Foundations of agile Python development by Jeff Younker.pdf

Agile, Revision Control, Test-Driven Development, Unit Testing, Stubs, Mock-Ups, Code Coverage, and more. This stuff requires a top-down approach -- understanding it on a high level before being able to use it to its fullest. Sure, you probably use CVS or Subversion and test your code. But this book gives you the full picture, and ties everything together.

https://refactoring.guru/ru - Прикольный сайт про паттерны и рефакторинг. Есть множество примеров по паттернам python https://refactoring.guru/ru/design-patterns/python

Еще паттерны проектирования описаны тут https://github.com/pkolt/design_patterns

Data Science in Production Building Scalable Model Pipelines with Python by Ben G Weber.pdf

Interactive Data Visualization With Python present your data as an. by Abha Belorkar.pdf

Practical Web Scraping for Data Science Best Practices and Examples with Python by Seppe vanden Broucke, Bart Baesens.pdf

Web Scraping with Python Collecting More Data from the Modern Web by Ryan Mitchell.pdf

Python Web Scraping Cookbook Over 90 proven recipes to get you scraping with Python, micro services, Docker and AWS.pdf

Python Web Scraping Cookbook

Python Web Scraping Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python.pdf

Learning the Pandas Library Python Tools for Data Munging, Analysis, and Visual.pdf

Learn Algorithmic Trading Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis.pdf

What you will learn
🏷 Understand the components of modern algorithmic trading systems and strategies
🏷 Apply machine learning in algorithmic trading signals and strategies using Python
🏷 Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more
🏷 Quantify and build a risk management system for Python trading strategies
🏷 Build a backtester to run simulated trading strategies for improving the performance of your trading bot
🏷 Deploy and incorporate trading strategies in the live market to maintain and improve profitability

A General Introduction to Data Analytics by João Moreira, Andre Carvalho, Tomás Horvath.pdf

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming.

Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems.

Information Quality: The Potential of Data and Analytics to Generate Knowledge
Ron S. Kenett, Galit Shmueli, Ron Kenett

This book:
📌 Explains how to integrate the notions of goal, data, analysis and utility that are the main building blocks of data analysis within any domain.
📌 Presents a framework for integrating domain knowledge with data analysis.
📌 Provides a combination of both methodological and practical aspects of data analysis.
📌 Discusses issues surrounding the implementation and integration of InfoQ in both academic programmes and business / industrial projects.
📌 Showcases numerous case studies in a variety of application areas such as education, healthcare, official statistics, risk management and marketing surveys.
📌 Presents a review of software tools from the InfoQ perspective along with example datasets on an accompanying website.

Урок #2 по Microsoft Power BI. Примеры использования языка DAX для Power BI
DAX — это набор функций, операторов и констант, которые можно использовать в формуле или выражении, чтобы подсчитывать и возвращать одно или несколько значений. Говоря проще, DAX помогает создавать новую информацию из данных, уже имеющихся в модели.

https://youtu.be/82e5FAn_s0s

Principles of Data Wrangling. Practical Techniques for data preparation. 2017

+ Understand what kind of data is available
+ Choose which data to use and at what level of detail
+ Meaningfully combine multiple sources of data
+ Decide how to distill the results to a size and shape that can drive downstream analysis

Foundations for Analytics with Python From Non-Programmer to Hacker.pdf

# Create and run your own Python scripts by learning basic syntax
# Use Python’s csv module to read and parse CSV files
# Read multiple Excel worksheets and workbooks with the xlrd module
# Perform database operations in MySQL or with the mysqlclient module
# Create Python applications to find specific records, group data, and parse text files
# Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn
# Produce summary statistics, and estimate regression and classification models
# Schedule your scripts to run automatically in both Windows and Mac environments