Comprehensive Learning Roadmap for Data Science
Phase 1: Foundational Skills
- Mathematics & Statistics
- Topics:
- Linear Algebra (vectors, matrices, eigenvalues)
- Calculus (derivatives, integrals)
- Probability (distributions, Bayes’ theorem)
- Statistics (hypothesis testing, regression, descriptive/inferential stats)
- Resources:
- 3Blue1Brown YouTube series for linear algebra
- Coursera’s “Statistics with R” (Duke University)
- Book: “Introduction to Statistical Learning” (James et al.)
- Programming
- Languages: Python (preferred) or R.
- Key Skills:
- Syntax, data structures (lists, dictionaries), control flow, functions.
- Libraries: NumPy (numerical computing), Pandas (data manipulation).
- Tools: Jupyter Notebook, Git/GitHub (version control).
- Resources:
- Coursera’s “Python for Everybody” (University of Michigan)
- Book: “Python Crash Course” (Eric Matthes)
Phase 2: Data Manipulation & Analysis
- SQL & Databases
- Topics: Querying, joins, aggregations, database design.
- Tools: PostgreSQL, MySQL.
- Resources:
- Mode Analytics SQL Tutorial
- Book: “SQL Cookbook” (Anthony Molinaro)
- Data Cleaning & Preprocessing
- Skills: Handling missing data, outliers, data normalization.
- Tools: Pandas, OpenRefine.
- Project: Clean a messy dataset (e.g., Kaggle’s Titanic dataset).
Phase 3: Data Visualization
- Tools & Techniques
- Libraries: Matplotlib, Seaborn, Plotly (Python); ggplot2 (R).
- BI Tools: Tableau, Power BI.
- Project: Create interactive dashboards for COVID-19 data.
- Resources:
- Coursera’s “Data Visualization with Python” (IBM)
- Tableau Public tutorials.
Phase 4: Machine Learning (ML)
- Core Concepts
- Algorithms:
- Supervised (Linear Regression, Decision Trees, SVM).
- Unsupervised (K-Means, PCA).
- Model Evaluation: Metrics (accuracy, F1-score, ROC-AUC), cross-validation.
- Libraries: Scikit-learn, XGBoost.
- Resources:
- Coursera’s “Machine Learning” (Andrew Ng)
- Book: “Hands-On ML with Scikit-Learn & TensorFlow” (Aurélien Géron).
- Advanced ML
- Ensemble Methods: Random Forests, Gradient Boosting.
- NLP: Tokenization, TF-IDF, word embeddings (Word2Vec).
- Project: Predict housing prices (Kaggle) or build a spam classifier.
Phase 5: Advanced Topics
- Deep Learning
- Frameworks: TensorFlow, PyTorch.
- Concepts: Neural Networks, CNNs, RNNs, transfer learning.
- Project: Image classification with CIFAR-10 dataset.
- Resources:
- Fast.ai courses
- Book: “Deep Learning for Coders” (Jeremy Howard).
- Big Data Tools
- Tools: Apache Spark (PySpark), Hadoop.
- Cloud Platforms: AWS (S3, EC2), Google Cloud (BigQuery).
- Project: Process large datasets using Spark on AWS.
Phase 6: Deployment & Production
- Model Deployment
- Tools: Flask/Django (APIs), Docker (containerization), Heroku/AWS (deployment).
- Project: Deploy a fraud detection model as a web API.
- MLOps
- CI/CD Pipelines: GitHub Actions, Jenkins.
- Monitoring: MLflow, Kubeflow.
Phase 7: Real-World Projects & Portfolio
- Kaggle Competitions: Participate in trending competitions (e.g., Titanic, House Prices).
- Personal Projects: End-to-end projects (e.g., customer churn analysis).
- Portfolio: Showcase work on GitHub, LinkedIn, or a personal blog.
Phase 8: Soft Skills & Continuous Learning
- Communication: Present insights using tools like PowerPoint/Tableau.
- Networking: Join communities (Kaggle, Reddit’s r/datascience).
- Stay Updated: Follow blogs (Towards Data Science, KDnuggets), podcasts (Data Skeptic).
Example Timeline (12-18 Months)
- Months 1-3: Math, Python, SQL, Pandas.
- Months 4-6: Visualization, ML basics, Kaggle projects.
- Months 7-9: Advanced ML, Deep Learning.
- Months 10-12: Big Data, Deployment, Portfolio building.
Key Tips
- Consistency: Code daily and revisit concepts.
- Community: Engage in forums and meetups.
- Adaptability: Stay open to new tools (e.g., ChatGPT for code assistance).
This roadmap balances theory, tools, and hands-on practice, preparing you for roles like Data Analyst, ML Engineer, or Data Scientist.