Data Analytics and Machine Learning for Students
Data Analytics and Machine Learning for Students
Using Python
About the Book
Unlock the Secrets of Data—No Experience Required!
Data Analytics and Machine Learning for Students is the ultimate beginner-friendly guide to data science and machine learning, designed specifically for students and curious minds ready to dive into the world of data. Whether you’re fascinated by movie recommendations, social media trends, or AI-powered predictions, this book gives you the tools to analyze, visualize, and model data like a pro.
With step-by-step tutorials, real-world datasets from Kaggle, and hands-on projects, you’ll learn how to clean messy data, create eye-catching visualizations, and build predictive models—all while developing skills that are in high demand across industries. From exploring neural networks with PyTorch to mastering machine learning techniques like decision trees and clustering, this book simplifies complex concepts and makes learning interactive and fun.
No prior coding experience? No problem! Data Analytics and Machine Learning for Students provides a Python quick-start guide and easy-to-follow examples to help you get started right away. Whether you want to ace your school project, impress college admissions, or prepare for a future in data science, this book will give you the confidence and tools to turn data into insights—and insights into action.
Your journey into the world of data starts here!
About the Author
Table of Contents
- Who Is This Book For?
- Chapter 1: What is Data Analytics?
- Definition of Data Analytics
- Key Components of Data Analytics
- Types of Data Analytics
- Importance of Data in Today’s World
- Applications of Data Analytics
- Data-Driven Decision Making
- Chapter 2: Types of Data
- Structured Data
- Unstructured Data
- Comparison of Structured and Unstructured Data
- Quantitative vs Qualitative Data
- Data Collection Methods
- Popular Platforms for Datasets: Kaggle and Hugging Face
- Case Study: The Titanic Dataset
- Chapter 3: Data Cleaning and Preprocessing
- Handling Missing Values
- Removing Duplicates
- Normalization and Standardization
- Introduction to Pandas and NumPy
- Hands-on Activity: Cleaning the Titanic Dataset from Kaggle
- Chapter 4: Introduction to Data Visualization
- Why Visualization Matters
- Types of Data Visualizations
- Example of Data Storytelling: “The Impact of Marketing Spend on Revenue Growth”
- Chapter 5: Tools for Visualization
- Introduction to
MatplotlibandSeaborn
- Visualizing Distributions, Relationships, and Comparisons
- Hands-on Activity: Visualizing the Iris Dataset from Kaggle
- Introduction to
- Chapter 6: Introduction to Exploratory Data Analysis
- What is EDA?
- Steps in EDA
- Case Study: Performing EDA on a Dataset
- Chapter 7: What is Machine Learning?
- Definition of Machine Learning
- Significance of Machine Learning
- Machine Learning in Everyday Life
- Types of Machine Learning
- Hands-On Example: Predicting Housing Prices with Python
- Chapter 8: Introduction to Regression and Classification
- Regression: Predicting Continuous Values
- Classification: Predicting Categorical Outcomes
- Difference Between Target Variable and Features
- Supervised Learning Algorithms: Linear Regression and Logistic Regression
- Hands-on Activity: Predicting Titanic Survival with Logistic Regression
- Chapter 9: Unsupervised Learning Basics
- Introduction to Clustering and Dimensionality Reduction
- Case Study: Kaggle Dataset for Clustering - Customer Segmentation
- Case Study: Adding PCA to Improve Visualization of K-Means Clustering
- Chapter 10: Decision Trees and Random Forests
- Introduction to Decision Trees
- Advantages and Drawbacks of Decision Trees
- Random Forests for Better Performance
- Hands-on Activity: Use the Heart Disease Dataset to Classify Data with Decision Trees
- Decision Tree
- Chapter 11: Neural Networks and Deep Learning
- Introduction to Neural Networks and Basic Architecture
- Deep Learning
- Overview of Frameworks like TensorFlow and PyTorch
- Hands-On Activity: Using the MNIST Dataset to Identify Numbers
- Chapter 12: Evaluating Machine Learning Models
- Metrics for Classification: Accuracy, Precision, Recall, F1 Score
- Metrics Example
- Complete Python Example
- Confusion Matrix
- Metrics for Regression: Mean Squared Error and R-Squared
- Overfitting and Underfitting in Machine Learning Models
- Cross-Validation Techniques
- Chapter 13: Hyperparameter Tuning
- Importance of Tuning Hyperparameters
- Practical Approach to Hyperparameter Tuning
- Grid Search and Random Search
- Tuning Comparison
- Hands-on Activity: Hyperparameter Tuning with Random Forests on the Wine Quality Dataset
- Reflective Questions
- Python Challenges for Chapter 13: Hyperparameter Tuning
- Chapter 14: Ethical Considerations in Data Analytics and ML - Bias
- Bias in Data and Algorithms
- Sources of Bias in Data
- How Machine Learning Models Can Perpetuate Bias
- Strategies to Mitigate Bias in Models
- Example: Bias in Hiring Models
- Preventing Bias Perpetuation
- Case Study: Examine bias in a real dataset
- Chapter 15: Privacy and Security Concerns
- Privacy Issues in Data Collection
- How to Anonymize Data
- Chapter 16: Data Analytics in Action
- Case Studies of Companies Using Data Analytics
- Building a Movie Recommendation System with the MovieLens Dataset
- Appendix A: Glossary of Terms
- Appendix B: Python Quick Start Guide
- 1. Setting Up Python for Data Science
- 2. Pandas: Data Manipulation
- 3. NumPy: Numerical Computations
- 4. Scikit-Learn: Machine Learning Basics
- 5. PyTorch: Deep Learning Basics
- Appendix C: Data Sets and Source Code
- Appendix D: Installation and Requirements
- 1. Running Notebooks Locally
- 2. Running Notebooks on Google Colab
- 3. Running Notebooks on Kaggle Notebook
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