How To Become A Data Scientist With Chatgpt A Beginner S Guide To Chatgpt Assisted Programming
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How To Become A Data Scientist With ChatGPT: A Beginner's Guide to ChatGPT-Assisted Programming
Are you aspiring to become a data scientist but feeling overwhelmed by the challenges of coding in programming languages? Are you new to data science and don't know how to code in any programming language? Look no further; this book is your comprehensive solution. Master the fundamentals of code generation with ChatGPT, learn to craft effective prompts, and navigate the DOs and DON'Ts of this invaluable tool. This book tackles the problem many aspiring data scientists face: the lack of programming skills. It's a step-by-step guide that utilizes the transformative potential of ChatGPT to empower you to code efficiently, streamline complex data analytics, and become a successful data scientist. The book contains: The role of ChatGPT in Data Science ChatGPT for Data Analytics ChatGPT-assisted programming Step-by-step approach to code generation in ChatGPT for data science Case Studies to Demonstrate Data Analysis with ChatGPT Whether you are an experienced data scientist or just starting, this book will be your trusted ally in the journey. It explores real-world applications, deepens your understanding of predictive analytics, and supercharges your data science projects. Don't let programming hurdles hold you back. Let ChatGPT assist you on your path to becoming a data scientist. Are you ready to become a data scientist without a programming background? This book is your definitive guide to a future where ChatGPT empowers your journey to become a data scientist.
Software Engineering for Data Scientists
Author: Catherine Nelson
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2024-04-16
Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more
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