Practical Python: Data Wrangling and Data Quality

1. Introduction to Data Wrangling and Data Quality
2. Introduction to Python
3. Understanding Data Quality
4. Working with File-Based and Feed-Based Data in Python
5. Accessing Web-Based Data
6. Assessing Data Quality
7. Cleaning, Transforming, and Augmenting Data
8. Structuring and Refactoring Your Code
9. Introduction to Data Analysis
10. Presenting Your Data
11. Beyond Python

https://github.com/PracticalPythonDataWranglingAndQuality/data_wrangling_exercises - This repo contains draft coding exercises for the early-release version of the book Practical Python: Data Wrangling and Data Quality to be published by O'Reilly Media in 2021.

В новом выпуске подкаста мы обсудили нового главу всея твиттера (а пропустить мы это не могли) и то, что теперь можно легально (правда, пока только в США) скрейпить данные и обучать свои нейроболталки (чтобы потом сделать стартап и войти в сотню самых интересных Big Data компаний).

Помимо дел бизнеса, обсудили современную хирургию, несовременный шутер и неожиданную объединяющую ведущих любовь к дирижаблям.

Все это в новом свежем новостном эпизоде подкаста Data Coffee🎙

#datacoffee #data #podcast #данные #подкаст

https://anchor.fm/data-coffee/episodes/46-S2E4----Mute--Twitter----etc-e1hrnkf

How to Add Value as a Data Analyst | by Cassie Kozyrkov | May, 2022 | Towards Data Science
https://towardsdatascience.com/how-to-add-value-as-a-data-analyst-8a6ae900b82a

Сегодня по расписанию будет - Python Environments and Best Practices
- Using the command line and command line applications
- How to set up projects using virtual environments
- Sharing code via git and GitHub
- Using IDE features for debugging, refactoring, and navigating Python code

В приложении презентация, ссылка на git, и reference document.

Data Quality Fundamentals

2022 O'Reilly Media, Inc.

- Build more trustworthy and reliable data pipelines
- Write scripts to make data checks and identify broken pipelines with data observability
- Program your own data quality monitors from scratch
- Develop and lead data quality initiatives at your company
- Generate a dashboard to highlight your company's key data assets
- Automate data lineage graphs across your data ecosystem
- Build anomaly detectors for your critical data assets

Dataviz-inspiration.com aims at being the biggest list of chart examples available on the web. It showcases 104 of the most beautiful and impactful dataviz projects I know. The collection is a good place to visit when you're designing a new graph, together with data-to-viz.com that shares dataviz best practices.
https://www.dataviz-inspiration.com/

Curated list of project-based tutorials
https://github.com/practical-tutorials/project-based-learning

Understanding Change Data Capture (CDC): Definition, Methods and Benefits | Airbyte
https://airbyte.com/blog/change-data-capture-definition-methods-and-benefits

The Data Engineering Cookbook
Mastering The Plumbing Of Data Science
September 12, 2021

https://github.com/andkret/Cookbook code examples

https://cookbook.learndataengineering.com/docs/01-Introduction - free online version

Introduction to Data Engineering (Daniel Beach).pdf

With the rise of Data Science and Machine Learning, Data Engineering is quickly becoming an in-demand skill. Data Engineering requires a unique skill set that is hard to learn without experience. I will teach you how to write scalable data pipelines and more!

Introduction
Chapter 1 - The Theory
Chapter 2 - Data Pipeline Basics
Chapter 3 - Pipeline Architecture
Chapter 4 - Storage
Chapter 5 - Compute and Resources
Chapter 6 - Mastering SQ
Chapter 7 - Data Warehousing / Data Lakes
Chapter 8 - Data Modeling
Chapter 9 - Data Quality
Chapter 10 - DevOps for Data Engineers

Урок из марафона с разбором Airbyte
https://www.youtube.com/watch?v=bzjXuilsUuU

Материалы марафона https://github.com/Arkronus/cooking-data

GitHub - ozlerhakan/datacamp: 🍧 DataCamp data-science and machine learning courses
https://github.com/ozlerhakan/datacamp