Data Science with Python

Module 1: Foundations of Data Science and Key Libraries

 

Introduction to Data Science

 

• Overview of Data Science Applications

• Basics of Data Analysis and Visualization

• NumPy Fundamentals

• Getting Started with NumPy

• Creating and Indexing Arrays

• Array Slicing and Data Types

• Managing Array Shape and Reshaping

• Iterating, Joining, and Splitting Arrays

• Searching, Sorting, and Filtering Arrays

• Random Number Generation and Built-in Methods

 

Module 2: Advanced Data Handling with Pandas

 

• Introduction to Pandas

• Overview of the Pandas Library

• Working with Series and DataFrames

• Importing Data: CSV, JSON, Excel

• Techniques for Data Analysis and Manipulation

 

Module 3: Data Cleaning and Wrangling

 

• Data Preparation with Pandas

• Techniques for Cleaning DataFrames

• Removing and Handling Columns and Rows

• Addressing Missing Values

• Enhancing Data Readability and Index Management

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Module 4: Data Visualization with Matplotlib

 

• Creating Visualizations with Matplotlib

• Introduction to Matplotlib and Pyplot

• Customizing Chart Properties and Styles

• Building Various Plots: Box Plots, Heatmaps, Scatter Plots, Line Charts, Pie Charts, Bar Charts

• Visualizing Time Series and Geographical Data

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Module 5: Enhanced Visualization with Seaborn

 

• Exploring Seaborn for Statistical Graphics

• Overview of the Seaborn Library

• Creating Visuals: Line Plots, Dist Plots, Lmplot, Histograms, Bar Plots, Count Plots, Point Plots, Violin Plots, Heatmaps

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Module 6: Statistical Fundamentals

 

Understanding Statistics

Basic Statistical Concepts

Types of Statistics: Descriptive and Inferential

Measures of Central Tendency: Mean, Median, Mode

Measures of Dispersion: Variance, Standard Deviation, Range

 

Module 7: Exploratory Data Analysis (EDA)

 

• Performing EDA

• Introduction to EDA and Its Importance

• Calculating Descriptive Statistics: Mean, Median, Mode, Quartiles

• Grouping Data for Better Visualization

• Techniques for Understanding Data Distribution and Relationships

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Capstone Project 1: Credit Card Fraud Detection

Project Overview

Application of Exploratory Data Analysis (EDA) in Fraud Detection

Developing Risk Analytics for Fraud Prevention

 

Module 8: Introduction to Machine Learning

 

• Basics of Machine Learning

• Overview and Types of Machine Learning

 

Module 9: Supervised Learning – Regression

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• Regression Techniques

• Fundamentals of Linear Regression

• Implementing Linear Regression with Scikit-Learn

 

Capstone Project 2: Predicting Student Grades

 

Project Overview

Using Simple Linear Regression to Predict Academic Performance

Introduction to Polynomial Regression

 

Capstone Project 3: Predicting Startup Profits

 

Project Overview

Applying Multiple Linear Regression to Forecast Profits for Startups

 

Module 10: Supervised Learning – Classification

 

• Classification Techniques

• Overview of Logistic Regression

 

Capstone Project 4: Diabetes Prediction in Healthcare

 

Project Overview

Using Logistic Regression to Predict Diabetes Risk Based on Medical Data

 

Module 11: Unsupervised Learning

 

• Exploring Unsupervised Learning

• Types and Applications of Unsupervised Learning

• Introduction to Clustering Algorithms

• Understanding K-Means Clustering and Apriori Algorithms

 

Capstone Project 5: Market Basket Analysis

 

Project Overview

Implementing the Apriori Algorithm to Analyze Product Relationships

Practical Applications in Product Recommendation Systems

 

Module 12: Applying Machine Learning Algorithms

 

• Application and Evaluation of Algorithms

• Comprehensive Overview of Machine Learning Algorithms

• Implementing and Evaluating Various Algorithms

 

Capstone Project 6: Customer Churn Prediction

 

Project Overview

Analyzing Customer Retention and Churn Rates

Applying Classification Algorithms to Predict Customer Churn

Comparing and Optimizing Models for Performance