This roadmap follows a semester-wise career plan for B.Tech Data Science students: build C and Python foundations, then SQL, Jupyter, EDA, and visualization, advance through ML, deep learning, and time series, and finish with big data (Hadoop, Spark), cloud data science, and deployment (Flask/FastAPI, Docker).
Align GATE / core academics with hands-on Kaggle-style and portfolio projects, competitive aptitude, and certifications (for example IBM Data Science, TensorFlow Developer, AWS ML Specialty) toward internship and placement goals.
Semester-Wise Table (1st to 8th)
| Semester | Programming / Development | GATE / Core Subjects | Projects / Activities | Aptitude / Competitive Prep | Output Goal |
|---|---|---|---|---|---|
| 1st Sem | Strong in C, Python basics, Gen AI tools (text). | Discrete Mathematics, C Programming. | Create GitHub account, upload programs, participate in coding contests. | Number systems, percentages, ratio & proportion. | 100 coding problems + strong programming basics. |
| 2nd Sem | C++ (OOP), Python libraries (NumPy, Matplotlib basics), GitHub basics. | Digital Logic Design, Linear Algebra (vectors, matrices, eigenvalues). | Small data plotting projects, coding contests. | Time & work, profit & loss, logical reasoning. | Understand OOP + 150 coding problems. |
| 3rd Sem | Python advanced, Pandas, NumPy, SQL basics, Jupyter Notebook. | Data Structures, Probability & Statistics. | Hackathon-1, Paper Presentation-1, portfolio website. | Data interpretation, puzzles. | Portfolio website + DSA basics. |
| 4th Sem | Data cleaning, EDA, SQL advanced, data visualization (Seaborn, Plotly), Tableau basics. | Algorithms, Computer Organization, Operating Systems. | Hackathon-2, Paper Presentation-2, EDA project on a Kaggle dataset. | Reasoning practice. | 200–300 coding problems + EDA project. |
| 5th Sem | Machine Learning basics (Scikit-Learn), feature engineering, model evaluation. | Theory of Computation, DBMS. | Hackathon-3, poster presentation, ML model on a Kaggle dataset. | Advanced aptitude, mock tests. | 2–3 strong ML projects + 350 coding problems. |
| 6th Sem | Deep Learning basics (TensorFlow / PyTorch), NLP basics, time series analysis. | Compiler Design, OS revision. | Mini Project-1, Research Paper-1, Certification-1 (e.g., IBM Data Science). | Competitive exam practice tests. | Internship search + 450 coding problems. |
| 7th Sem | Big Data (Hadoop, Spark), cloud DS (AWS SageMaker / GCP), MLOps basics. | Computer Networks, GATE revision. | Mini Project-2, Research Paper-2, Certification-2 (e.g., TensorFlow Developer). | Government exam mock tests. | Internship / placement preparation. |
| 8th Sem | Model deployment (Flask/FastAPI), Docker, SQL & NoSQL databases, advanced ML projects. | Full GATE preparation + mock tests. | Major Data Science project, portfolio polishing. | Final competitive exam preparation. | 600+ coding problems + job readiness. |
Strong Data Science Project Themes
Target 4–6 strong Data Science projects aligned with this roadmap—for example:
- EDA and storytelling on public or Kaggle datasets
- End-to-end ML pipelines and model evaluation on tabular or text data
- Time series forecasting or analytics projects
- Big data processing with Hadoop / Spark
- Deployed APIs (Flask or FastAPI) with Docker; SQL and NoSQL in production-style setups
- Major capstone Data Science project for your portfolio
See also project ideas on Engineers Hub.
Expected Outcome by Graduation
- 600+ coding problems solved
- 4–6 strong Data Science projects
- 2 research papers (for example on ML or predictive modeling)
- 1–2 certifications (for example IBM Data Science, TensorFlow Developer, AWS ML Specialty)
- Strong GATE preparation (CS/IT or DA)
- Portfolio + GitHub ready
- Placement-ready resume