› › How To Become A Data Scientist With Chatgpt A Beginner S Guide To Chatgpt Assisted Programming

How To Become A Data Scientist With ChatGPT: A Beginner's Guide to ChatGPT-Assisted Programming

Author : Rafiq Muhammad

Publisher : Rafiq Muhammad

Page : 152 pages

File Size : 27,34 MB

Release : 2024-01-13

Category : Young Adult Nonfiction

ISBN : 9198900706

Get eBook

Book Description

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.



Data Science from Scratch

Author : Joel Grus

Publisher : "O'Reilly Media, Inc."

Page : 336 pages

File Size : 28,50 MB

Release : 2015-04-14

Category : Computers

ISBN : 1491904399

Get eBook

Book Description

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases



Software Engineering for Data Scientists

Author : Catherine Nelson

Publisher : "O'Reilly Media, Inc."

Page : 248 pages

File Size : 50,56 MB

Release : 2024-04-16

Category : Computers

ISBN : 1098136160

Get eBook

Book Description

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



Storytelling with Data

Author : Cole Nussbaumer Knaflic

Publisher : John Wiley & Sons

Page : 284 pages

File Size : 15,63 MB

Release : 2015-10-09

Category : Mathematics

ISBN : 1119002265

Get eBook

Book Description

Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!



SQL for Data Scientists

Author : Renee M. P. Teate

Publisher : John Wiley & Sons

Page : 400 pages

File Size : 26,47 MB

Release : 2021-08-17

Category : Computers

ISBN : 1119669391

Get eBook

Book Description

Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!



Build a Career in Data Science

Author : Emily Robinson

Publisher : Manning

Page : 352 pages

File Size : 31,72 MB

Release : 2020-03-24

Category : Computers

ISBN : 1617296244

Get eBook

Book Description

Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder



Python Object-Oriented Programming

Author : Steven F. Lott

Publisher : Packt Publishing Ltd

Page : 715 pages

File Size : 33,26 MB

Release : 2021-07-02

Category : Computers

ISBN : 1801075239

Get eBook

Book Description

A comprehensive guide to exploring modern Python through data structures, design patterns, and effective object-oriented techniques Key Features Build an intuitive understanding of object-oriented design, from introductory to mature programs Learn the ins and outs of Python syntax, libraries, and best practices Examine a machine-learning case study at the end of each chapter Book Description Object-oriented programming (OOP) is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Python Object-Oriented Programming, Fourth Edition dives deep into the various aspects of OOP, Python as an OOP language, common and advanced design patterns, and hands-on data manipulation and testing of more complex OOP systems. These concepts are consolidated by open-ended exercises, as well as a real-world case study at the end of every chapter, newly written for this edition. All example code is now compatible with Python 3.9+ syntax and has been updated with type hints for ease of learning. Steven and Dusty provide a comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, and explain how they work together with Python's classes and data structures to facilitate good design. In addition, the book also features an in-depth look at Python's exception handling and how functional programming intersects with OOP. Two very powerful automated testing systems, unittest and pytest, are introduced. The final chapter provides a detailed discussion of Python's concurrent programming ecosystem. By the end of the book, you will have a thorough understanding of how to think about and apply object-oriented principles using Python syntax and be able to confidently create robust and reliable programs. What you will learn Implement objects in Python by creating classes and defining methods Extend class functionality using inheritance Use exceptions to handle unusual situations cleanly Understand when to use object-oriented features, and more importantly, when not to use them Discover several widely used design patterns and how they are implemented in Python Uncover the simplicity of unit and integration testing and understand why they are so important Learn to statically type check your dynamic code Understand concurrency with asyncio and how it speeds up programs Who this book is for If you are new to object-oriented programming techniques, or if you have basic Python skills and wish to learn how and when to correctly apply OOP principles in Python, this is the book for you. Moreover, if you are an object-oriented programmer coming from other languages or seeking a leg up in the new world of Python, you will find this book a useful introduction to Python. Minimal previous experience with Python is necessary.



Applied Predictive Modeling

Author : Max Kuhn

Publisher : Springer Science & Business Media

Page : 595 pages

File Size : 44,7 MB

Release : 2013-05-17

Category : Medical

ISBN : 1461468493

Get eBook

Book Description

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.



Practical Programming

Author : Paul Gries

Publisher :

Page : 375 pages

File Size : 48,18 MB

Release : 2013

Category : Computers

ISBN : 9781937785451

Get eBook

Book Description

Previous edition: published as by Jennifer Campbell ... [et al]. 2009.



Data-Oriented Programming

Author : Yehonathan Sharvit

Publisher : Simon and Schuster

Page : 422 pages

File Size : 26,92 MB

Release : 2022-09-27

Category : Computers

ISBN : 1638356785

Get eBook

Book Description

Eliminate the unavoidable complexity of object-oriented designs. The innovative data-oriented programming paradigm makes your systems less complex by making it simpler to access and manipulate data. In Data-Oriented Programming you will learn how to: Separate code from data Represent data with generic data structures Manipulate data with general-purpose functions Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data Benefit from polymorphism without objects Debug programs without a debugger Data-Oriented Programming is a one-of-a-kind guide that introduces the data-oriented paradigm. This groundbreaking approach represents data with generic immutable data structures. It simplifies state management, eases concurrency, and does away with the common problems you’ll find in object-oriented code. The book presents powerful new ideas through conversations, code snippets, and diagrams that help you quickly grok what’s great about DOP. Best of all, the paradigm is language-agnostic—you’ll learn to write DOP code that can be implemented in JavaScript, Ruby, Python, Clojure, and also in traditional OO languages like Java or C#. Forewords by Michael T. Nygard and Ryan Singer. About the technology Code that combines behavior and data, as is common in object-oriented designs, can introduce almost unmanageable complexity for state management. The Data-oriented programming (DOP) paradigm simplifies state management by holding application data in immutable generic data structures and then performing calculations using non-mutating general-purpose functions. Your applications are free of state-related bugs and your code is easier to understand and maintain. About the book Data-Oriented Programming teaches you to design software using the groundbreaking data-oriented paradigm. You’ll put DOP into action to design data models for business entities and implement a library management system that manages state without data mutation. The numerous diagrams, intuitive mind maps, and a unique conversational approach all help you get your head around these exciting new ideas. Every chapter has a lightbulb moment that will change the way you think about programming. What's inside Separate code from data Represent data with generic data structures Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data About the reader For programmers who have experience with a high-level programming language like JavaScript, Java, Python, C#, Clojure, or Ruby. About the author Yehonathan Sharvit has over twenty years of experience as a software engineer. He blogs, speaks at conferences, and leads Data-Oriented Programming workshops around the world. Table of Contents PART 1 FLEXIBILITY 1 Complexity of object-oriented programming 2 Separation between code and data 3 Basic data manipulation 4 State management 5 Basic concurrency control 6 Unit tests PART 2 SCALABILITY 7 Basic data validation 8 Advanced concurrency control 9 Persistent data structures 10 Database operations 11 Web services PART 3 MAINTAINABILITY 12 Advanced data validation 13 Polymorphism 14 Advanced data manipulation 15 Debugging