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Data Science Roadmap
Phase 1: Foundations (0–2 Months)
👉 Goal: Learn the basics needed for Data Science.
- Programming:
- Python (preferred) → NumPy, Pandas, Matplotlib.
- R (optional, more statistical).
- Python (preferred) → NumPy, Pandas, Matplotlib.
- Mathematics Basics:
- Statistics (mean, median, variance, probability, distributions).
- Linear Algebra (vectors, matrices).
- Calculus basics (derivatives, gradient).
- Statistics (mean, median, variance, probability, distributions).
- Tools:
- Jupyter Notebook, Google Colab.
- Git & GitHub.
- Jupyter Notebook, Google Colab.
✅ Why? → These are the building blocks for analysis & ML.
Phase 2: Data Analysis & Visualization (2–4 Months)
👉 Goal: Handle and visualize datasets.
- Libraries:
- Pandas (data cleaning, manipulation).
- Matplotlib & Seaborn (visualization).
- Plotly (interactive charts).
- SQL:
- SELECT, JOIN, GROUP BY, aggregate functions.
- Work with large datasets.
- Excel/Sheets (optional but useful).
✅ Projects:
- Sales dashboard.
- Movie ratings analysis.
- COVID-19 trend visualization.
Phase 3: Machine Learning Basics (4–7 Months)
👉 Goal: Predict outcomes using algorithms.
- Scikit-learn: Linear Regression, Logistic Regression, Decision Trees, Random Forest, KNN.
- Concepts:
- Train/Test split, cross-validation.
- Overfitting vs Underfitting.
- Model evaluation metrics (accuracy, precision, recall, F1-score).
- Train/Test split, cross-validation.
- Feature Engineering & Data Preprocessing:
- Handling missing values, scaling, encoding.
- Handling missing values, scaling, encoding.
✅ Projects:
- Predict house prices.
- Spam email classifier.
- Customer churn prediction.
Phase 4: Advanced Machine Learning & Deep Learning (7–12 Months)
👉 Goal: Work on advanced AI problems.
- Deep Learning (with TensorFlow / PyTorch):
- Neural Networks basics (ANN, CNN, RNN).
- Image recognition.
- NLP (Natural Language Processing) → text classification, sentiment analysis.
- Neural Networks basics (ANN, CNN, RNN).
- Unsupervised Learning:
- Clustering (K-Means, DBSCAN).
- Dimensionality reduction (PCA).
- Clustering (K-Means, DBSCAN).
✅ Projects:
- Image classifier (cats vs dogs).
- Sentiment analysis (Twitter data).
Recommender system (like Netflix).
Phase 5: Deployment & Real-World Skills (1–2 Years)
👉 Goal: Take projects to production.
- Model Deployment:
- Flask / FastAPI → deploy ML models as APIs.
- Streamlit / Dash → build data apps.
- Flask / FastAPI → deploy ML models as APIs.
- Big Data & Cloud:
- Hadoop, Spark basics.
- AWS/GCP/Azure ML services.
- Hadoop, Spark basics.
- MLOps:
- CI/CD for ML, monitoring models in production.
- CI/CD for ML, monitoring models in production.
✅ Projects:
- Deploy a sentiment analysis API.
- Build a dashboard with live data.
Real-time fraud detection system.
Phase 6: Mastery (2–5 Years)
👉 Goal: Senior Data Scientist.
- Specialize in:
- Computer Vision
- NLP
- Reinforcement Learning
- Computer Vision
- Research Papers & Open Source Contributions.
Leadership: Mentor juniors, work on large datasets, collaborate with teams.
🚀 Example 90-Day Plan
Since you can study 2 hrs daily & 4 hrs Sunday:
Days 1–30:
- Python (NumPy, Pandas, Matplotlib).
- Basic statistics & probability.
- Project: Data cleaning & visualization (Kaggle dataset).
Days 31–60:
- SQL basics.
- Advanced Pandas, Seaborn, Plotly.
- Project: Build a Sales Dashboard.
Days 61–90:
- Intro to Machine Learning (Scikit-learn).
- Work on 2 ML projects (House Price Prediction + Spam Detection).
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