Here’s a month-wise roadmap for learning Data Science effectively:
Month 1: Python & SQL Basics
✅ Learn Python Basics (Variables, Loops, Functions, Data Structures)
✅ NumPy & Pandas for Data Manipulation
✅ Matplotlib & Seaborn for Data Visualization
✅ SQL Basics (SELECT, WHERE, GROUP BY, JOINS)
✅ Mini-Project: Analyze a simple dataset (e.g., Titanic dataset)
Month 2: Statistics & EDA (Exploratory Data Analysis)
✅ Descriptive Statistics (Mean, Median, Mode, Variance, Std Dev)
✅ Probability, Bayes Theorem, Probability Distributions
✅ Hypothesis Testing (t-test, chi-square test)
✅ Exploratory Data Analysis (Handling missing values, outliers, correlations)
✅ Mini-Project: Perform EDA on a dataset (e.g., Airbnb, Sales Data)
Month 3: Machine Learning Basics
✅ Understanding Supervised & Unsupervised Learning
✅ Regression Models (Linear, Polynomial, Ridge, Lasso)
✅ Classification Models (Logistic Regression, Decision Trees, Random Forest)
✅ Hyperparameter Tuning & Model Evaluation (Cross-Validation, Metrics)
✅ Mini-Project: Build a simple ML model (e.g., Predict House Prices)
Month 4: Advanced Machine Learning & Feature Engineering
✅ Feature Engineering (Encoding, Scaling, Feature Selection)
✅ Ensemble Learning (Bagging, Boosting, Stacking)
✅ Clustering (K-Means, DBSCAN, Hierarchical)
✅ Dimensionality Reduction (PCA, t-SNE)
✅ Mini-Project: Apply ML on real-world data (e.g., Customer Segmentation)
Month 5: Deep Learning & Time Series Analysis
✅ Basics of Neural Networks (ANN, CNN, RNN)
✅ Frameworks: TensorFlow/Keras or PyTorch
✅ Time Series Analysis (ARIMA, LSTM, Prophet)
✅ Mini-Project: Predict Stock Prices or Image Classification Task
Month 6: NLP & Big Data
✅ Natural Language Processing (TF-IDF, Word2Vec, Transformers)
✅ Sentiment Analysis & Text Classification
✅ Introduction to Big Data (Hadoop, Spark)
✅ Mini-Project: Chatbot or Sentiment Analysis
Month 7: Deployment & MLOps
✅ Model Deployment (Flask, FastAPI, Streamlit)
✅ CI/CD, Docker, and Cloud Platforms (AWS, GCP, Azure)
✅ Data Pipelines & Model Monitoring
✅ Mini-Project: Deploy an ML Model
Month 8+: Specialization & Real-World Projects
🔹 Computer Vision (Object Detection, GANs)
🔹 Reinforcement Learning (Q-Learning, Deep Q Networks)
🔹 Industry-Specific Use Cases (Healthcare, Finance, Retail)
🔹 Build an End-to-End Data Science Portfolio