Module 1: Introduction to Data Science
1.1. What is Data Science?
Definition and scope of data science
Applications and importance of data science in various industries
1.2. Fundamentals of Statistics
Descriptive statistics (mean, median, mode, variance, standard deviation)
Inferential statistics (probability distributions, hypothesis testing, confidence intervals)
1.3. Introduction to Python Programming
Basic Python syntax
Data types, variables, and operators
Control structures (if, for, while)
Functions and libraries (NumPy, Pandas)
Module 2: Data Wrangling and Preprocessing
2.1. Data Acquisition
Retrieving data from various sources (CSV files, databases, APIs)
Web scraping techniques
2.2. Data Cleaning
Handling missing values
Outlier detection and treatment
Data normalization and standardization
2.3. Exploratory Data Analysis (EDA)
Summary statistics and data visualization
Correlation analysis
Data transformation and feature engineering
Module 3: Machine Learning Fundamentals
3.1. Introduction to Machine Learning
Types of machine learning algorithms (supervised, unsupervised, reinforcement learning)
Model evaluation and validation techniques
3.2. Supervised Learning Algorithms
Linear regression
Logistic regression
Decision trees and ensemble methods (Random Forest, Gradient Boosting)
3.3. Unsupervised Learning Algorithms
Clustering algorithms (K-means, hierarchical clustering)
Dimensionality reduction techniques (PCA, t-SNE)
Module 4: Advanced Machine Learning Techniques
4.1. Model Selection and Optimization
Cross-validation
Hyper parameter tuning techniques (grid search, random search)
4.2. Advanced Supervised Learning Algorithms
Support Vector Machines (SVM)
Neural networks and deep learning
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
4.3. Time Series Analysis
Handling time series data
Time series forecasting techniques (ARIMA, SARIMA, LSTM)
Module 5: Model Deployment and Project Work
5.1. Model Deployment
Deployment strategies (APIs, containers)
Model monitoring and maintenance
5.2. Capstone Project
Applying learned concepts to a real-world data science project
Data collection, preprocessing, model building, and evaluation
Presentation and documentation of project findings