Lecture 1: Overview of Data Science

• What is Data Science?
• Importance of Data Science in the Industry
• Data Science Workflow and Methodologies

Lecture 2: Introduction to Python for Data Science

• Why Python for Data Science?
• Setting Up Python and Anaconda Environment
• Basic Python Syntax and Data Structures

Lecture 3: Data Types and Data Structures in Python

• Introduction to Lists, Tuples, and Dictionaries
• Working with Sets and Strings
• Understanding Functions and Modules

Lecture 4: Introduction to NumPy

• What is NumPy and Why Use It?
• NumPy Arrays and Operations
• Indexing, Slicing, and Reshaping Arrays

Lecture 5: Data Manipulation with Pandas

• Introduction to Pandas
• DataFrames and Series in Pandas
• Data Cleaning and Transformation with Pandas

Lecture 6: Data Visualization with Matplotlib and Seaborn

• Introduction to Data Visualization
• Plotting with Matplotlib
• Advanced Visualization with Seaborn

Lecture 7: Descriptive Statistics

• Measures of Central Tendency (Mean, Median, Mode)
• Measures of Dispersion (Variance, Standard Deviation)
• Data Distribution and Skewness

Lecture 8: Inferential Statistics

• Understanding Sampling and Hypothesis Testing
• Confidence Intervals and P-Values
• T-tests, Chi-Square Tests, and ANOVA

Lecture 9: Probability Concepts and Distributions

• Basic Probability Theory
• Probability Distributions (Normal, Binomial, Poisson)
• Bayes’ Theorem and Applications in Data Science

Lecture 10: Data Preprocessing Techniques

• Handling Missing Data
• Data Encoding and Transformation
• Normalization and Standardization

Lecture 11: Feature Engineering

• Creating New Features from Existing Data
• Feature Selection Techniques
• Dimensionality Reduction with PCA

Lecture 12: Introduction to SQL for Data Science

• Understanding Databases and SQL
• Writing Basic SQL Queries
• Data Aggregation and Joins in SQL

Lecture 13: Overview of Machine Learning

• What is Machine Learning?
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Key Concepts in Machine Learning

Lecture 14: Supervised Learning Algorithms

• Linear Regression and Logistic Regression
• Decision Trees and Random Forests
• Model Evaluation Metrics (Accuracy, Precision, Recall)

Lecture 15: Unsupervised Learning Algorithms

• Introduction to Clustering (K-Means, Hierarchical Clustering)
• Association Rule Learning
• Dimensionality Reduction Techniques

Lecture 16: Model Evaluation and Tuning

• Cross-Validation and Hyperparameter Tuning
• Grid Search and Random Search
• Dealing with Overfitting and Underfitting

Lecture 17: Ensemble Methods

• Introduction to Bagging and Boosting
• Understanding AdaBoost and Gradient Boosting
• Implementing Random Forest and XGBoost

Lecture 18: Introduction to Deep Learning

• What is Deep Learning?
• Neural Networks Basics
• Overview of TensorFlow and Keras

Lecture 19: Time Series Analysis

• Understanding Time Series Data
• Moving Averages and Exponential Smoothing
• ARIMA Models and Forecasting

Lecture 20: Natural Language Processing (NLP)

• Introduction to NLP Concepts
• Text Preprocessing and Tokenization
• Sentiment Analysis and Text Classification

Lecture 21: Image Processing and Computer Vision

• Basics of Image Processing
• Convolutional Neural Networks (CNNs)
• Implementing Image Classification Models

Lecture 22: Capstone Project Introduction

• Overview of the Capstone Project
• Project Guidelines and Expectations
• Team Formation and Topic Selection

Lecture 23: Project Work and Instructor Guidance

• Working on the Capstone Project
• Q&A and Troubleshooting
• Instructor Feedback Sessions

Lecture 24: Project Work and Final Touches

• Finalizing the Project
• Preparing for Presentations
• Peer Review and Feedback

Lecture 25: Project Presentations

• Student Presentations
• Q&A Sessions
• Grading and Evaluation

Lecture 26: Industry Insights and Guest Lecture

• Guest Speaker Session from Industry Experts
• Future Trends in Data Science
• Networking and Career Opportunities

Lecture 27: Course Wrap-Up and Certification

• Recap of Key Learnings
• Final Q&A and Feedback
• Issuance of Internship Certificate