Practical Data Science with Jupyter: Explore Data Cleaning, Pre-processing, Data Wrangling, Feature Engineering and Mach

Solve business problems with data-driven techniques and easy-to-follow Python examples
Key Features
Essential coverage on statistics and data science techniques.
Exposure to Jupyter, PyCharm, and use of GitHub.
Real use-cases, best practices, and smart techniques on the use of data science for data applications.
Description
This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready.

This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms.
What you will learn
Rapid understanding of Python concepts for data science applications.
Understand and practice how to run data analysis with data science techniques and algorithms.
Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms.
Become self-sufficient to perform data science tasks with the best tools and techniques.
Who this book is for
This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples.
Table of Contents
1. Data Science Fundamentals
2. Installing Software and System Setup
3. Lists and Dictionaries
4. Package, Function, and Loop
5. NumPy Foundation
6. Pandas and DataFrame
7. Interacting with Databases
8. Thinking Statistically in Data Science
9. How to Import Data in Python?
10. Cleaning of Imported Data
11. Data Visualization
12. Data Pre-processing
13. Supervised Machine Learning
14. Unsupervised Machine Learning
15. Handling Time-Series Data
16. Time-Series Methods
17. Case Study-1
18. Case Study-2
19. Case Study-3
20. Case Study-4
21. Python Virtual Environment
22. Introduction to An Advanced Algorithm - CatBoost
23. Revision of All Chapters’ Learning
About the Author
Prateek Gupta is a Data Enthusiast and loves data-driven technologies. Prateek has completed his B.Tech in Computer Science & Engineering and he is currently working as a Data Scientist in an IT company. Prateek has a total 9 years of experience in the software industry, and currently, he is working in the computer vision area. Prateek has implemented various end-to-end Data Science projects for fishing, winery, and ecommerce clients. His implemented object detection and recognition models and product recommendation engines have solved many business problems of various clients. His keen area of interest is in natural language processing and computer vision. In his leisure time, he writes posts about artificial intelligence in his blog.

Blog links: http://dsbyprateekg.blogspot.com/
LinkedIn Profile: https://www.linkedin.com/in/prateek-gupta-64203354/