Data Analysis with Microsoft Power BI - Brian Larson 2020

Learn Power BI A Beginner’s Guide to Developing Interactive Business Intelligence Solutions.pdf

Power BI MVP Book A book of tricks and techniques for working with Power BI 2019.pdf

Изучите сложную работу DAX и механизмы, необходимые для решения сложных задач Power BI. Эта книга полностью посвящена DAX (выражениям анализа данных), языку формул, используемому в Power BI - ведущем приложении Microsoft для самообслуживания в области бизнес-аналитики, - и охватывает другие продукты, такие как PowerPivot и SQL Server Analysis Services Tabular. Вы узнаете, как использовать передовые приложения DAX для решения сложных задач.
Часто задача кажется сложной из-за отсутствия понимания или неправильного понимания основных принципов и того, как определенные компоненты взаимодействуют друг с другом. Авторы этой книги используют решения и примеры, чтобы научить вас, как решать сложные проблемы. Они объясняют сложную работу важных понятий, таких как контекст фильтра и переход контекста. Вы узнаете, как Power BI, объединяя строительные блоки DAX (такие как меры, фильтрация таблиц и линии данных), может обеспечить исключительную аналитическую мощность.

Supercharge Power BI Power BI Is Better When You Learn to Write DAX.pdf

Microsoft Power BI Complete Reference by Brett PowellDevin Knight.pdf

The Definitive Guide to DAX-Business intelligence with Microsoft Excel, SQL Server Analysis Services, and Power BI.pdf

Mastering Microsoft Power BI: Expert techniques for effective data analytics and business intelligence

Проектируйте, создавайте и управляйте надежными решениями Power BI, чтобы получать значимые бизнес-идеи

Ключевая особенность
- Освойте все функции панели мониторинга и отчетности Microsoft Power BI
- Объединяйте данные из разных источников, создавайте потрясающие визуализации и публикуйте свои отчеты на разных платформах

This book supplies insightful, detailed guidance on fundamental data mining techniques. The book guides readers through the use of Python software for developing predictive models and techniques in order to describe and find patterns in data. The authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, with a focus on analytics rather than programming.

Professional developers know the many benefits of writing application code that’s clean, well-organized, and easy to maintain. By learning and following established patterns and best practices, you can take your code and your career to a new level.
With Practices of the Python Pro, you’ll learn to design professional-level, clean, easily maintainable software at scale using the incredibly popular programming language, Python. You’ll find easy-to-grok examples that use pseudocode and Python to introduce software development best practices, along with dozens of instantly useful techniques that will help you code like a pro.

Designing Event-Driven Systems Concepts and Patterns for Streaming Services with Apache Kafka.pdf

Mastering pandas for Finance.pdf

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6
- Use the IPython shell and Jupyter notebook for exploratory computing
- Learn basic and advanced features in NumPy (Numerical Python)
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examples

Python for Finance
Mastering Data-Driven Finance
Second Edition
Yves Hilpisch

Practical DataOps Delivering Agile Data Science At Scale by Harvinder Atwal.pdf

Avoiding Data Pitfalls How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations.pdf

The DevOps Handbook How to Create World-Class Agility, Reliability, and Security in Technology Organizations by Gene Kim, Jez Humble, and Patrick Debois.pdf

DevOps in Python Infrastructure as Python by Zadka, Moshe
Explore and apply best practices for efficient application deployment. This book draws upon author Moshe Zadka's years of Dev Ops experience and focuses on the parts of Python, and the Python ecosystem, that are relevant for DevOps engineers.

You'll start by writing command-line scripts and automating simple DevOps-style tasks. You'll then move on to more advanced cases, like using Jupyter as an auditable remote-control panel, and writing Ansible and Salt extensions. This work also covers how to use the AWS API to manage cloud infrastructure, and how to manage Python programs and environments on remote machines.

Take a modern API-first approach to creating 3 different Django back-ends: a Library API, Todo API, and a Blog API with user authentication, permissions, proper documentation, and more. You'll even learn how to connect them to a React front-end for a truly full-stack web application.

Topics covered include Django 3.0 and Python 3.7, Django REST Framework, HTTP, API permissions, custom user models, user authentication (basic, sessions, and tokens), viewsets, routers, schemas, and documentation.