End-to-end Data Science project, including python step-by-step code
A complete guide to learn data science and machine learning step by step based on a Fintech project
Dear reader,
I am happy to share that the complete end-to-end data science project for beginners is now available as a book!
Who is this book for?
Anyone who wants to embrace data science challenges: 1) Experienced professionals from any field pursuing job switching, 2) Fresh graduates planning to choose a career path, 3) Anyone trying to understand how data science may help to improve their business. It does not matter what your age is or your background is. This book is designed to take you on the data science journey without prior analytical, coding, or other technical experience.
What is the book idea?
The main idea of the book is ‘learn by doing”. We vote for practicality over theory, thus, you will find many real on-the-job practical exercises and examples to get the flavor of data science. This, in turn, will help you to gain knowledge in the field quickly, and even decide if this job suits you, and prepare yourself to switch if you ever wondered. You get an assignment to solve a real fintech problem by diving deep into the business perspective and getting an opportunity to develop a predictive machine learning model.
What will you learn?
By the end of this book, you will be able to perform data science tasks independently (e.g., building ML models) and answer the most popular Data Scientist job interview questions. In addition, you will complete an end-to-end project portfolio that you can demonstrate to anyone to prove your knowledge and experience.
You learn data science step by step through building a data science project end-to-end on a real business problem focused on Fintech Industry. However, you can apply this knowledge in any other industry!
The project is based on three pillars: 1) define the problem, understand, prepare, and process the data (e.g., handling outliers, selecting ML features, etc.), 2) build, evaluate, and select Machine Learning models (e.g., Logistic Regression, XGBoost, Neural Networks) and, 3) productionize the selected ML model to make it available to anyone by building a User Interface with Streamlit, or an API (with AWS SageMaker, Lambda, and Gateway).
This is a User Interface web app that you complete by the end of this book:
You can find a live demo preview of the UI here:
How to get the book:
You can order the book on Amazon as a paperback, hardcover copy,or epub:
Should you need any further information, please feel free to contact me at data.s.enthusiast@gmail.com.
