Module 1: Introduction to Data Science
- What is Data Science?
- Data Science Workflow
- Applications of Data Science
- Roles and Responsibilities of a Data Scientist
Module 2: Data Science Foundations
- Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
- Inferential Statistics: Hypothesis Testing, Confidence Intervals
- Probability Distributions: Normal, Binomial, Poisson
- Linear Algebra: Matrices, Vectors, Eigenvalues
- Calculus for Optimization: Gradients and Derivatives
Module 3: Python for Data Science
- Python Basics: Data Types, Control Structures, Functions
- Python Libraries: NumPy, Pandas, Matplotlib, Seaborn
- Data Manipulation with Pandas
- Data Visualization Techniques
- Hands-On Data Analysis
Module 4: Data Wrangling and Preprocessing
- Handling Missing Data
- Data Cleaning Techniques
- Feature Engineering and Feature Scaling
- Encoding Categorical Variables
- Outlier Detection and Treatment
Module 5: Exploratory Data Analysis (EDA)
- Understanding Data Distributions
- Correlation Analysis
- Univariate, Bivariate, and Multivariate Analysis
- Advanced Visualization Techniques
- Hands-On EDA Project
Module 6: Machine Learning Basics
- Supervised Learning: Classification and Regression
- Algorithms: Linear Regression, Logistic Regression, Decision Trees
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Algorithms: K-Means, DBSCAN, PCA
- Model Evaluation Metrics
Module 7: Advanced Machine Learning
- Ensemble Techniques: Random Forest, Gradient Boosting, XGBoost
- Hyperparameter Tuning: Grid Search, Random Search
- Cross-Validation Techniques
- Model Deployment Basics
Module 8: Introduction to Big Data and SQL
- Overview of Big Data Technologies: Hadoop, Spark
- SQL for Data Analysis
- Writing Queries, Joins, Grouping, Aggregations
- Integrating SQL with Python
Module 9: Introduction to Deep Learning
- Basics of Neural Networks
- Introduction to TensorFlow and Keras
- Building a Simple Neural Network
- Hands-On Project: Image Classification or Text Analysis
Module 10: Data Science Project and Deployment
- End-to-End Data Science Project Workflow
- Insights and Reporting
- Deploying Models Using Flask or FastAPI
Module 11: Capstone Project and Review
- Real-World Data Science Capstone Project
- Dataset Exploration
- EDA and Feature Engineering
- Model Building and Optimization
- Insights and Presentation