, Excel, JSON; fetch data from APIs; connect to databases

05

Automation

Schedule scripts, automate reports, batch-process files, send emails

06

Intro to Statistics

Distributions, correlation, regression with scipy and statsmodels

Example — Sales Analysis with pandas
import pandas as pd import matplotlib.pyplot as plt # Load data df = pd.read_csv('sales.csv', parse_dates=['date']) # Clean: drop nulls, filter current year df = df.dropna(subset=['amount']) df = df[df['date'].dt.year == 2025] # Aggregate revenue by month monthly = ( df.groupby(df['date'].dt.to_period('M'))['amount'] .sum() .reset_index() ) # Plot monthly.plot(x='date', y='amount', kind='bar') plt.title('Monthly Revenue 2025') plt.tight_layout() plt.savefig('revenue.png')
Practice Checklist
Set up Python + Jupyter Notebook (use Anaconda for easy install)
Load a CSV with pandas, inspect shape, dtypes, missing values
Filter, group, and pivot data — replicate an Excel pivot in pandas
Plot 3 chart types: bar, line, scatter using matplotlib/seaborn
Fetch data from a public API (e.g., exchange rates) and analyze it
Automate a weekly Excel report: read → analyze → write new file
Portfolio project: end-to-end analysis from raw data to final chart
💡

Learning path: Python feels hard for non-programmers at first. Focus on pandas only for the first week — it's 90% of analyst work. Use Google Colab (free, no setup) to start. Don't try to learn all of Python; learn what you need for data.