Notes

• ​If you can solve a problem with a simple heuristic, do that. Sometimes you don't need machine learning.​

• Data is not always useful and it doesn't matter how much of it you have.

  There’s no mathematical tool to tell you if your hypothesis is true; you can only see whether it is consistent with the data, and if the data is sparse or unclear, your conclusions are uncertain.

Links

• ​Some advice for young and aspiring Data Scientists​

• ​A Beginner’s Guide to Data Engineering — Part I​

• ​The Rise of the Data Engineer​

• ​Cookiecutter Data Science - Logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

• ​Best way to organize research code? (2018)​

• ​Data Science Cheat Sheet​

• ​Our world in data​

• ​Free data science books​

• ​Pachyderm - Reproducible Data Science at Scale.

• ​Data Science Cheat Sheets​

• ​Data Science in Visual Studio Code using Neuron, a new VS Code extension (2018)​

• ​Virgilio - Mentor for Data Science E-Learning.

• ​Awesome Data Science with Python - Curated list of Python resources for data science.

• ​Data Science (Cheat Sheets)​

• ​nteract - Interactive computing suite for you.

• ​120 Data Science Interview Questions​

• ​How To Become a Data Engineer​

• ​Ingestion Data Mapping Language​

• ​The reference implementation of IDML for the JVM​

• ​Pandas - Powerful Python data analysis toolkit.

• ​Programming Language Support for Data-intensive Applications meeting (2019)​

• ​Datasette - Tool for exploring and publishing data.

• ​Weld - High-performance runtime for data analytics applications.

• ​Vaex - Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second.

• ​PyParis 2018 - Vaex: Out of Core Dataframes for Python​

• ​Maarten Breddels & Jovan Veljanoski- A new approach to DataFrames and pipelines - PyData London 2019 (GitHub)

• ​The Data Engineering Cookbook​

• ​Data science without borders - Wes McKinney (2017)​

• ​Curated list of awesome ETL frameworks, libraries, and software​

• ​Ibis - Python data analysis framework for Hadoop and SQL engines.

• ​Kyso - Data analytics knowledge hub.

• ​Feather - Fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow.

• ​ROOT system - Provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data in a very efficient way.

• ​Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.

• ​Prefect - New workflow management system, designed for modern infrastructure and powered by the open-source Prefect Core workflow engine.

• ​Turn Python Scripts into Beautiful ML Tools (2019) (HN)

• ​Foundations of Data Science (2019) (HN)

• ​Gyana - Desktop app making data science approachable to all.

• ​Dask - Parallel computing with task scheduling. (Website)

• ​Numba - NumPy aware dynamic Python compiler using LLVM.

• ​What's your typical data pipeline in a small company? (2019)​

• ​dbt - Data build tool. Analytics engineering workflow.

• ​Apache Airflow - Platform to programmatically author, schedule, and monitor workflows.

• ​Overview of Popular Open Source Big Data Technologies (2018)​

• ​Introducing Apache Arrow Flight: A Framework for Fast Data Transport (2019)​

• ​Understanding Apache Arrow Flight (2019)​

• ​New Developments in the Open Source Ecosystem: Apache Spark 3 0, Delta Lake, and Koalas (2019)​

• ​Materials and IPython notebooks for Python for Data Analysis, 2nd Edition book​

• ​Technical Notes On Using Data Science & Artificial Intelligence​

• ​Amazon Data Science Interview (2018)​

• ​Data Analysis and Prediction Algorithms with R (2019)​

• ​Elegant SciPy book​

• ​Data science blogs​

• ​Awesome Data Science​

• ​Things About Real-World Data Science Not Discussed In MOOCs and Thought Pieces (2018)​

• ​Apache Zeppelin - Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.

• ​Apache Nifi - Easy to use, powerful, and reliable system to process and distribute data.

• ​Data Science: Principles and Practice Course materials (2018/19)​

• ​Modern Data Practice and the SQL Tradition (2019) (HN)

• ​Koalas - Pandas API on Apache Spark.

• ​Ask HN: What does your BI stack look like? (2019)​