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A common-sense guide to data structures and algorithms : level up your core programming skills / Macdonald, Brian.

Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today’s web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You’ll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your code faster and more scalable.


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Beginning DAX with Power BI : the SQL pro's guide to better business intelligence / Seamark, Philip.

Attention all SQL Pros, DAX is not just for writing Excel-based formulas! Get hands-on learning and expert advice on how to use the vast capabilities of the DAX language to solve common data modeling challenges. Beginning DAX with Power BI teaches key concepts such as mapping techniques from SQL to DAX, filtering, grouping, joining, pivoting, and using temporary tables, all aimed at the SQL professional. Join author Philip Seamark as he guides you on a journey through typical business data transformation scenarios and challenges, and teaches you, step-by-step, how to resolve challenges using DAX. Tips, tricks, and shortcuts are included and explained, along with examples of the SQL equivalent, in order to accelerate learning. Examples in the book range from beginner to advanced, with plenty of detailed explanation when walking through each scenario.

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Breaking and entering : the extraordinary story of a hacker called "Alien" / Smith, Jeremy N.

"This taut, true thriller takes a deep dive into a dark world that touches us all, as seen through the brilliant, breakneck career of an extraordinary hacker - a woman known only as Alien"--Provided by publisher.

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Coders : the making of a new tribe and the remaking of the world / Thompson, Clive

"From acclaimed tech writer Clive Thompson, a brilliant and immersive anthropological reckoning with the most powerful tribe in the world today, computer programmers - where they come from, how they think, what makes for greatness in their world, and what should give us pause"--Provided by publisher.

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Introduction to machine learning with Python : a guide for data scientists / Guido, Sarah

Data mining
Programming languages (Electronic computers)
Python (Computer program language)

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Learning Dapr : building distributed cloud native applications / Bai, Haishi

Get the authoritative guide to Dapr, the distributed application runtime that works with new and existing programming languages alike. Written by the model's creators, this introduction shows you how Dapr not only unifies stateless, stateful, and actor programming models but also runs everywhere--in the cloud or on the edge. Authors Haishi Bai and Yaron Schneider with Microsoft's Azure CTO team explain that, with Dapr, you don't need to include any SDKs or libraries in your user code. Instead, you automatically get flexible binding, state management, the actor pattern, pub-sub, reliable messaging, and many more features. This book shows developers, architects, CIOs, students, and computing enthusiasts how to get started with Dapr.

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Practical recommender systems / Falk, Kim.

Summary Online recommender systems help users find movies, jobs, restaurants--even romance! There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application! Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you'll see how to collect user data and produce personalized recommendations. You'll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you'll encounter as your site grows. About the technology Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors. What's inside >/p> How to collect and understand user behavior Collaborative and content-based filtering Machine learning algorithms Real-world examples in Python About the reader Readers need intermediate programming and database skills. About the author Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems.

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Python for data analysis : data wrangling with Pandas, NumPy, and IPython / Mckinney, Wes.

Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python? The second edition of this hands-on guide--updated for Python 3.5 and Pandas 1.0--is packed with practical cases studies that show you how to effectively solve a broad set of data analysis problems, using Python libraries such as NumPy, pandas, matplotlib, and IPython. Written by Wes McKinney, the main author of the pandas library, Python for Data Analysis also serves as a practical, modern introduction to scientific computing in Python for data-intensive applications. It's ideal for analysts new to Python and for Python programmers new to scientific computing.

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Python workout : 50 ten-minute exercises / Lerner, Reuven M.

"Python Workout presents 50 exercises that focus on key Python 3 features. In it, expert Python coach Reuven Lerner guides you through a series of small projects, practicing the skills you need to tackle everyday tasks. You'll appreciate the clear explanations of each technique, and you can watch Reuven solve each exercise in the accompanying videos."