Course Syllabus

Module 1: Introduction to Data Science

Understanding Data Science
Data Science Life Cycle
Artificial Intelligence Overview
Machine Learning & Deep Learning Basics
Natural Language Processing (NLP)

Module 2: Data Science with Python

Python Basics, Data Types, Variables
Lists, Tuples, Dictionaries & Sets
File Handling & Exception Handling
Functions, Modules & Packages
Regular Expressions
NumPy, Pandas, SciPy
Matplotlib & Seaborn for Data Visualization

Module 3: Statistics & Data Analysis

Descriptive vs Inferential Statistics
Mean, Median, Mode, Variance, Std. Dev.
Probability Distributions
Hypothesis Testing (T, Z, ANOVA, Chi-Square)
Exploratory Data Analysis (EDA)

Module 4: Databases & SQL

SQL Basics (DDL, DML, Joins, Subqueries)
Stored Procedures & Triggers
Working with MySQL / PostgreSQL
Connecting Python with Databases

Module 5: Machine Learning

Supervised Learning (Regression, Classification)
Unsupervised Learning (K-Means, Hierarchical)
Reinforcement Learning
Decision Trees, Random Forest, SVM, Naïve Bayes
Model Evaluation & Boosting (AdaBoost, XGBoost)
Time Series Analysis (AR, MA, Forecasting)

Module 6: Deep Learning & AI

Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN, LSTM)
NLP (Text Classification, Sentiment Analysis)
Transfer Learning & Deployment

Module 7: Data Science with R

R Basics & Environment Setup
Vectors, Lists, Matrices, Data Frames
Data Manipulation with R
Statistical Analysis in R
Data Visualization with ggplot2
Machine Learning with R

Module 8: Capstone Projects

EDA on Real-World Dataset
Predictive Modeling Project
NLP Text Classification
Time Series Forecasting
End-to-End Data Science Capstone Project