🚀 Data Analytics Roadmap (Beginner → Advanced)
1. Understand the Fundamentals
✔ What is Data Analytics?
✔ Types: Descriptive, Diagnostic, Predictive, Prescriptive
✔ Basic terms: data types, variables, metrics, KPIs
Resources: YouTube basics, free courses (Coursera/Google DA)
2. Learn Excel / Google Sheets
This is the foundation tool for analysts.
What to learn:
- Formulas: SUM, AVERAGE, COUNTIF, VLOOKUP/XLOOKUP
- Pivot Tables
- Charts & Dashboards
- Data cleaning basics
3. Learn SQL (Most Important Skill)
SQL is used in almost every analytics job.
Topics:
- SELECT, WHERE, ORDER BY
- GROUP BY + Aggregations
- JOINS (INNER, LEFT, RIGHT)
- Subqueries & CTEs
- Window Functions (advanced)
4. Learn a Programming Language (Python)
Used for automation, data cleaning, ML, etc.
Topics:
- Python basics: variables, loops, functions
- Libraries:
- Pandas (data manipulation)
- NumPy (arrays & calculations)
- Matplotlib/Seaborn (visualization)
5. Data Visualization / BI Tools
Choose at least one:
- Power BI (popular in India)
- Tableau
- Looker Studio
Learn:
- Creating dashboards
- DAX (for Power BI)
- Storytelling with data
6. Statistics for Data Analytics
Critical for understanding data.
Key topics:
- Mean, median, mode
- Probability basics
- Correlation & regression
- Hypothesis testing
- A/B testing
7. Real Projects (Very Important)
Build a portfolio on GitHub or Kaggle.
Example projects:
- Sales dashboard
- HR analytics
- Customer churn analysis
- E-commerce data exploration
8. Learn Data Cleaning & ETL Concepts
- Handling missing values
- Outliers
- Data pipelines
- Using tools like SQL, Python, Power Query
9. Optional (For Advanced Profiles)
- Machine Learning
- Cloud tools: AWS, Azure, GCP
- Big data: Spark, Hadoop
📌 Estimated Timeline (Beginner-Friendly)
| Skill | Duration |
|---|---|
| Fundamentals | 1–2 weeks |
| Excel | 2–3 weeks |
| SQL | 1–1.5 months |
| Python | 1 month |
| BI Tools | 1 month |
| Statistics | 2–3 weeks |
| Projects & Portfolio | Continuous |
📚 Want a Personalized
Roadmap?
Tell me:
- Your current level (beginner /
- intermediate). Here is a more detailed and expanded roadmap with step-by-step topics, resources, projects, and tips.
🔥 Full Detailed Roadmap for Data Analytics (Expanded Version)
📌 1. Foundations & Mindset
Learn:
- What is data? Structured vs unstructured
- What is Data Analytics & its lifecycle
- Data → Information → Insights → Decisions
- Roles: Data Analyst vs Business Analyst vs Data Scientist
- Common industries that hire analysts
Output:
✔ Clear understanding of the field
✔ You know exactly what skills are needed
📌 2. Excel / Google Sheets (Strong Foundation)
Learn these topics in order:
✦ Formulas
- SUMIF / COUNTIF / AVERAGEIF
- IF, AND, OR, NOT
- VLOOKUP, XLOOKUP, INDEX–MATCH
- TEXT functions (LEFT, RIGHT, MID)
- DATE functions
✦ Data Cleaning
- Remove duplicates
- Trim spaces
- Text-to-columns
- Handling missing values
✦ Pivot Tables
- Grouping
- Filtering
- Calculations
- Slicers
✦ Dashboards
- Charts
- KPI cards
- Formatting
Mini Projects:
- Sales analysis dashboard
- HR employee turnover dashboard
📌 3. SQL (The Heart of Data Analytics)
Learn in this sequence:
✦ SQL Basics
- SELECT
- WHERE
- ORDER BY
- DISTINCT
✦ Intermediate
- JOINS (INNER, LEFT, RIGHT, FULL)
- GROUP BY
- HAVING
- Aggregate functions
✦ Advanced
- Subqueries
- CTEs (With clause)
- Window functions (ROW_NUMBER, RANK, LEAD, LAG)
- Case statements
SQL Projects:
- E-commerce sales analysis
- Customer segmentation using SQL
- Create dashboards using SQL + Power BI
📌 4. Python for Data Analysis
Learn enough for real work, not full programming.
✦ Python Fundamentals
- Variables, loops, conditions
- Lists, dicts, tuples
✦ Data Libraries
- Pandas → Data cleaning, manipulation
- NumPy → Calculations
- Matplotlib / Seaborn → Charts
✦ Special Topics
- Reading data (CSV, Excel, JSON)
- GroupBy operations
- Merging & joining datasets
- Time series basics
Python Projects:
- Customer churn analysis
- Stock price analysis
- Movie ratings analysis
- Bank loan prediction (basic ML)
📌 5. BI Tools (Power BI / Tableau)
Choose one. Power BI is most common in India.
✦ Learn:
- Importing data
- Data modeling
- Creating relationships
- DAX formulas (Power BI)
- KPIs, slicers, drill-downs
- Interactions and filters
Dashboards you can build:
- Sales dashboard
- Finance dashboard
- Marketing KPI dashboard
- HR analytics dashboard
📌 6. Statistics for Data Analytics
You don’t need deep math—just the practical parts.
✦ Key Topics:
- Mean, median, mode
- Standard deviation, variance
- Probability basics
- Correlation vs causation
- Regression
- Outliers
- Hypothesis testing
- A/B testing
Mini Projects:
- Marketing A/B test analysis
- Revenue forecasting with regression
📌 7. Data Cleaning & EDA (Exploratory Data Analysis)
This is where most of the real work happens.
Learn:
- Handling missing data
- Handling outliers
- Feature selection
- Visualizing patterns
- Asking the right questions
Tools:
- SQL
- Excel
- Python
- Power Query
📌 8. Build Portfolio Projects (Very Important for Jobs)
Your portfolio should have at least 5 projects:
Recommended Projects:
- 🛒 E-commerce Sales Dashboard (Power BI)
- 🧑💼 HR Analytics — Attrition Analysis (Excel + SQL)
- 📊 Financial Data Analysis
- 📈 Customer Churn Analysis (Python)
- 🚌 Travel data/Flight delay analysis
Upload to:
- GitHub
- Kaggle
📌 9. Develop Business Understanding
Companies hire analysts who understand business.
Learn domain knowledge:
- Sales
- Marketing
- Finance
- Supply Chain
- HR
Learn KPIs:
- Revenue, profit, margin
- CAC, ROAS
- Churn
- Inventory turnover
📌 10. Soft Skills & Communication (Very Important)
- Storytelling with data
- Writing summaries
- Presenting dashboards
- Explaining insights to non-technical people
📌 Complete Timeline (Detailed)
| Stage | Duration |
|---|---|
| Excel | 3–4 weeks |
| SQL | 1–2 months |
| Python | 1–1.5 months |
| Power BI / Tableau | 1 month |
| Statistics | 3–4 weeks |
| Projects & Portfolio | Ongoing |
| Job preparation | 3–5 weeks |
Total: 5–7 months to become job-ready.
📥 Want me to create a custom roadmap based on:
✔ Your background
✔ Daily hours
✔ Career goal
✔ Skills you know
Tell me the details and I’ll tailor it for you.
- How much time you can study per day
- Your career goal (Data Analyst / Business Analyst / ML path)
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