Become a Job-Ready Data Scientist
Master statistics, machine learning, data engineering, model deployment, and storytelling with data through hands-on projects and industry mentorship.
📊 Module 1: Statistics & Probability Foundations
- Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
- Probability Distributions: Normal, Binomial, Poisson
- Hypothesis Testing: T-tests, Chi-square tests, ANOVA
- Confidence Intervals and Statistical Significance
- Bayesian Statistics Fundamentals
- Correlation vs Causation Analysis
🐍 Module 2: Python Programming for Data Science
- Python Fundamentals: Data Types, Control Structures
- NumPy: Arrays, Mathematical Operations, Broadcasting
- Pandas: DataFrames, Data Manipulation, Merging, Grouping
- Data Cleaning: Missing Values, Outliers, Data Types
- Exploratory Data Analysis (EDA) Techniques
- Feature Engineering and Selection
- Working with APIs and Web Scraping
📈 Module 3: Data Visualization & Storytelling
- Matplotlib: Basic Plots, Customization, Subplots
- Seaborn: Statistical Visualizations, Heatmaps
- Plotly: Interactive Charts, Dashboards
- Data Storytelling Principles
- Creating Executive Dashboards
- Business Intelligence Reporting
🧠 Module 4: Machine Learning Fundamentals
- Supervised Learning: Linear & Logistic Regression
- Classification: Decision Trees, Random Forest, SVM
- Model Evaluation: Accuracy, Precision, Recall, F1-Score
- Cross-Validation and Hyperparameter Tuning
- Unsupervised Learning: K-Means, Hierarchical Clustering
- Dimensionality Reduction: PCA, t-SNE
- Scikit-learn Library Deep Dive
⚡ Module 5: Advanced Machine Learning
- Ensemble Methods: Bagging, Boosting, Stacking
- Gradient Boosting: XGBoost, LightGBM
- Neural Networks Introduction
- Deep Learning with TensorFlow/Keras
- Natural Language Processing Basics
- Time Series Analysis and Forecasting
🗄️ Module 6: Data Engineering Essentials
- SQL: Advanced Queries, Joins, Window Functions
- Database Design and Normalization
- ETL Processes and Data Pipelines
- Working with Big Data: Spark Basics
- Cloud Platforms: AWS, Google Cloud, Azure
- Data Warehousing Concepts
☁️ Module 7: Model Deployment & MLOps
- Model Serialization: Pickle, Joblib
- REST API Development with Flask/FastAPI
- Containerization with Docker
- Cloud Deployment: AWS SageMaker, Google Cloud ML
- Model Monitoring and Versioning
- CI/CD for Machine Learning Projects
- A/B Testing for Models
🏆 Module 8: Capstone Project & Portfolio
End-to-end project combining data extraction, cleaning, modeling, and deployment to solve a real-world business problem.
- End-to-End Data Science Project
- Problem Definition and Business Understanding
- Data Collection and Cleaning
- Exploratory Analysis and Feature Engineering
- Model Building, Evaluation, and Deployment
- Project Documentation and Presentation
- GitHub Portfolio Development
- Resume Building for Data Science Roles