DataDrooler Resources Handbook

A collection of Data Science/ Data Engineering / Data Analysis Resources DataDrooler Community is a place where Early-Career Data Professionals including Data Engineers, Data Scientists, and AI Engineers can discover meaningful projects and connect with brilliant collaborators to enhance data skills & make impact.

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Finding Relevant Data

Free Datasets

• Kaggle Datasets
• UCI Machine Learning Repository
• Google Dataset Search
• Data.gov
• World Bank Open Data

Free APIs

• Public APIs
• Awesome Public Datasets
• NASA API
• The Movie Database (TMDb) API

Webscraping Methods

• Beautiful Soup
• Scrapy
• Selenium
• Requests-HTML
• Puppeteer (for JavaScript)

Public Repositories

• GitHub Public Datasets
• AWS Public Datasets
• Google Cloud Public Datasets
• Microsoft Research Open Data

Tools

Data Science Tools

1. Python Python for Data Science Essential Training
2. R R Programming Tutorial
3. Jupyter Notebooks Jupyter Notebook Tutorial
4. Anaconda Getting Started with Anaconda
5. scikit-learn scikit-learn Tutorials
6. TensorFlow TensorFlow Tutorials
7. PyTorch PyTorch Tutorials
8. Keras Keras Documentation
9. Pandas Pandas User Guide
10. NumPy NumPy Quickstart Tutorial

Data Engineering Tools

1. Apache Spark Apache Spark Tutorial
2. Apache Airflow Airflow Tutorial
3. Apache Kafka Kafka Quickstart
4. Docker Docker Get Started Guide
5. Kubernetes Kubernetes Basics
6. Apache Hadoop Hadoop Tutorial
7. Apache Hive Hive Tutorials
8. Apache Flink Flink Tutorials
9. dbt (data build tool) dbt Documentation
10. Apache NiFi NiFi User Guide

Data Analyst Tools

1. Microsoft Excel Excel for Data Analysis
2. SQL SQL Tutorial
3. Tableau Tableau Tutorials
4. Power BI Power BI Guided Learning
5. Google Analytics Google Analytics Academy
6. Looker Looker Documentation
7. Google Data Studio Data Studio Tutorial
8. Qlik Sense Qlik Sense Tutorials

Best Practices

1. Coding Best Practices Guidelines
2. Create a Github Repository Github Repository
3. Git for version control Git

Books

Data Science

• "Python for Data Analysis" by Wes McKinney
• "An Introduction to Statistical Learning" by James, Witten, Hastie, Tibshirani
• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

Data Engineering

• "Designing Data-Intensive Applications" by Martin Kleppmann
• "The Data Warehouse Toolkit" by Ralph Kimball
• "Data Engineering with Python" by Paul Crickard

Data Analyst

• "Storytelling with Data" by Cole Nussbaumer Knaflic
• "Data Analysis with Python" by Wes McKinney
• "Practical Statistics for Data Scientists" by Peter Bruce & Andrew Bruce

Websites/Blogs

• Towards Data Science
• KDnuggets
• Analytics Vidhya
• Towards Data Engineering
• Data Engineering Weekly
• Seattle Data Guy