LLMs for Data Science: What You Need to Know
ITDA - Western Technical College
Basic Computer Operations & Concepts Practice Test
This practice test assesses fundamental skills in Basic Computer Operations & Concepts, focusing on the core functions of computers including input, processing, output, and storage. It evaluates understanding of device types and data handling, essential for learners in computer science.
Free CompTIA IT+ Practice Test 2025
Our CompTIA ITF+ practice test will accompany you on the path to achieving certification. Don't wait any longer, check it out now!
Level Up Your MongoDB Skills
Learn in-demand database skills to build scalable applications and drive impact. Developers & architects will learn to model data and apply advanced schema design patterns.
A Quick Introduction to Market Basket Analysis | Towards Data Science
A Primer with a Real World Demonstration
The Effect (online text)
The Effect is a textbook that covers the basics and concepts of research design, especially as applied to causal inference from observational data.
Data Privacy Lab and Public Interest Tech Lab | The Institute for Quantitative Social Science
Power BI Desktop projects (PBIP) - Power BI
Learn how to save and edit a Power BI Desktop project.
Boost Collaboration in Power BI Desktop Using GitHub Integration
Boost Collaboration in Power BI Desktop Using GitHub Integration Source Control + Versioning + Team Collaboration = Better BI Development Introduction In the evolving world of data analytics, collaboration and version control have become essential. Power BI has long been a go-to tool for building ...
Modern Pandas (Part 1)
This is part 1 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
Effective Pandas
Introduction
This series is about how to make effective use of pandas, a data analysis library for the Python programming language.
It’s targeted at an intermediate level: people who have some experience with pandas, but are looking to improve.
Prior Art
There are many great resources for learning pandas; this is not one of them.
For beginners, I typically recommend Greg Reda’s 3-part introduction, especially if they’re familiar with SQL. Of course, there’s the pandas documentation itself. I gave a talk at PyData Seattle targeted as an introduction if you prefer video form. Wes McKinney’s Python for Data Analysis is still the goto book (and is also a really good introduction to NumPy as well). Jake VanderPlas’s Python Data Science Handbook, in early release, is great too.
Kevin Markham has a video series for beginners learning pandas.
Python Pandas DataFrame: load, edit, view data | Shane Lynn
It's difficult starting out with Pandas DataFrames. Learn how to load, preview, select, rename, edit, and plot data using Python Data Frames in this post.
YData Fabric Platform
Get easy access to data, improve data quality, and create synthetic data with the YData Fabric platform.
Dataiku Academic Program
Blog: History of Artificial Intelligence: 1960s & 1970s
Oct 10, 2021 | History of Artificial Intelligence: machine learning and expert systems
History of artificial intelligence | Dates, Advances, Alan Turing, ELIZA, & Facts | Britannica
This article covers the history of artificial intelligence from its beginnings with the work of Alan Turing to advancements at the turn of the 21st century.
AI in Power BI: Time to pay attention - SQLBI
This article is about the state of AI tools with Power BI, and how to use the model context protocol (or MCP) to interact with and control Power BI or Fabri
A few thoughts about newsletter #300 and AI - SQLBI
Newsletter #300 is a significant milestone for SQLBI: it marks nearly 12 years of sending our updates every two weeks. I thought this was a good time to cla
Data Warehousing Book
Emailed author on 7/16/25 for exam copy.
Data.gov - Data.gov Dataset
The Home of the U.S. Government's Open Data
Window Functions using DuckDB
Code for "Advanced data transformations in SQL" free live workshop - josephmachado/adv_data_transformation_in_sql
Using Joins and Group Bys the right way for data warehousing
Joins represent the denormalization of data to create dimensions and add dimension attributes to fact tables for reporting. Group bys enable the creation of metrics that determine how your data is used to make decisions.
If you have wondered
Why do your joins often end up in a dense block that's hard to pick apart.
Why are people running nearly identical queries with only different join combinations.
How to avoid losing data when performing joins
How group bys have been used to mask underlying data modeling issues
Why a group by all is considered a red flag
Then this post is for you. In this post, we will cover how to use joins and group bys, as well as what to look out for when using them.
By the end of this post, you will know what to watch out for when you perform joins or group bys.
Building Data Engineering Pipelines in Python - DataCamp Learn
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Streamlined Data Ingestion with pandas - DataCamp Learn
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ETL and ELT in Python - DataCamp Learn
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Data Warehousing Concepts - DataCamp Learn
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Data Literacy Courses - The Data Literacy Project
Think Stats, 3rd edition — Think Stats, 3rd edition
Performing Statistical Estimation
An attempt to explain the estimation process in statistics in the simplest form.
Electric Vehicle Population Data - Comma Separated Values File - Catalog
This dataset shows the Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) that are currently registered through Washington State Department of Licensing (DOL).
All the secrets of SUMMARIZE - SQLBI
SUMMARIZE is a very powerful and very complex function to use. This article describes its internal behavior, and provides guidance on how to use it. If you