How To Use Praw
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Practical Data Science with Python
Author: Nathan George
language: en
Publisher: Packt Publishing Ltd
Release Date: 2021-09-30
Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Genetic Programming
This book constitutes the refereed proceedings of the 24th European Conference on Genetic Programming, EuroGP 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events, EvoCOP, EvoMUSART, and EvoApplications. The 11 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 27 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover interesting topics including developing new operators for variants of GP algorithms, as well as exploring GP applications to the optimisation of machine learning methods and the evolution of complex combinational logic circuits.
From Social Science to Data Science
From Social Science to Data Science is a fundamental guide to scaling up and advancing your programming skills in Python. From beginning to end, this book will enable you to understand merging, accessing, cleaning and interpreting data whilst gaining a deeper understanding of computational techniques and seeing the bigger picture. With key features such as tables, figures, step-by-step instruction and explanations giving a wider context, Hogan presents a clear and concise analysis of key data collection and skills in Python.