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Curriculum
- 6 Sections
- 46 Lessons
- 26 Weeks
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- Month 1: Python Programming & Data Handling9
- 1.1Week 1–2: Python Fundamentals
- 1.2Variables, Data Types, Operators, Control Structures
- 1.3Functions, Loops, Exception Handling
- 1.4File I/O, Modules, and Packages
- 1.5Week 3–4: Python for Data Science
- 1.6NumPy for numerical computing
- 1.7Pandas for data manipulation
- 1.8Working with CSV, Excel, and JSON files
- 1.9Mini Project: Data wrangling and preprocessing on real-world dataset
- Month 2: Statistics, SQL & Data Analysis10
- 2.1Week 5–6: Statistics for Data Science
- 2.2Descriptive & Inferential Statistics
- 2.3Probability, Normal Distribution
- 2.4Hypothesis Testing & p-values
- 2.5Week 7–8: SQL for Data Analysis
- 2.6Week 7–8: SQL for Data Analysis
- 2.7SELECT, JOINs, Subqueries, Aggregations
- 2.8Window functions, CTEs
- 2.9Creating analytical queries from real datasets
- 2.10Mini Project: Analyze a database (e.g., sales or customer data) using SQL
- Month 3: Machine Learning – Supervised & Unsupervised9
- 3.1Week 9-10: Supervised Learning
- 3.2Linear & Logistic Regression
- 3.3Decision Trees, Random Forests, KNN
- 3.4Model evaluation metrics (accuracy, precision, recall, AUC)
- 3.5Week 11-12: Unsupervised Learning
- 3.6Clustering: K-Means, Hierarchical
- 3.7Dimensionality reduction: PCA, t-SNE
- 3.8Anomaly detection
- 3.9Mini Project: Customer segmentation or sales prediction
- Month 4: Deep Learning & NLP9
- 4.1Week 13-14: Neural Networks & Deep Learning
- 4.2Introduction to Neural Nets using TensorFlow/Keras
- 4.3CNNs for image classification
- 4.4Hyperparameter tuning, dropout, batch normalization
- 4.5Week 15-16: NLP (Natural Language Processing)
- 4.6Text cleaning, tokenization, vectorization (TF-IDF, Word2Vec)
- 4.7Sentiment analysis, classification
- 4.8Intro to transformers (BERT, GPT)
- 4.9Mini Project: Sentiment analysis or image classification
- Month 5: Power BI, Tableau & Model Deployment9
- 5.1Week 17-18: Data Visualization Tools
- 5.2Power BI: DAX, dashboards, publishing
- 5.3Tableau: Storytelling, filters, actions, dashboard design
- 5.4Comparison and integration with ML outputs
- 5.5Week 19-20: Model Deployment
- 5.6Saving models (pickle, joblib)
- 5.7Deploying with Flask/FastAPI
- 5.8Hosting on Heroku, Streamlit or AWS
- 5.9Mini Project: ML-powered dashboard with deployment
- Month 6: Capstone Project, Resume & Interview Prep7
- 6.1Week 21-22: Capstone Project (End-to-End)10 Minutes0 Questions
- 6.2Real-world dataset10 Minutes0 Questions
- 6.3EDA → Modeling → Visualization → Deployment10 Minutes0 Questions
- 6.4Week 23-24: Portfolio & Career Preparation10 Minutes0 Questions
- 6.5Build portfolio on GitHub10 Minutes0 Questions
- 6.6Create resume & LinkedIn profile for data roles10 Minutes0 Questions
- 6.7Mock interviews & case studies10 Minutes0 Questions
Variables, Data Types, Operators, Control Structures
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