Foundation Course for Data Science

1. Introduction to Data Science 

• Overview, real-world applications, and scope of data science.

2. Introduction to Python and Libraries

• Basic Python programming.
• Introduction to key libraries: NumPy, Pandas, Matplotlib.

3. Math Foundation for Data Science

• Linear algebra: Matrices and vectors.
• Calculus: Derivatives and gradients.

4. Data Visualization and Analysis

• Visualization techniques using Matplotlib and Seaborn.
• Introduction to Excel for data analysis.

5. Basic Statistics

• Measures of central tendency (mean, median, mode).
• Variance, standard deviation, and correlation.

6. Exploratory Data Analysis (EDA)

• Identifying trends, handling missing data, and outliers.
• Practical application using sample datasets.

7. Probability

• Basic probability rules and real-world applications.

Machine Learning

1. Linear Regression

• Fundamentals and practical examples.
• Tools: Scikit-Learn for model implementation.

2. Cost Function and Gradient Descent

• Mathematical foundation for optimizing models.

3. Logistic Regression

• Binary classification with hands-on examples.

4. Support Vector Machines (SVM)

• Introduction and use cases for classification problems.

5. Tree Models, Clustering, and Classification

• Basics of decision trees and k-means clustering.

Deep Learning

1. Introduction to Neural Networks

• Architecture and activation functions.
• Real world application and Lab

Hands-On Labs

1. Lab Work Using Data

• Cleaning, preprocessing, and analyzing datasets.

2. Model Implementation

• Building regression and clustering models using Python.

Capstone Project

•  Goal: Solve a real-world business problem.
•  Steps:
  • Dataset preparation and EDA.
  • Model selection (linear regression or clustering).
  • Evaluation and presentation of results.