TensorFlow 2 Pocket ReferenceThis easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.
When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases.
Understand best practices in TensorFlow model patterns and ML workflows; Use code snippets as templates in building TensorFlow models and workflows; Save development time by integrating prebuilt models in TensorFlow Hub; Make informed design choices about data ingestion, training paradigms, model saving, and inferencing; Address common scenari ...
Mastering Tableau 2021, 3rd EditionTableau is one of the leading business intelligence (BI) tools used to solve data analysis challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain.
Updated with fresh topics including Quick Level of Detail expressions, the newest Tableau Server features, Einstein Discovery, and more, this book covers essential Tableau concepts and advanced functionalities. Leveraging Tableau Hyper files and using Prep Builder, you'll be able to perform data preparation and handling easily. You'll gear up to perform complex joins, spatial joins, unions, and data blending tasks using practical examples. Following this, you'll learn how to execute data densification and further explore expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. You'll also learn about improving dashboard performance, connecting to Tableau Server and understanding data visualization with examples. Finally, you'll cov ...
Advanced Analytics with Transact-SQLLearn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python. This book shows you how to compute statistical measures using your existing skills in T-SQL. You will learn how to calculate descriptive statistics, including centers, spreads, skewness, and kurtosis of distributions. You will also learn to find associations between pairs of variables, including calculating linear regression formulas and confidence levels with definite integration.
No analysis is good without data quality. Advanced Analytics with Transact-SQL introduces data quality issues and shows you how to check for completeness and accuracy, and measure improvements in data quality over time. The book also explains how to optimize queries involving temporal data, such as when you search for overlapping intervals. More advanced time-oriented information in the book includes hazard and surviva ...
SQL Pocket Guide, 4th EditionIf you use SQL in your day-to-day work as a data analyst, data scientist, or data engineer, this popular pocket guide is your ideal on-the-job reference. You'll find many examples that address the language's complexities, along with key aspects of SQL used in Microsoft SQL Server, MySQL, Oracle Database, PostgreSQL, and SQLite.
In this updated edition, author Alice Zhao describes how these database management systems implement SQL syntax for both querying and making changes to a database. You'll find details on data types and conversions, regular expression syntax, window functions, pivoting and unpivoting, and more.
Quickly look up how to perform specific tasks using SQL; Apply the book's syntax examples to your own queries; Update SQL queries to work in five different database management systems; Connect Python and R to a relational database; Look up frequently asked SQL questions in the "How Do I?" chapter. ...
Learning AlgorithmsWhen it comes to writing efficient code, every software professional needs to have an effective working knowledge of algorithms. In this practical book, author George Heineman (Algorithms in a Nutshell) provides concise and informative descriptions of key algorithms that improve coding. Software developers, testers, and maintainers will discover how algorithms solve computational problems creatively.
Each chapter builds on earlier chapters through eye-catching visuals and a steady rollout of essential concepts, including an algorithm analysis to classify the performance of every algorithm presented in the book. At the end of each chapter, you'll get to apply what you've learned to a novel challenge problem - simulating the experience you might find in a technical code interview.
With this book, you will: Examine fundamental algorithms central to computer science and software engineering; Learn common strategies for efficient problem solving - such as divide and conquer, dynamic p ...
Data Science at the Command Line, 2nd EditionThis thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools-useful whether you work with Windows, macOS, or Linux.
You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, engineers, system administrators, and researchers.
Obtain data from websites, APIs, databases, and spreadsheets; Perform scrub operations on text, CSV, HTM, XML, and JSON files; Explore data, compute descriptive statistics, and create visualizations; M ...
Just Enough RIf your job involves working with data in any manner, you cannot afford to ignore the R revolution! If your domain is called data analysis, analytics, informatics, data science, reporting, business intelligence, data management, big data, or visualization, you just have to learn R as this programming language is a game-changing sledgehammer.
However, if you have looked at a standard text on R or read some of the online discussions, you might feel that there is a steep learning curve of six months or more to grok the language. I will debunk this myth through my book by focusing on practical essentials instead of theory.
If you have programmed in some language in the past (whether that language be SAS, SPSS, C, C++, C#, Java, Python, Perl, Visual Basic, Ruby, Scala, shell scripts, or plain old SQL), even if you are rusty, this book will get you up and running with R in a single day, writing programs for data analysis and visualization. ...
Learning Test-Driven DevelopmentYour code is a testament to your skills as a developer. No matter what language you use, code should be clean, elegant, and uncluttered. By using test-driven development (TDD), you'll write code that's easy to understand, retains its elegance, and works for months, even years, to come. With this indispensable guide, you'll learn how to use TDD with three different languages: Go, JavaScript, and Python.
Author Saleem Siddiqui shows you how to tackle domain complexity using a unit test-driven approach. TDD partitions requirements into small, implementable features, enabling you to solve problems irrespective of the languages and frameworks you use. With Learning Test-Driven Development at your side, you'll learn how to incorporate TDD into your regular coding practice.
This book helps you: Use TDD's divide-and-conquer approach to tame domain complexity; Understand how TDD works across languages, testing frameworks, and domain concepts; Learn how TDD enables continuous integration; ...
Pandas in ActionPandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You'll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data.
Data analysis with Python doesn't have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It's a perfect way to up your data game.
Pandas in Action introduces Python-based data analysis using the amazing pandas library. You'll learn to automate repetitive operations and gain deeper insights into your data that would be impractical - or impossible - in Excel. Each chapt ...
The Little ASP.NET Core BookIf you're new to programming, this book will introduce you to thepatterns and concepts used to build modern web applications. You'lllearn how to build a web app (and how the big pieces fit together) by building something from scratch! While this little book won't be able tocover absolutely everything you need to know about programming, it'llgive you a starting point so you can learn more advanced topics.
If you already code in a backend language like Node, Python, Ruby, Go,or Java, you'll notice a lot of familiar ideas like MVC, view templates, anddependency injection. The code will be in C#, but it won't look toodifferent from what you already know.
If you're an ASP.NET MVC developer, you'll feel right at home! ASP.NETCore adds some new tools and reuses (and simplifies) the things youalready know. I'll point out some of the differences below.
No matter what your previous experience with web programming, thisbook will teach you everything you need to create a simple and useful ...