<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data-Science on Caktus Group</title><link>https://www.caktusgroup.com/tags/data-science/</link><description>Recent content in Data-Science on Caktus Group</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 21 Oct 2025 09:00:00 +0400</lastBuildDate><atom:link href="https://www.caktusgroup.com/tags/data-science/index.xml" rel="self" type="application/rss+xml"/><item><title>Accessible Traffic Stop Data with NC CopWatch</title><link>https://www.caktusgroup.com/case-study/accessible-nc-traffic-stops-data/</link><pubDate>Tue, 21 Oct 2025 09:00:00 +0400</pubDate><guid>https://www.caktusgroup.com/case-study/accessible-nc-traffic-stops-data/</guid><description>Forward Justice partnered with Caktus to transform North Carolina&amp;rsquo;s 32 million+ inaccessible traffic stop records into an open-source public resource. This tool empowers communities and advocates to analyze law enforcement data and drive data-driven policy reform.</description></item><item><title>Analyze data with SQL window functions</title><link>https://www.caktusgroup.com/blog/2023/04/05/analyze-data-sql-window-functions/</link><pubDate>Wed, 05 Apr 2023 14:00:00 +0000</pubDate><guid>https://www.caktusgroup.com/blog/2023/04/05/analyze-data-sql-window-functions/</guid><description>&lt;p>We regularly use tools like &lt;a href="https://www.postgresql.org/" target="_blank" rel="noopener noreferrer">PostgreSQL&lt;/a>,
&lt;a href="https://pandas.pydata.org/" target="_blank" rel="noopener noreferrer">Pandas&lt;/a>, and &lt;a href="https://jupyter.org/" target="_blank" rel="noopener noreferrer">Jupyter
Notebooks&lt;/a> to analyze data here at Caktus.
Recently, we were reviewing North Carolina traffic stop data for the &lt;a href="https://nccopwatch.org/" target="_blank" rel="noopener noreferrer">NC
CopWatch&lt;/a> project and had the opportunity to
use &lt;a href="https://www.postgresql.org/docs/current/tutorial-window.html" target="_blank" rel="noopener noreferrer">PostgreSQL's window
functions&lt;/a>,
which are helpful when aggregating data.&lt;/p></description></item><item><title>Begin your Data Analysis Journey with Pandas and Seaborn</title><link>https://www.caktusgroup.com/blog/2023/03/28/begin-your-data-analysis-journey-pandas-and-seaborn/</link><pubDate>Tue, 28 Mar 2023 14:00:00 +0000</pubDate><guid>https://www.caktusgroup.com/blog/2023/03/28/begin-your-data-analysis-journey-pandas-and-seaborn/</guid><description>&lt;p>Lately, there has been a lot of talk about scoring in the NBA because
LeBron James surpassed Kareem Abdul-Jabbar with 38,390 career points. I
have noticed that there is not much discussion about post-season
scoring, so I searched for this
&lt;a href="https://www.kaggle.com/datasets/isaienkov/nba-top-25-alltime-playoff-scorers" target="_blank" rel="noopener noreferrer">dataset&lt;/a>
on Kaggle (nba_playoffs.csv) which contains the top 25 all-time
post-season scoring leaders. Post-season scoring is its own beast. Since
teams face one opponent multiple times in a row, they can better
concentrate on the opposing team and its individual players,
particularly star players. This results in improved defenses across the
board. However, the post-season also means players improving their game.
What is the result of improved defenses and players alike? Only elite
players score consistently and thus, only the NBA's elite are on this
list. This post will first examine the dataset using &lt;strong>Pandas&lt;/strong> and then
use &lt;strong>Seaborn&lt;/strong> to graph such data.&lt;/p></description></item><item><title>Our Favorite PyCon 2019 Presentations</title><link>https://www.caktusgroup.com/blog/2019/06/11/favorite-pycon-2019-presentations/</link><pubDate>Tue, 11 Jun 2019 18:31:31 +0000</pubDate><guid>https://www.caktusgroup.com/blog/2019/06/11/favorite-pycon-2019-presentations/</guid><description>&lt;p>&lt;em>Above: A view of the busy exhibit hall. Photo copyright © 2019 by Sean Harrison. All rights reserved.&lt;/em>&lt;/p>
&lt;p>&lt;a href="https://us.pycon.org/2019/" target="_blank" rel="noopener noreferrer">PyCon 2019&lt;/a> attracted 3,393 attendees, including a group of six Cakti. When we weren’t networking with attendees at our booth, we attended some fascinating presentations. Below are some of our favorites. You can watch these talks and more on the &lt;a href="https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ/videos" target="_blank" rel="noopener noreferrer">PyCon 2019 YouTube channel&lt;/a>.&lt;/p></description></item></channel></rss>