Data Science Roadmap 2026 — Complete Career Path

Why Follow a Data Science Roadmap?

Data science is one of the fastest-growing careers in India. But without a structured roadmap, most beginners waste months jumping between random tutorials. This guide gives you a clear, project-first path — the same one we teach at DSWallah in Lucknow.

Phase 1: Python Fundamentals (Weeks 1–4)

Python is the backbone of data science. Start with:

• Variables, data types, loops, functions
• Lists, dictionaries, sets, tuples
• File handling and error handling
• OOP basics (classes, inheritance)
• Virtual environments and pip

Project: Build a web scraper that collects data from a public API and saves it to CSV.

Phase 2: SQL & Data Access (Weeks 5–7)

Every data scientist needs SQL. You'll use it daily to extract, clean, and analyze data:

• SELECT, WHERE, GROUP BY, ORDER BY
• JOINs (INNER, LEFT, RIGHT, FULL)
• Window Functions and CTEs
• Subqueries and stored procedures
• Query optimization basics

Project: Analyze a 10,000-row sales dataset using only SQL — find top products, seasonal trends, and customer segments.

Phase 3: Data Analysis & Visualization (Weeks 8–10)

Turn raw data into insights:

• Pandas for data manipulation
• NumPy for numerical computing
• Matplotlib & Seaborn for visualization
• Power BI for interactive dashboards
• Exploratory Data Analysis (EDA)

Project: Build an interactive Power BI dashboard analyzing customer behavior for an e-commerce dataset.

Phase 4: Statistics & Probability (Weeks 11–13)

Statistics is the math behind data science:

• Descriptive statistics (mean, median, mode, std dev)
• Probability distributions
• Hypothesis testing (t-test, chi-square)
• Correlation and regression analysis
• A/B testing fundamentals

Phase 5: Machine Learning (Weeks 14–20)

The core of data science — building predictive models:

• Linear & Logistic Regression
• Decision Trees & Random Forests
• SVM, KNN, Naive Bayes
• Ensemble methods (XGBoost, LightGBM)
• Model evaluation (precision, recall, F1, ROC-AUC)
• Cross-validation and hyperparameter tuning

Project: Build a customer churn prediction model with 85%+ accuracy using XGBoost.

Phase 6: Deep Learning & NLP (Weeks 21–24)

Advanced AI skills for senior roles:

• Neural networks (ANN, CNN, RNN)
• TensorFlow / PyTorch basics
• NLP fundamentals (tokenization, embeddings)
• Computer vision basics
• Transfer learning

Phase 7: GenAI & LLMs (Weeks 25–28)

The 2026 differentiator — every data scientist needs GenAI skills:

• OpenAI API & prompt engineering
• LangChain for LLM applications
• RAG systems (Retrieval-Augmented Generation)
• Vector databases (Pinecone, ChromaDB)
• AI agents and automation

Project: Build a RAG chatbot that answers questions from a company's documentation.

Phase 8: Portfolio & Job Search (Weeks 29–30)

• GitHub portfolio with 6+ projects
• Resume optimization for ATS
• LinkedIn profile optimization
• Mock interviews (technical + behavioral)
• Salary negotiation

Data Science Salary in Lucknow (2026)

Ready to Start?

DSWallah's Data Science course in Lucknow covers this entire roadmap in 3 months with 6+ real projects, 1-on-1 mentorship, and placement support. Fees start from ₹4,999.