Notes – Learning Roadmap of Data Science
This roadmap is designed to help you move from zero knowledge to job-ready level in Data Science. Follow each stage in sequence and practice consistently to build real-world skills.
Stage 1: Foundations (Week 1–3)
Goal: Build a strong base in programming, statistics, and data handling.
What to Learn:
- Python Programming Basics (syntax, variables, loops, functions)
- Core Python Libraries: NumPy, Pandas
- Basic Statistics (mean, median, mode, standard deviation)
- Introduction to Data Science, roles, and tools
- Excel for data analysis
Practice:
- Simple Python projects like calculator, data summarizer
- Analyze sample datasets (CSV files) using Pandas
Stage 2: Data Analysis & Visualization (Week 4–6)
Goal: Learn how to explore, clean, and visualize data.
What to Learn:
- Data Cleaning and Preprocessing with Pandas
- Exploratory Data Analysis (EDA)
- Visualization Libraries: Matplotlib, Seaborn
- Handling missing values, duplicates, and outliers
Practice:
- Create visual reports (bar charts, histograms, scatter plots)
- Work on datasets like Titanic, IPL Stats, or COVID-19 data
Stage 3: Databases & SQL (Week 7–8)
Goal: Learn how to work with structured data stored in databases.
What to Learn:
- SQL Basics (SELECT, WHERE, GROUP BY)
- Joins and Nested Queries
- Connecting SQL with Python using libraries like
sqlite3
Practice:
- Solve SQL queries on platforms like Hackerrank or Mode Analytics
- Build a mini project to fetch and analyze data from a database
Stage 4: Statistics & Probability (Week 9–10)
Goal: Understand the math behind data analysis and machine learning.
What to Learn:
- Probability distributions (normal, binomial)
- Correlation and covariance
- Hypothesis testing
- Central limit theorem
Practice:
- Solve statistics-based case studies
- Use real data to apply statistical formulas
Stage 5: Machine Learning Basics (Week 11–14)
Goal: Learn to build simple predictive models.
What to Learn:
- Supervised Learning: Linear Regression, Logistic Regression, Decision Trees
- Unsupervised Learning: K-Means Clustering, PCA
- Model Evaluation: Accuracy, Precision, Recall, Confusion Matrix
- Introduction to Scikit-learn
Practice:
- Build ML models for predicting house prices, student performance, or churn
- Apply cross-validation and hyperparameter tuning
Stage 6: Real-World Projects (Week 15–18)
Goal: Build a portfolio of practical projects.
Project Ideas:
- Sentiment analysis on tweets
- Sales forecasting for a store
- Fraud detection using transaction data
- Recommendation system for movies/products
Deliverables:
- Host projects on GitHub
- Write project summaries on LinkedIn or personal blog
- Make a clean and updated resume
Stage 7: Career Prep (Week 19–20)
Goal: Get ready for interviews and job applications.
What to Focus On:
- Revise Python, ML, and SQL interview questions
- Practice mock interviews
- Prepare a strong GitHub portfolio and LinkedIn profile
- Apply for internships and entry-level roles (Data Analyst, Junior Data Scientist)
Final Checklist: Job-Ready Skills
| Area | Must Have |
|---|---|
| Python (Pandas, NumPy) | Yes |
| SQL | Yes |
| Statistics & Probability | Yes |
| Data Visualization | Yes |
| Machine Learning Basics | Yes |
| Project Portfolio | Yes |
| Communication Skills | Yes |
