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

Data Analysis With Python A Modern ApproachSource Code by David Taieb.zip

Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis.

Key Features
+ Bridge your data analysis with the power of programming, complex algorithms, and AI
+ Use Python and its extensive libraries to power your way to new levels of data insight
+ Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series
+ Explore this modern approach across with key industry case studies and hands-on projects

Data Science with Python and Dask by Jesse C. Daniel.epub

About the book
Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you’ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you’ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.

What's inside
+ Working with large, structured and unstructured datasets
+ Visualization with Seaborn and Datashader
+ Implementing your own algorithms
+ Building distributed apps with Dask Distributed
+ Packaging and deploying Dask apps

Data Visualization with Python and JavaScript Scrape, Clean, Explore Transform Your Data.epub

+ Learn how to manipulate data with Python
+ Understand the commonalities between Python and JavaScript
+ Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy
+ Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries
+ Serve data and create RESTful web APIs with Python’s Flask framework
+ Create engaging, interactive web visualizations with JavaScript’s D3 library

Data Wrangling with Python Tips and Tools to Make Your Life Easier by Jacqueline Kazil, Katharine Jarmul.pdf

Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.
+ Quickly learn basic Python syntax, data types, and language concepts
+ Work with both machine-readable and human-consumable data
+ Scrape websites and APIs to find a bounty of useful information
+ Clean and format data to eliminate duplicates and errors in your datasets
+ Learn when to standardize data and when to test and script data cleanup
+ Explore and analyze your datasets with new Python libraries and techniques
+ Use Python solutions to automate your entire data-wrangling process

Learn Data Analysis with Python Lessons in Coding by A.J. Henley, Dave Wolf.pdf

Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.
If you aren’t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.

What You Will Learn
+ Get data into and out of Python code
+ Prepare the data and its format
+ Find the meaning of the data
+ Visualize the data using iPython

Beginning Data Science with Python and Jupyter by Alex Galea.epub

+ Identify potential areas of investigation and perform exploratory data analysis
+ Plan a machine learning classification strategy and train classification models
+ Use validation curves and dimensionality reduction to tune and enhance your models
+ Scrape tabular data from web pages and transform it into Pandas DataFrames
+ Create interactive, web-friendly visualizations to clearly communicate your findings

Data analysis with Python a modern approach by Taieb, David.epub

Key Features
+ Bridge your data analysis with the power of programming, complex algorithms, and AI
+ Use Python and its extensive libraries to power your way to new levels of data insight
+ Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series
+ Explore this modern approach across with key industry case studies and hands-on projects

What you will learn
+ A new toolset that has been carefully crafted to meet for your data analysis challenges
+ Full and detailed case studies of the toolset across several of today's key industry contexts
+ Become super productive with a new toolset across Python and Jupyter Notebook
+ Look into the future of data science and which directions to develop your skills next

Чеклист: на что смотреть, когда затягиваешь в проект новую библиотеку

Зависимости — кошмар любого большого проекта: они приводят к уязвимостям, конфликтуют друг с другом, протухают и блокируют обновление фреймворка. Так получается потому, что добавить в проект зависимость не стоит ничего, а вот поддерживать её (или просто выпилить) — огромный труд.

Кроме очевидного способа минимизировать проблемы от зависимостей (поменьше их притаскивать, кек), есть ещё простая гигиена, которая помогает упростить жизнь. Прежде чем набрать npm install или что там у вас, найдите репозиторий зависимости в Гитхабе и проверьте его:
— Не смотрите на количество лайков.
— Есть ли тесты? Понятно ли написаны?
— Посмотрите 5 минут на код. Удаётся ли понять, как он работает?
— Были ли значимые (не «version bump») коммиты в последние полгода?
— Не смотрите на количество лайков.
— Растёт или падает количество скачиваний (можно найти в npm/pypi).
— Сколько висит неотвеченных пулл-реквестов?
— Какие issues обсуждают?
— Понятно ли написано ридми, много ли документации?

Ну и конечно, не смотрите на количество лайков — люди ставят их за громкие названия и красивые ридми, а не за код, который решает проблемы без геморроя.

Есть что добавить в чек-лист? Напишите на fedor@borshev.com