Here’s a 21-day step-by-step breakdown for learning data analytics — structured so that by the end,
you’ll have both the theory and hands-on skills to analyze real data confidently.
Week 1 – Foundations (Days 1–7)
Goal: Understand what data analytics is, learn basic tools, and prepare your environment.
Day 1: Introduction to Data Analytics
What is data analytics? Types (descriptive, diagnostic, predictive, prescriptive).
Real-world applications (business, healthcare, finance, etc.).
Key tools: Excel, SQL, Python, Power BI/Tableau.
Day 2: Data Types & Sources
Structured vs unstructured data.
Qualitative vs quantitative data.
Where data comes from (databases, APIs, files, surveys).
Day 3: Data Collection Methods
Surveys, sensors, transaction logs, APIs.
Ethical considerations in data collection.
Day 4: Data Cleaning Basics (Excel)
Removing duplicates.
Handling missing values.
Formatting data for analysis.
Day 5: Introduction to Excel Formulas for Analysis
SUM, AVERAGE, COUNT, IF, VLOOKUP/XLOOKUP.
Basic percentage and ranking calculations.
Day 6: Data Visualization Basics (Excel Charts)
Bar chart, line chart, pie chart.
Choosing the right chart for your data.
Day 7: Mini Project #1 – Sales Data Summary in Excel
Clean raw sales data.
Create summary tables and visual charts.
Week 2 – Core Analytics Skills (Days 8–14)
Goal: Learn statistical basics, SQL queries, and deeper analysis techniques.
Day 8: Basic Statistics for Analytics
Mean, median, mode, variance, standard deviation.
How to interpret them in business scenarios.
Day 9: Introduction to SQL for Data Retrieval
SELECT, WHERE, ORDER BY, LIMIT.
Filtering data from a database.
Day 10: SQL Joins & Aggregations
INNER JOIN, LEFT JOIN.
GROUP BY, SUM, COUNT, AVG.
Day 11: Data Cleaning with SQL
Removing nulls.
Formatting text and numbers.
Day 12: Introduction to Python for Data Analytics (Pandas)
Loading datasets (CSV, Excel).
Basic data inspection: head(), describe(), info().
Day 13: Data Manipulation with Python
Filtering rows, selecting columns.
Creating new calculated fields.
Day 14: Mini Project #2 – Customer Database Analysis
Use SQL to find top customers, sales by region, and trends.
Week 3 – Advanced Tools & Real Projects (Days 15–21)
Goal: Build dashboards, interpret data, and create reports.
Day 15: Introduction to Power BI / Tableau
Connecting to a dataset.
Creating simple dashboards.
Day 16: Dashboard Design Best Practices
Choosing visuals.
Making dashboards interactive.
Day 17: Advanced Excel Analytics
PivotTables, PivotCharts.
Conditional formatting for insights.
Day 18: Data Storytelling & Insight Communication
Turning numbers into insights.
Writing short analysis summaries.
Day 19: Case Study Analysis
Work on a real dataset (e.g., retail sales, social media engagement).
Identify trends and anomalies.
Day 20: Final Project – Full Analytics Workflow
Collect → Clean → Analyze → Visualize → Present findings.
Day 21: Review & Portfolio Building
Save all projects in a portfolio folder.
Create a short presentation of your final project.
If you want, I can also prepare a ready-to-use Excel + SQL + Power BI project set so you can follow this
21-day plan with real-life data instead of theory only. That way you learn faster through practice.
Do you want me to prepare that practice dataset for you?
