Become a `Data Scientist`
Become a Data Scientist
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Learning Path
Notes:
- Verify resource links closer to your learning period, as availability may change.
- If targeting GAFAM, prioritize steps 21-23 after mastering the core skills.
| Step | Topic/Skill | Explanation | Learning Resources/Tools | Expected Time |
|---|---|---|---|---|
| 1 | Understand Data Science Fundamentals | Learn what Data Science is: extracting insights from data using statistics, programming, and domain expertise. Focus on the data science lifecycle and problem-solving approach. | - Introduction to Data Science - IBM - Data Science Methodology - IBM - Kaggle Learn Data Science | 1-2 weeks |
| 2 | Mathematics & Statistics Foundations | Master descriptive statistics, probability, hypothesis testing, and linear algebra essential for machine learning algorithms and data analysis. | - Khan Academy Statistics - MIT Linear Algebra - StatQuest YouTube Channel | 4-6 weeks |
| 3 | Python Programming for Data Science | Learn Python fundamentals: variables, loops, functions, and data structures. Focus on libraries like NumPy, Pandas for data manipulation. | - Python.org Tutorial - Pandas Documentation - NumPy Quickstart - Kaggle Python Course | 3-4 weeks |
| 4 | Data Manipulation with Pandas | Master Pandas for data cleaning, filtering, grouping, and merging. Learn to handle missing data, outliers, and data transformation techniques. | - Pandas Tutorial - W3Schools - 10 Minutes to Pandas - Data Cleaning Course - Kaggle | 2-3 weeks |
| 5 | Data Visualization | Create compelling visualizations using Matplotlib, Seaborn, and Plotly. Learn when to use different chart types for effective storytelling. | - Matplotlib Tutorials - Seaborn Tutorial - Plotly Documentation - Data Visualization - Kaggle | 2-3 weeks |
| 6 | SQL for Data Analysis | Learn SQL for database querying: SELECT, JOIN, GROUP BY, and window functions. Essential for extracting data from relational databases. | - W3Schools SQL Tutorial - SQLBolt Interactive Lessons - HackerRank SQL Practice - Mode SQL Tutorial | 2-3 weeks |
| 7 | Exploratory Data Analysis (EDA) | Learn systematic approaches to explore datasets, identify patterns, correlations, and anomalies using statistical summaries and visualizations. | - EDA with Python - Kaggle - Exploratory Data Analysis - Coursera - EDA Guide - Towards Data Science | 2-3 weeks |
| 8 | Machine Learning Fundamentals | Understand supervised vs unsupervised learning, overfitting, cross-validation, and model evaluation metrics like accuracy, precision, recall, and F1-score. | - Machine Learning Course - Andrew Ng - Scikit-learn Documentation - Machine Learning - Kaggle | 3-4 weeks |
| 9 | Supervised Learning Algorithms | Master linear/logistic regression, decision trees, random forests, and SVM. Learn when to use each algorithm and how to tune hyperparameters. | - Scikit-learn Tutorials - Hands-On Machine Learning Book - Machine Learning Mastery | 4-5 weeks |
| 10 | Unsupervised Learning & Clustering | Learn K-means, hierarchical clustering, DBSCAN, and dimensionality reduction techniques like PCA for pattern discovery in unlabeled data. | - Unsupervised Learning - Coursera - Clustering Algorithms Guide - PCA Explained - StatQuest | 3-4 weeks |
| 11 | Feature Engineering & Selection | Learn to create meaningful features, handle categorical variables, scale data, and select important features to improve model performance. | - Feature Engineering Course - Kaggle - Feature Engineering for Machine Learning Book - Scikit-learn Preprocessing | 2-3 weeks |
| 12 | Model Evaluation & Validation | Master cross-validation, ROC curves, confusion matrices, and bias-variance tradeoff. Learn to properly evaluate and compare models. | - Model Evaluation Guide - Cross-validation - StatQuest - ROC and AUC Explained | 2-3 weeks |
| 13 | Big Data Tools (Spark/Hadoop) | Learn Apache Spark with PySpark for processing large datasets. Understand distributed computing concepts and data processing at scale. | - PySpark Documentation - Spark Tutorial - Databricks - Big Data with Spark - edX | 3-4 weeks |
| 14 | Deep Learning Basics | Understand neural networks, backpropagation, and implement models using TensorFlow/Keras for image and text classification problems. | - Deep Learning Specialization - Coursera - TensorFlow Tutorials - Keras Documentation - Fast.ai Deep Learning Course | 4-6 weeks |
| 15 | Natural Language Processing (NLP) | Learn text preprocessing, sentiment analysis, topic modeling, and work with libraries like NLTK, spaCy, and transformers. | - NLTK Documentation - spaCy Tutorial - NLP Course - Hugging Face - NLP with Python - NLTK | 3-4 weeks |
| 16 | Time Series Analysis | Master time series forecasting using ARIMA, seasonal decomposition, and Prophet for predicting trends and patterns over time. | - Time Series Analysis - Python - Prophet Documentation - Time Series Forecasting - Kaggle | 2-3 weeks |
| 17 | Build an End-to-End ML Project | Create a complete project: data collection, EDA, feature engineering, model training, evaluation, and deployment using Flask or Streamlit. | - Flask Documentation - Streamlit Documentation - ML Project Template - Deploy ML Models | 3-4 weeks |
| 18 | Cloud Platforms for ML (AWS/GCP/Azure) | Learn cloud ML services: AWS SageMaker, Google AI Platform, Azure ML for scalable model training and deployment. | - AWS SageMaker Tutorial - Google Cloud AI - Azure Machine Learning - MLOps on Cloud | 3-4 weeks |
| 19 | MLOps & Model Deployment | Learn MLOps practices: version control for models, MLflow for experiment tracking, Docker for containerization, and CI/CD for ML pipelines. | - MLflow Documentation - Docker for Data Science - DVC (Data Version Control) - MLOps Guide | 2-3 weeks |
| 20 | Build a Data Science Portfolio | Create a GitHub portfolio with diverse projects: EDA, ML classification/regression, NLP, time series, and deployment examples with detailed documentation. | - GitHub - Portfolio Examples - Jupyter Notebook Best Practices - LinkedIn Data Science | 2-3 weeks |
| 21 | Advanced Deep Learning & Neural Nets | Master CNNs for computer vision, RNNs/LSTMs for sequences, GANs for generation, and transformer architectures for state-of-the-art NLP. | - Deep Learning Book - PyTorch Tutorials - Computer Vision - Stanford CS231n - Transformer Architecture - Papers with Code | 4-6 weeks |
| 22 | Advanced MLOps & AutoML | Implement automated machine learning pipelines, hyperparameter optimization with tools like Optuna, and advanced deployment strategies with Kubernetes. | - AutoML with H2O - Optuna Documentation - Kubeflow - AutoML Papers | 3-4 weeks |
| 23 | Research & Cutting-Edge Applications | Stay current with research papers, contribute to open source, and build innovative projects using reinforcement learning, graph neural networks, or federated learning. | - arXiv Papers - Google Scholar - Kaggle Competitions - Open Source ML Projects - Reinforcement Learning | 4-6 weeks |