Invent Your Own Computer Games with Python
Invent Your Own Computer Games with Python teaches you how to program in the Python language. Each chapter gives you the complete source code for a new game, and then teaches the programming concepts from the examples. Games include Guess the Number, Hangman, Tic Tac Toe, and Reversi. This book also has an introduction to making games with 2D graphics using the Pygame framework. Programming isn't hard. But it is hard to find learning materials that teach you to do interesting things with programming. Other computer books go over many topics most newbie coders don't need. This book will teach you how to program your own computer games. You'll learn a useful skill and have fun games to show for it! ...
Invent Your Own Computer Games with Python teaches you how to program in the Python language. Each chapter gives you the complete source code for a new game, and then teaches the programming concepts from the examples. Games include Guess the Number, Hangman, Tic Tac Toe, and Reversi. This book also has an introduction to making games with 2D graphics using the Pygame framework. Programming isn't hard. But it is hard to find learning materials that teach you to do interesting things with programming. Other computer books go over many topics most newbie coders don't need. This book will teach you how to program your own computer games. You'll learn a useful skill and have fun games to show for it! ...
Pro Power BI Desktop, 3rd Edition
Deliver eye-catching and insightful business intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features of Microsoft's continually evolving visualization product. New in this edition is help with storytelling - adapted to PCs, tablets, and smartphones - and the building of a data narrative. You will find coverage of templates and JSON style sheets, data model annotations, and the use of composite data sources. Also provided is an introduction to incorporating Python visuals and the much awaited Decomposition Tree visual. Pro Power BI Desktop shows you how to use source data to produce stunning dashboards and compelling reports that you mold into a data narrative to seize your audience's attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Convert raw data into clear, accurate, and interactive information with Microsoft's free self-ser ...
Deliver eye-catching and insightful business intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features of Microsoft's continually evolving visualization product. New in this edition is help with storytelling - adapted to PCs, tablets, and smartphones - and the building of a data narrative. You will find coverage of templates and JSON style sheets, data model annotations, and the use of composite data sources. Also provided is an introduction to incorporating Python visuals and the much awaited Decomposition Tree visual. Pro Power BI Desktop shows you how to use source data to produce stunning dashboards and compelling reports that you mold into a data narrative to seize your audience's attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Convert raw data into clear, accurate, and interactive information with Microsoft's free self-ser ...
The Self-Service Data Roadmap
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can't scale data science teams fast enough to keep up with the growing amounts of data to transform. What's the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. ...
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can't scale data science teams fast enough to keep up with the growing amounts of data to transform. What's the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. ...
Python re(gex)?
Scripting and automation tasks often need to extract particular portions of text from input data or modify them from one format to another. This book will help you learn Python Regular Expressions, a mini-programming language for all sorts of text processing needs. The book heavily leans on examples to present features of regular expressions one by one. It is recommended that you manually type each example and experiment with them. You should have prior experience working with Python, should know concepts like string formats, string methods, list comprehension and so on. ...
Scripting and automation tasks often need to extract particular portions of text from input data or modify them from one format to another. This book will help you learn Python Regular Expressions, a mini-programming language for all sorts of text processing needs. The book heavily leans on examples to present features of regular expressions one by one. It is recommended that you manually type each example and experiment with them. You should have prior experience working with Python, should know concepts like string formats, string methods, list comprehension and so on. ...
Machine Learning in the Oil and Gas Industry
Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industry covers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and prod ...
Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industry covers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and prod ...
Biopython: Tutorial and Cookbook
The Biopython Project is an international association of developers tools for computational molecular biology. Python is an object oriented, interpreted,flexible language that is becoming increasingly popular for scientific computing. Python is easy to learn, hasa very clear syntax and can easily be extended with modules written in C, C++ or FORTRAN. Thegoal of Biopython is to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and classes. Biopython features include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...), interfaces to commonand not-so-common programs (Clustalw, DSSP, MSMS...), a standard sequence class, various clusteringmodules, a KD tree data structure etc. ...
The Biopython Project is an international association of developers tools for computational molecular biology. Python is an object oriented, interpreted,flexible language that is becoming increasingly popular for scientific computing. Python is easy to learn, hasa very clear syntax and can easily be extended with modules written in C, C++ or FORTRAN. Thegoal of Biopython is to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and classes. Biopython features include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...), interfaces to commonand not-so-common programs (Clustalw, DSSP, MSMS...), a standard sequence class, various clusteringmodules, a KD tree data structure etc. ...
Introduction to Data Science
The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown. The book is divided into six parts: R, Data Visualization, Data Wrangling, Probability, Inference and Regression with R, Machine Learning, and Productivity Tools. Each part has several chapters meant to be presented as one lecture. The book includes dozens of exercises distributed across most chapters. ...
The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown. The book is divided into six parts: R, Data Visualization, Data Wrangling, Probability, Inference and Regression with R, Machine Learning, and Productivity Tools. Each part has several chapters meant to be presented as one lecture. The book includes dozens of exercises distributed across most chapters. ...
Real-World Python
With its emphasis on project-based practice, Real World Python will take you from playing with syntax to writing complete programs in no time. You'll conduct experiments, explore statistical concepts, and solve novel problems that have frustrated geniuses throughout history, like detecting distant exoplanets, as you continue to build your Python skills. Chapters begin with a clearly defined project goal and a discussion of ways to attack the problem, followed by a mission designed to make you think like a programmer. You'll direct a Coast Guard search-and-rescue effort, plot and execute a NASA flight to the moon, protect access to a secure lab using facial recognition, and more. Along the way you'll learn how to: Use libraries like matplotlib, NumPy, Bokeh, pandas, Requests, Beautiful Soup, and turtle; Work with Natural Language Processing and computer vision modules like NLTK and OpenCV; Write a program to detect and track objects moving across a starfield; Scrape speeches from the ...
With its emphasis on project-based practice, Real World Python will take you from playing with syntax to writing complete programs in no time. You'll conduct experiments, explore statistical concepts, and solve novel problems that have frustrated geniuses throughout history, like detecting distant exoplanets, as you continue to build your Python skills. Chapters begin with a clearly defined project goal and a discussion of ways to attack the problem, followed by a mission designed to make you think like a programmer. You'll direct a Coast Guard search-and-rescue effort, plot and execute a NASA flight to the moon, protect access to a secure lab using facial recognition, and more. Along the way you'll learn how to: Use libraries like matplotlib, NumPy, Bokeh, pandas, Requests, Beautiful Soup, and turtle; Work with Natural Language Processing and computer vision modules like NLTK and OpenCV; Write a program to detect and track objects moving across a starfield; Scrape speeches from the ...
SQL Server Data Automation Through Frameworks
Learn to automate SQL Server operations using frameworks built from metadata-driven stored procedures and SQL Server Integration Services (SSIS). Bring all the power of Transact-SQL (T-SQL) and Microsoft .NET to bear on your repetitive data, data integration, and ETL processes. Do this for no added cost over what you've already spent on licensing SQL Server. The tools and methods from this book may be applied to on-premises and Azure SQL Server instances. The SSIS framework from this book works in Azure Data Factory (ADF) and provides DevOps personnel the ability to execute child packages outside a project - functionality not natively available in SSIS. Frameworks not only reduce the time required to deliver enterprise functionality, but can also accelerate troubleshooting and problem resolution. You'll learn in this book how frameworks also improve code quality by using metadata to drive processes. Much of the work performed by data professionals can be classified as "drudge work" ...
Learn to automate SQL Server operations using frameworks built from metadata-driven stored procedures and SQL Server Integration Services (SSIS). Bring all the power of Transact-SQL (T-SQL) and Microsoft .NET to bear on your repetitive data, data integration, and ETL processes. Do this for no added cost over what you've already spent on licensing SQL Server. The tools and methods from this book may be applied to on-premises and Azure SQL Server instances. The SSIS framework from this book works in Azure Data Factory (ADF) and provides DevOps personnel the ability to execute child packages outside a project - functionality not natively available in SSIS. Frameworks not only reduce the time required to deliver enterprise functionality, but can also accelerate troubleshooting and problem resolution. You'll learn in this book how frameworks also improve code quality by using metadata to drive processes. Much of the work performed by data professionals can be classified as "drudge work" ...
Artificial Intelligence in Finance
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI); Understand why data-driven finance, AI, and machin ...
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI); Understand why data-driven finance, AI, and machin ...