Workshops In Python And Data Science
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Workshops in Python and Data Science
This workbook is for the beginner data scientist or anyone who is interested in Python, data science and machine learning. It includes 1 year of FREE premium membership. which gives you access to online video tutorials, interactive Jupyter notebooks, lesson quizzes and the complete Math@Work premium membership workshop library.
Data Science Crash Course for Beginners with Python: Fundamentals and Practices with Python
Data Science Crash Course for Beginners with Python Data Science is here to stay. The tremendous growth in the volume, velocity, and variety of data has a substantial impact on every aspect of a business. While data continues to grow exponentially, accuracy remains a problem. This is where data scientists play a decisive role. A data scientist analyzes data, discovers new insights, paints a picture, and creates a vision. And a competent data scientist will provide a business with the competitive edge it needs and address pressing business problems. Data Science Crash Course for Beginners with Python presents you with a hands-on approach to learn data science fast. How Is This Book Different? Every book by AI Publishing has been carefully crafted. This book lays equal emphasis on the theoretical sections as well as the practical aspects of data science. Each chapter provides the theoretical background behind the numerous data science techniques, and practical examples explain the working of these techniques. In the Further Reading section of each chapter, you will find the links to informative data science posts. This book presents you with the tools and packages you need to kick-start data science projects to resolve problems of practical nature. Special emphasis is laid on the main stages of a data science pipeline--data acquisition, data preparation, exploratory data analysis, data modeling and evaluation, and interpretation of the results. In the Data Science Resources section, links to data science resources, articles, interviews, and data science newsletters are provided. The author has also put together a list of contests and competitions that you can try on your own. Another added benefit of buying this book is you get instant access to all the learning material presented with this book-- PDFs, Python codes, exercises, and references--on the publisher's website. They will not cost you an extra cent. The datasets used in this book can be downloaded at runtime, or accessed via the Resources/Datasets folder. The author simplifies your learning by holding your hand through everything. The step by step description of the installation of the software you need for implementing the various data science techniques in this book is guaranteed to make your learning easier. So, right from the beginning, you can experiment with the practical aspects of data science. You'll also find the quick course on Python programming in the second and third chapters immensely helpful, especially if you are new to Python. This book gives you access to all the codes and datasets. So, access to a computer with the internet is sufficient to get started. The topics covered include: Introduction to Data Science and Decision Making Python Installation and Libraries for Data Science Review of Python for Data Science Data Acquisition Data Preparation (Preprocessing) Exploratory Data Analysis Data Modeling and Evaluation Using Machine Learning Interpretation and Reporting of Findings Data Science Projects Key Insights and Further Avenues Click the BUY button to start your Data Science journey.
The Data Wrangling Workshop
A beginner's guide to simplifying Extract, Transform, Load (ETL) processes with the help of hands-on tips, tricks, and best practices, in a fun and interactive way Key FeaturesExplore data wrangling with the help of real-world examples and business use casesStudy various ways to extract the most value from your data in minimal timeBoost your knowledge with bonus topics, such as random data generation and data integrity checksBook Description While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you're a beginner, then The Data Wrangling Workshop will help to break down the process for you. You'll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you'll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you'll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources. What you will learnGet to grips with the fundamentals of data wranglingUnderstand how to model data with random data generation and data integrity checksDiscover how to examine data with descriptive statistics and plotting techniquesExplore how to search and retrieve information with regular expressionsDelve into commonly-used Python data science librariesBecome well-versed with how to handle and compensate for missing dataWho this book is for The Data Wrangling Workshop is designed for developers, data analysts, and business analysts who are looking to pursue a career as a full-fledged data scientist or analytics expert. Although this book is for beginners who want to start data wrangling, prior working knowledge of the Python programming language is necessary to easily grasp the concepts covered here. It will also help to have a rudimentary knowledge of relational databases and SQL.