Data Science
( 24 Modules )

Module #1
Introduction to Data Science
Overview of data science, importance, and applications

Module #2
Data Science Process
Understanding the data science process:problem definition, data collection, cleaning, analysis, and visualization

Module #3
Python for Data Science
Introduction to Python programming language and its libraries for data science (NumPy, Pandas, etc.)

Module #4
Data Preprocessing
Handling missing values, data normalization, feature scaling, and data transformation

Module #5
Data Visualization
Introduction to data visualization using Matplotlib and Seaborn

Module #6
Descriptive Statistics
Measures of central tendency, variability, and data distribution

Module #7
Inferential Statistics
Hypothesis testing, confidence intervals, and p-values

Module #8
Regression Analysis
Simple and multiple linear regression, regression assumptions, and model evaluation

Module #9
Feature Engineering
Feature selection, extraction, and creation techniques

Module #10
Supervised Learning
Introduction to supervised learning, classification, and regression

Module #11
Unsupervised Learning
Introduction to unsupervised learning, clustering, and dimensionality reduction

Module #12
Model Evaluation
Metrics for evaluating model performance, overfitting, and underfitting

Module #13
Decision Trees and Random Forests
Introduction to decision trees and random forests, advantages, and limitations

Module #14
Support Vector Machines
Introduction to support vector machines, kernel trick, and SVM types

Module #15
Neural Networks
Introduction to neural networks, perceptron, and multilayer perceptron

Module #16
Deep Learning
Introduction to deep learning, convolutional neural networks, and recurrent neural networks

Module #17
Natural Language Processing
Introduction to natural language processing, text preprocessing, and text representation

Module #18
Big Data and NoSQL Databases
Introduction to big data, Hadoop ecosystem, and NoSQL databases

Module #19
Data Storytelling
Effective communication of insights and results using data visualization and storytelling

Module #20
Data Science Tools and Technologies
Introduction to data science tools and technologies, Jupyter Notebooks, and Git

Module #21
Case Study 1:Regression Analysis
Applying regression analysis to a real-world problem

Module #22
Case Study 2:Classification
Applying classification techniques to a real-world problem

Module #23
Case Study 3:Clustering
Applying clustering techniques to a real-world problem

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Data Science career

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