The Data Science Manual: A Comprehensive Guide to Tools and Techniques for Data Analysis, Modeling, and Deployment with Python
The field of data science is constantly evolving, and new tools and techniques are always emerging. However, the fundamentals of data science remain the same, and mastering these fundamentals is essential to success in the field. This book is designed to provide you with a solid foundation in the fundamentals of Data Science and the tools and techniques you need to master to become a successful data scientist. Whether you are just starting or already an experienced practitioner, this book will provide you with the knowledge and skills to tackle real-world data science problems. It covers a wide range of topics from data analysis, visualization, modeling, to deployment with Python. It assumes a basic knowledge of Python and requires Python version 3.8 or higher.
- Basics to Advances of Python programming
- Data Manipulation with NumPy and Pandas libraries
- Data Wrangling: The process of Gathering, Importing, Cleaning, Transforming, and Preparing raw data for analysis
- Data Exploration: Descriptive Statistics, Data Visualization, Outlier Detection, and Feature Selection
- Statistics Fundamentals: Fundamentals of Probability Theory, Probability Laws, Statistical distributions, Hypothesis testing, Sampling, Estimation and more
- Machine Learning and Predictive Modeling
- Model Deployment
This repo provides example codes for each chapter, which can be found in the examples/ directory. In addition, some popular datasets are provided in various formats, including CSV, Excel, and JSON. All the example codes are written in Python and require Python version 3.8 or higher.
This book also provides exercises for each chapter to help readers/learners practice and reinforce the concepts learned. The exercises are designed to be challenging but doable, and solutions are provided in the exercises/ directory.
In summary, I will introduce solutions for some real-world Data Science problems, including California Housing Price, Wine, Credit Card Fraud Detection datasets, and more. These ones can be found in the completed_solutions directory. Real-world datasets are a crucial component of data science, as they provide the raw materials you can use to solve complex problems and gain insights into various fields. These datasets can come from various sources, such as scientific research, business operations, government agencies, social media, etc. However, working with real-world datasets can be challenging due to their size, complexity, and lack of structure. Therefore, you must use various tools and techniques to preprocess, clean, and analyze these datasets to make sense of their information. This process involves several stages, including data wrangling, data exploration, data visualization, machine learning or predictive modeling, deploying the trained model, and choosing the right tools to depend on the problems you’re solving. I hope the solutions below are just for your reference to help you understand the steps to solve real problems.
To run the example codes and exercises, you need to have Python version 3.8 or higher installed on your computer/environment and other nescessary libraries, which can be found in requirements.txt file.
This book is designed to be read sequentially, as each chapter builds upon the previous one. However, each chapter is also self-contained, so you can skip around and read the chapters that interest you the most.
To get the most out of this book, I recommend that you follow along with the example codes and try the exercises on your own. You should also practice working with your own datasets and try applying the techniques you learn to real-world problems.
I welcome your feedback on this book and any issues you encounter. If you have any questions or suggestions, please contact me at [email protected].
I would like to thank the Python community for creating such a powerful and versatile language, as well as the open-source community for providing numerous tools and libraries that make data science accessible to everyone. I would also like to thank my colleages and friends for their support and encouragement throughout the writing of this book.