Microsoft Developer just posted a the full series of “Even More Python for Beginners – Data Tools” as one video.

Time Index:

  • 0:00 Series 2 Intro
  • 3:00 What Are Jupyter Notebooks
  • 15:54 Intro To Anaconda And Conda
  • 19:22 Intro To Anaconda And Conda Demo
  • 23:19 Getting Started With Pandas
  • 32:21 Getting Started With Pandas Demo
  • 39:09 Examining Pandas Data Frame Contents
  • 42:51 Examining Pandas Data Frame Contents demo
  • 45:41 Query A Data Frame
  • 54:23 Query A Data Frame Demo
  • 58:40 CSV File And Jupyter Notebooks
  • 1:00:15 CSV File And Jupyter Notebooks Demo
  • 1:01:20 Read And Write CSV Files From Pandas Data Frames
  • 1:05:56 Read And Write CSV Files From Pandas Data Frames Demo
  • 1:11:56 Removing And Splitting Data Frame Columns
  • 1:15:41 Removing And Splitting Data Frame Columns Demo
  • 1:19:30 Handling Duplicates And Rows With Missing Values
  • 1:24:33 Handling Duplicates And Rows With Missing Values Demo
  • 1:30:11 Splitting Test And Training Data With Scikit-learn
  • 1:41:30 Splitting Test And Training Data With Scikit-learn Demo
  • 1:47:08 Train A Linear Regression Model With Scikit-Learn
  • 1:50:55 Train A Linear Regression Model With Scikit-Learn Demo
  • 1:53:03 Testing A Model
  • 1:55:50 Testing A Model Demo
  • 1:59:34 Evaluating Accuracy Of A Model Using Calculations
  • 2:02:50 Evaluating Accuracy Of A Model Using Calculations Demo
  • 2:06:20 Numpy Vs Pandas
  • 2:11:37 Numpy Vs Pandas Demo
  • 2:16:36 Visualizing DataWith Matplotlib
  • 2:26:01 Visualizing DataWith Matplotlib Demo
  • 2:30:03 Thank You!

In this video, Anna Hoffman and Jeroen ter Heerdt discuss and show how to create auto-failover groups in Azure SQL using PowerShell notebooks and a Java application.

This video was based on a tutorial, which you can follow here: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-implement-geo-distributed-database?tabs=azure-powershell&WT.mc_id=dataexposed-c9-niner.

To compare geo-replication and auto-failover groups, refer to the table here: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-business-continuity#compare-geo-replication-with-failover-groups&WT.mc_id=dataexposed-c9-niner.

Time Index:

  • [00:30] Background
  • [01:15] Comparing geo-replication and auto-failover groups
  • [02:12] Tutorial for implementing a geo-distributed database
  • [02:45] Demo starts
  • [04:55] Failover initiated
  • [06:21] Fail back and summary

Lex Fridman interviews Harry Cliff in the latest episode of his podcast.

Harry Cliff is a particle physicist at the University of Cambridge working on the Large Hadron Collider beauty experiment that specializes in searching for hints of new particles and forces by studying a type of particle called the “beauty quark”, or “b quark”. In this way, he is part of the group of physicists who are searching answers to some of the biggest questions in modern physics. He is also an exceptional communicator of science with some of the clearest and most captivating explanations of basic concepts in particle physics I’ve ever heard. 

Time Index:

  • 0:00 – Introduction
  • 3:51 – LHC and particle physics
  • 13:55 – History of particle physics
  • 38:59 – Higgs particle
  • 57:55 – Unknowns yet to be discovered
  • 59:48 – Beauty quarks
  • 1:07:38 – Matter and antimatter
  • 1:10:22 – Human side of the Large Hadron Collider
  • 1:17:27 – Future of large particle colliders
  • 1:24:09 – Data science with particle physics
  • 1:27:17 – Science communication
  • 1:33:36 – Most beautiful idea in physics

Databricks hosted this webinar introducing Apache Spark, the platform that Databricks is based upon.

Abstract: scikit-learn is one of the most popular open-source machine learning libraries among data science practitioners.

This workshop will walk through what machine learning is, the different types of machine learning, and how to build a simple machine learning model. This workshop focuses on the techniques of applying and evaluating machine learning methods, rather than the statistical concepts behind them. We will be using data released by the New York Times (https://github.com/nytimes/covid-19-data).

Prior basic Python and pandas experience is required.

Previous webinars in the series:

  • Watch Part1, Intro to Python: https://youtu.be/HBVQAlv8MRQ ( to learn about python)
  • Watch Part 2, Data Analysis with pandas: https://youtu.be/riSgfbq3jpY
  • Watch Part 3, Machine Learning: https://youtu.be/g103iO-izoI