INTRODUCTION TO DATA SCIENCE (ML AND DL ) | IDS

  Foundations of Data Science (2 weeks)

• Introduction to Data Science
•	Definition and scope
•	Applications in various industries

• Python for Data Science
•	Basics of Python programming
•	Libraries: NumPy, Pandas, Matplotlib

  Data Manipulation and Cleaning (3 weeks)

• Data Acquisition
•	Importing data from various sources
•	APIs and web scraping

• Data Cleaning and Preprocessing
•	Handling missing values
•	Data normalization and standardization

  Exploratory Data Analysis (2 weeks)

• Descriptive Statistics
•	Measures of central tendency and dispersion
•	Visualization techniques

• Statistical Analysis with Python
•	Hypothesis testing
•	Correlation and regression analysis

  Machine Learning Fundamentals (4 weeks)

• Introduction to Machine Learning
•	Supervised vs. unsupervised learning
•	Types of machine learning algorithms
•	Model Training and Evaluation
•	Splitting datasets
•	Cross-validation

• Supervised Learning Algorithms
•	Linear regression, logistic regression
•	Decision trees, random forests

• Unsupervised Learning Algorithms
•	Clustering (K-means, hierarchical)
•	Dimensionality reduction (PCA)

  Advanced Topics (3 weeks)

• Feature Engineering
•	Importance and techniques

• Deep Learning
•	Neural networks basics
•	Introduction to TensorFlow or PyTorch

• Model Deployment
•	Basics of deploying machine learning models
•	Basics of Cloud services (AWS, Azure)

  Capstone Project (4 weeks)

• Capstone Project
•	Apply learned skills to a real-world problem (Kaggle)
•	Presentation and documentation