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Eleven-year-old Koi is eager to kill a python as part of the rite of passage to manhood among his Betetelan people, but in the meantime he must face a bully who is picking on an old hunter in the village marketplace.
Leverage the power of Python to clean, scrape, analyze, and visualize your data About This Book Clean, format, and explore your data using the popular Python libraries and get valuable insights from it Analyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and more Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation from which to analyze data with varying complexity. A working knowledge of Python (and a strong interest in playing with your data) is recommended. What You Will Learn Understand the importance of data analysis and master its processing steps Get comfortable using Python and its associated data analysis libraries such as Pandas, NumPy, and SciPy Clean and transform your data and apply advanced statistical analysis to create attractive visualizations Analyze images and time series data Mine text and analyze social networks Perform web scraping and work with different databases, Hadoop, and Spark Use statistical models to discover patterns in data Detect similarities and differences in data with clustering Work with Jupyter Notebook to produce publication-ready figures to be included in reports In Detail Data analysis is the process of applying logical and analytical reasoning to study each component of data present in the system. Python is a multi-domain, high-level, programming language that offers a range of tools and libraries suitable for all purposes, it has slowly evolved as one of the primary languages for data science. Have you ever imagined becoming an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? If yes, look no further, this is the course you need! In this course, we will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations. You'll then find your way around different data and numerical problems, get to grips with Spark and HDFS, and set up migration scripts for web mining. You'll be able to quickly and accurately perform hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. Finally, you will delve into advanced techniques such as performing regression, quantifying cause and effect using Bayesian methods, and discovering how to use Python's tools for supervised machine learning. The course provides you with highly practical content explaining data analysis with Python, from the following Packt books: Getting Started with Python Data Analysis. Python Data Analysis Cookbook. Mastering Python Data Analysis. By the end of this course, you will have all the knowledge you need to analyze your data with varying complexity levels, and turn it into actionable insights. Style and approach Learn Python data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. It offers you a useful way of analyzing the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of data analysis.
Games and activities that teach kids ages 10+ to code with Python Learning to code isn't as hard as it sounds—you just have to get started! Coding for Kids: Python starts kids off right with 50 fun, interactive activities that teach them the basics of the Python programming language. From learning the essential building blocks of programming to creating their very own games, kids will progress through unique lessons packed with helpful examples—and a little silliness! Kids will follow along by starting to code (and debug their code) step by step, seeing the results of their coding in real time. Activities at the end of each chapter help test their new knowledge by combining multiple concepts. For young programmers who really want to show off their creativity, there are extra tricky challenges to tackle after each chapter. All kids need to get started is a computer and this book. This beginner's guide to Python for kids includes: 50 Innovative exercises—Coding concepts come to life with game-based exercises for creating code blocks, drawing pictures using a prewritten module, and more. Easy-to-follow guidance—New coders will be supported by thorough instructions, sample code, and explanations of new programming terms. Engaging visual lessons—Colorful illustrations and screenshots for reference help capture kids' interest and keep lessons clear and simple. Encourage kids to think independently and have fun learning an amazing new skill with this coding book for kids.
This volume constitutes the refereed proceedings of the 7th International Conference on Modelling and Development of Intelligent Systems, MDIS 2020, held in Sibiu, Romania, in October 2020. Due to the COVID-19 pandemic the conference was held online. The 25 revised full papers presented in the volume were carefully reviewed and selected from 57 submissions. The papers are organized in topical sections on ​evolutionary computing; intelligent systems for decision support; machine learning; mathematical models for development of intelligent systems; modelling and optimization of dynamic systems; ontology engineering.
Master Python and become a programmer - even if you never thought you could. This breakthrough book and CD can help practically anyone get started in programming. Zed A. Shaw teaches the Python programming language through a series of 52 brilliantly-crafted exercises.
Easy-to-use, comprehensive reference covering the less common species encountered in general veterinary practice Handbook of Exotic Pet Medicine provides easy-to-access, detailed information on a wide variety of exotic species that can be encountered in general veterinary practice. Offering excellent coverage of topics such as basic techniques, preventative health measures, and a formulary for each species, each chapter uses the same easy-to-follow format so that users can find information quickly while working in the clinic. Presented in full colour, with over 400 photographs, the book gives small animal practitioners the confidence to handle and treat more familiar pets such as budgerigars, African grey parrots, bearded dragons, corn snakes, tortoises, pygmy hedgehogs, hamsters and rats. Other species that may be presented less frequently including skunks, marmosets, sugar gliders, koi carp, chameleons and terrapins are also covered in detail to enable clinicians to quickly access relevant information. Provides comprehensive coverage of many exotic pet species that veterinarians may encounter in general practice situations Presents evidence-based discussions of topics including biological parameters, husbandry, clinical evaluation, hospitalization requirements, common medical and surgical conditions, radiographic imaging, and more The Handbook of Exotic Pet Medicine is an ideal one-stop reference for the busy general practitioner seeing the occasional exotic animal, veterinary surgeons with an established exotic animal caseload, veterinary students and veterinary nurses wishing to further their knowledge.
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building Python programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis. This book is one of a series of textbooks in machine learning by the same Author. Other titles are: Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679) Statistical Learning with Math and Pyth (https://www.springer.com/gp/book/9789811578762) Sparse Estimation with Math and R
Lilah's heart has been broken by betrayal, and she's looking for a fresh start. She goes looking in Japan where she accepts a position at a prestigious school, only to find more emotional turmoil and physical threat.