Data Analyst / Data Scientist Learning Roadmap(Start → Job → Advanced)
Data Analyst = insights, dashboards, business decisions
Data Scientist = prediction, modeling, machine learning
(Many people start as analysts and grow into scientists.)
PHASE 0: Foundations & Setup (Week 1)
Tools You Need
Laptop (8GB RAM minimum)
Anaconda / Python
VS Code or Jupyter Notebook
Excel / Google Sheets
Git & GitHub (basic)
🎯 Goal: Be able to run Python and analyze a simple dataset.
PHASE 1: Core Math & Statistics (Month 1–2)
You don’t need PhD-level math—only applied understanding.
Statistics (Must-Know)
Mean, median, mode
Variance & standard deviation
Probability basics
Normal distribution
Correlation vs causation
Hypothesis testing
Confidence intervals
Math (Light but Important)
Linear algebra (vectors, matrices – basics)
Calculus (derivatives – concept only)
🎯 Goal: Understand what the numbers mean, not just calculate them.
PHASE 2: Excel & Business Analysis (Month 2–3)
Excel / Sheets Skills
VLOOKUP / XLOOKUP
Pivot tables
Conditional formatting
Charts & dashboards
Data cleaning
Business Thinking
KPIs
Trends
Comparisons
Storytelling with data
🛠 Projects:
Sales performance dashboard
Monthly revenue report
Customer churn analysis (Excel)
🎯 Goal: Become job-ready Data Analyst.
PHASE 3: Python for Data Analysis (Month 3–5)
Python Basics
Variables, loops, functions
Lists, dictionaries
Error handling
Core Libraries
NumPy → numerical computing
Pandas → data cleaning & analysis
Matplotlib & Seaborn → visualization
🛠 Projects:
COVID data analysis
Travel data trends (perfect for ghurtecholo.com)
Customer behavior analysis
🎯 Goal: Analyze real datasets end-to-end in Python.
PHASE 4: SQL & Databases (Month 5–6)
SQL Skills (Very Important)
SELECT, WHERE, GROUP BY
JOINs (INNER, LEFT, RIGHT)
Subqueries
Window functions
Index basics
🛠 Projects:
Analyze sales database
User engagement analysis
Booking data analysis
🎯 Goal: Query large datasets like a professional.
PHASE 5: Data Visualization & BI Tools (Month 6–7)
Learn at least one BI tool:
Power BI or Tableau
Skills:
Interactive dashboards
Filters & drill-downs
Storytelling dashboards
🛠 Projects:
Business performance dashboard
Travel booking analytics dashboard
Marketing campaign analysis
🎯 Goal: Turn numbers into decisions.
PHASE 6: Choose Your Track
🟢 TRACK A: Data Analyst (Job-Ready)
Focus on:
Excel + SQL + Python
Power BI / Tableau
Business insights
Typical jobs:
Data Analyst
Business Analyst
Reporting Analyst
⏱ Time: 6–8 months
🔵 TRACK B: Data Scientist (Advanced Path)
PHASE 7: Machine Learning (Month 7–10)
ML Concepts
Supervised vs Unsupervised learning
Regression & classification
Overfitting & underfitting
Model evaluation
Algorithms
Linear & Logistic Regression
Decision Trees
Random Forest
K-Means
Support Vector Machines
Libraries
Scikit-learn
TensorFlow / PyTorch (later)
🛠 Projects:
Price prediction model
Customer segmentation
Recommendation system
🎯 Goal: Build predictive models.
PHASE 8: Advanced Data Science (Month 10–12)
Advanced Topics
Feature engineering
Time series analysis
NLP (text data)
Basic deep learning
Model deployment (Flask / FastAPI)
🎯 Goal: Build production-ready models.
PHASE 9: Real-World Skills (Critical)
Data Cleaning (80% of the job!)
Missing values
Outliers
Inconsistent data
Data validation
Tools & Practices
Git
Documentation
Reproducibility
Ethics & bias
PHASE 10: Portfolio & Job Preparation
Portfolio Projects (Must-Have)
Sales analytics dashboard
SQL case study
Python EDA project
Machine learning project (for DS)
Where to Publish
GitHub
Kaggle
Personal website (great add-on)
🎯 Goal: Show proof, not certificates.
Interview Preparation
SQL queries
Statistics questions
Case studies
Python logic
ML concepts (for DS)
Common Mistakes to Avoid
❌ Jumping to ML too early
❌ Ignoring Excel & SQL
❌ Learning tools without projects
❌ Certificates without skills
Career Path Progression
Junior Data Analyst → Senior Analyst
Analyst → Data Scientist
Data Scientist → ML Engineer
Analytics Manager / AI Lead
Recommended Daily Study Plan
⏱ 2–3 hours/day:
40% hands-on practice
30% projects
20% learning
10% revision
Final Advice
Data is useless without context. Insights create value.
If you want, I can:
Create a 6-month Data Analyst plan
Make a 12-month Data Scientist plan
Design real portfolio project ideas
Tailor this roadmap for Bangladesh / remote jobs
Build a learning roadmap using free resources only
Just tell me which one you want 🚀 Highest Paying Remote Jobs
