Prateek Gupta - Practical Data Science with Jupyter

All Aspiring Data Scientists

Who have chosen to solve this worlds problem with data

Prateek Gupta is a seasoned Data Science professional with nine years of experience in finding patterns, applying advanced statistical methods and algorithms to uncover hidden insights. His data-driven solutions maximize revenue, profitability, and ensure efficient operations management. He has worked with several multinational IT giants like HCL, Zensar, and Sapient.

He is a self-starter and committed data enthusiast with expertise in fishing, winery, and e-commerce domain. He has helped various clients with his machine learning expertise in automatic product categorization, sentiment analysis, customer segmentation, product recommendation engine, and object detection and recognition models. He is a firm believer in Hard work triumphs talent when talent doesnt work hard.

His keen area of interest is in the areas of cutting-edge research papers on machine learning and applications of natural language processing with computer vision techniques. In his leisure time, he enjoys sharing knowledge through his blog and motivates young minds to enter the exciting world of Data Science.

His Blog: http://dsbyprateekg.blogspot.com/

His LinkedIn Profile: www.linkedin.com/in/prateek-gupta-64203354

I would like to thank some of the brilliant knowledge sharing minds - Jason Brownlee Ph.D., Adrian Rosebrock, Ph.D., and Andrew Ng, from whom I have learned and am still learning many concepts. I would also like to thank open data science community, Kaggle and various data science bloggers for making data science and machine learning knowledge available to everyone.

I would also like to express my gratitude to almighty God, my parents, my wife Pragya, and my brother Anubhav, for being incredibly supportive throughout my life and for the writing of this book.

Finally, I would like to thank the entire BPB publications team, who made this book possible. Many thanks to Manish Jain, Nrip Jain, and Varun Jain for giving me the opportunity to write my second book.

Today, Data Science has become an indispensable part of every organization, for which employers are willing to pay top dollars to hire skilled professionals. Due to the rapidly changing needs of industry, data continues to grow and evolve, thereby increasing the demand for data scientists. However, the questions that continuously haunt every company are there enough highly-skilled individuals who can analyze how much data will be available, where it will come from, and what the advancement are in analytical techniques to serve them more significant insights? If you have picked up this book, you must have already come across these topics through talks or blogs from several experts and leaders in the industry.

To become an expert in any field, everyone must start from a point to learn. This book is designed with keeping such perspective in mind, to serve as your starting point in the field of data science. When I started my career in this field, I had little luck finding a compact guide that I could use to learn concepts of data science, practice examples, and revise them when faced with similar problems at hand. I soon realized Data Science is a very vast domain, and having all the knowledge in a small version of a book is highly impossible. Therefore, I decided I accumulate my experience in the form of this book, where youll gain essential knowledge and skill set required to become a data scientist, without wasting your valuable time finding material scattered across the internet.

I planned the chapters of this book in a chained form. In the first chapter, you will be made familiar with the data and the new data science skills set. The second chapter is all about setting up tools for the trade with the help of which you can practice the examples discussed in the book. In chapters three to six, you will learn all types of data structures in Python, which you will use in your day-to-day data science projects. In 7th chapter you will lean how to interact with different databases with Python. The eighth-chapter of this book will teach you the most used statistical concepts in data analysis. By the ninth chapter, you will be all set to start your journey of becoming a data scientist by learning how to read, load, and understand different types of data in Jupyter notebook for analysis. The tenth and eleventh chapters will guide you through different data cleaning and visualizing techniques.

From the twelfth chapter onwards, you will have to combine knowledge acquired from previous chapters to do data pre-processing of real-world use-cases. In chapters thirteen and fourteen, you will learn supervised and unsupervised machine learning problems and how to solve them. Chapters fifteen and sixteen will cover time series data and will teach you how to handle them. After covering the key concepts, I have included four different case studies, where you will apply all the knowledge acquired and practice solving real-world problems. The last three chapters of this book will make you industry-ready data scientists. Using best practices while structuring your project and use of GitHub repository along with your Data Science concepts will not make you feel naive, while working with other software engineering team.