Data Science Guide

Description

Welcome to the Data Science Guide repository! This project serves as a comprehensive guide to the foundational and advanced concepts of data science, machine learning, and artificial intelligence. It is based on summaries and exercises from four essential books in the field:

1. Python Crash Course by Eric Matthes
2. Data Science for Business by Foster Provost
3. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurelien Geron
4. Designing Machine Learning Systems by Chip Huyen

Each book is covered chapter by chapter, with summaries and solutions to exercises and problems provided to facilitate learning and application.

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Index

1. Python Crash Course by Eric Matthes

Part I: Basics

• Chapter 1: Getting Started
  • Introduction to Python programming.
  • Setting up Python environment.
• Chapter 2: Variables and Simple Data Types
  • Understanding variables, strings, numbers, and comments.
• Chapter 3: Introducing Lists
  • Working with lists, including indexing and slicing.
• Chapter 4: Working with Lists
  • Modifying lists and working with loops.
• Chapter 5: If Statements
  • Conditional tests and if statements.
• Chapter 6: Dictionaries
  • Key-value pairs, working with dictionaries.
• Chapter 7: User Input and While Loops
  • Handling user input and using while loops.
• Chapter 8: Functions
  • Writing functions, passing arguments.
• Chapter 9: Classes
  • Creating and using classes.
• Chapter 10: Files and Exceptions
  • Reading from and writing to files, handling exceptions.
• Chapter 11: Testing Your Code
  • Writing tests and debugging code.

Part II: Projects

• Project 1: Alien Invasion
  • Building a 2D game using Pygame.
• Project 2: Data Visualization
  • Visualizing data with Matplotlib and Pygal.
• Project 3: Web Applications
  • Building simple web apps with Django.

2. Data Science for Business by Foster Provost

• Chapter 1: Introduction
  • Understanding data science and its importance in business.
• Chapter 2: Business Problems and Data Science Solutions
  • Identifying business problems that can be solved with data science.
• Chapter 3: Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
  • Fundamentals of predictive modeling.
• Chapter 4: Fitting a Model to Data
  • Techniques for fitting models to data.
• Chapter 5: Overfitting and Its Avoidance
  • Understanding and avoiding overfitting.
• Chapter 6: Similarity, Neighbors, and Clusters
  • Clustering techniques and similarity measures.
• Chapter 7: Decision Analytic Thinking I: What Is a Good Model?
  • Criteria for evaluating model quality.
• Chapter 8: Visualizing Model Performance
  • Techniques for visualizing model performance.
• Chapter 9: Evidence and Probabilities
  • Working with evidence and probabilities.
• Chapter 10: Representing and Mining Text
  • Techniques for text mining and representation.
• Chapter 11: Decision Analytic Thinking II: Toward Analytical Engineering
  • Advanced decision analytic thinking.
• Chapter 12: Other Data Science Tasks and Techniques
  • Additional tasks and techniques in data science.
• Chapter 13: Data Science and Business Strategy
  • Integrating data science with business strategy.
• Chapter 14: Conclusion
  • Summary and future directions in data science.

3. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurelien Geron

Part I: The Fundamentals of Machine Learning

• Chapter 1: The Machine Learning Landscape
  • Overview of machine learning, types of machine learning systems.
• Chapter 2: End-to-End Machine Learning Project
  • Hands-on project to go through the entire process of a machine learning project.
• Chapter 3: Classification
  • Understanding classification algorithms and performance metrics.
• Chapter 4: Training Models
  • Techniques for training machine learning models.
• Chapter 5: Support Vector Machines
  • Introduction to SVMs and their applications.
• Chapter 6: Decision Trees
  • Building and using decision trees.
• Chapter 7: Ensemble Learning and Random Forests
  • Combining models to improve performance.
• Chapter 8: Dimensionality Reduction
  • Techniques for reducing dimensionality of data.
• Chapter 9: Unsupervised Learning Techniques
  • Identifying patterns without labeled data.

Part II: Neural Networks and Deep Learning

• Chapter 9: Introduction to Artificial Neural Networks with Keras
  • Basics of neural networks, building models with Keras.
• Chapter 10: Training Deep Neural Networks
  • Techniques for training deep networks.
• Chapter 11: Custom Models and Training with TensorFlow
  • Customizing models and training processes in TensorFlow.
• Chapter 12: Loading and Preprocessing Data with TensorFlow
  • Handling and preprocessing data in TensorFlow.
• Chapter 13: Deep Computer Vision Using Convolutional Neural Networks
  • Using CNNs for computer vision tasks.
• Chapter 14: Processing Sequences Using RNNs and CNNs
  • Working with sequential data using RNNs and CNNs.

4. Designing Machine Learning Systems by Chip Huyen

• Chapter 1: Overview of Machine Learning Systems
  • Principles and components of machine learning systems.
• Chapter 2: Introduction to Machine Learning Systems Design
  • Basics of designing machine learning systems.
• Chapter 3: Data Engineering Fundamentals
  • Fundamentals of data engineering for ML systems.
• Chapter 4: Training Data
  • Techniques for collecting and preparing training data.
• Chapter 5: Feature Engineering
  • Methods for creating and selecting features.
• Chapter 6: Model Development and Offline Evaluation
  • Developing models and evaluating them offline.
• Chapter 7: Model Deployment and Prediction Service
  • Deploying models and setting up prediction services.
• Chapter 8: Data Distribution Shifts and Monitoring
  • Handling data distribution shifts and monitoring models.
• Chapter 9: Continual Learning and Testing in Production
  • Techniques for continual learning and testing in production.
• Chapter 10: Infrastructure and Tooling for MLOps
  • Infrastructure and tools for machine learning operations.
• Chapter 11: The Human Side of Machine Learning
  • Addressing human factors in machine learning projects.

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Required Software and Tools

To effectively use the materials and run the code in this repository, you will need the following software and tools installed on your system:

Python

Make sure you have Python installed. You can download it from Python's official website. The repository is compatible with Python 3.7 and above. If you use Linux like me, you can install python package from your distros package manager.

Python Packages

You will need to install several Python packages. It is recommended to create a virtual environment to manage dependencies. You can use the following commands to create a virtual environment and install the required packages:

1. Create a Virtual Environment

   python -m venv data_science
   source data_science/bin/activate  # On Windows use `data_science\Scripts\activate`

2. Install Required Packages

You can install required libraries with pip into your virtual environment data_science:

pip install -r notebook pygame scikit-learn tensorflow keras matplotlib

Additional Tools

• Git

Make sure you have Git installed to clone the repository. You can download it from Git's official website. If you use Linux like me, you can install git package from your distros package manager.

• Docker

For containerizing and deploying models. Install from Docker's official website. If you use Linux like me, you can install docker package from your distros package manager.