<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[The Data School RSS Feed]]></title><description><![CDATA[The Data School RSS Feed]]></description><link>https://www.thedataschool.co.uk</link><generator>GatsbyJS</generator><lastBuildDate>Tue, 05 Aug 2025 13:52:30 GMT</lastBuildDate><item><title><![CDATA[Dashboard Week Day 5: SQL, Snowflake and LEGO]]></title><description><![CDATA[So here we are, the final day of training! To wrap up a great four months of training. For our last ever training project, this saw us download LEGO catalogue data from Rebrickable, containing LEGO CSV files (sets, parts, colours, inventories, themes, etc.) and visualise it, based on a summer theme!


The Challenge/Brief

Using a range of technologies and processes the challenge was to:

 * Download all the tables and upload them individually to your own schema on Snowflake
 * Clean, join and cr]]></description><link>https://www.thedataschool.co.uk/ted-evans/dashboard-week-day-5-43</link><guid isPermaLink="false">https://www.thedataschool.co.uk/ted-evans/dashboard-week-day-5-43</guid><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Ted Evans</dc:creator><image>https://www.thedataschool.co.uk/content/images/2025/08/lego-n-snow.jpg</image></item><item><title><![CDATA[Power BI: Hack to Bring Marks to Front]]></title><description><![CDATA[After working on my Power BI rebuild for Dashboard Week, I just couldn’t let go of getting those selected countries to move to the front. This is where I left the dashboard. 

I’d been working on it just before the end of day Dashboard Week presentation, so the version I actually presented was missing the non-selected dots in the top dot strip plot. 

This is a perfect time to remind any readers to save and save and save again and maybe (...definitely) if you’re going to continue exploring non-n]]></description><link>https://www.thedataschool.co.uk/eliza-hokanson/power-bi-hack-to-bring-marks-to-front</link><guid isPermaLink="false">https://www.thedataschool.co.uk/eliza-hokanson/power-bi-hack-to-bring-marks-to-front</guid><pubDate>Mon, 04 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Eliza Hokanson</dc:creator><image/></item><item><title><![CDATA[MOM in Power BI - London Underground Temperatures]]></title><description><![CDATA[Recently, I took part in a Makeover Monday challenge using Power BI to further develop my data visualisation and analytical skills.]]></description><link>https://www.thedataschool.co.uk/younes-ghouini/mom-in-power-bi-london-underground-temperatures</link><guid isPermaLink="false">https://www.thedataschool.co.uk/younes-ghouini/mom-in-power-bi-london-underground-temperatures</guid><pubDate>Sun, 03 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Younes Ghouini</dc:creator><image>https://images.unsplash.com/photo-1516919220054-3554951d310c?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDEwfHxsb25kb24lMjB1bmRlcmdyb3VuZHxlbnwwfHx8fDE3NTQyOTkwNTV8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week Day 3 - Back to  Basics!]]></title><description><![CDATA[It is now day 3 of Dashboard week, our final day. Today, we were tasked with web-scraping the Ben & Jerry's website using Alteryx and create a clean visualization with the data in Tableau. With today's task being back with the tools we have most of our experience with, I was so excited to get started!

Despite using tools that I have a lot of proficiency in, this task definitely challenged me a bunch and forced me to make tough decisions on what ideas could be done within the allotted timeframe ]]></description><link>https://www.thedataschool.co.uk/stosh-sawicz/dashboard-week-day-3-back-to-basics</link><guid isPermaLink="false">https://www.thedataschool.co.uk/stosh-sawicz/dashboard-week-day-3-back-to-basics</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Stosh Sawicz</dc:creator><image>https://www.thedataschool.co.uk/content/images/2025/08/Screenshot-2025-08-01-144243.png</image></item><item><title><![CDATA[Webscraping for visuals: Creating a dashboard that explores allergens in Ben & Jerry's ice cream flavors]]></title><description><![CDATA[We're in the middle of the summer, so let's build a dashboard that allows a user to decide on a Ben & Jerry's ice cream flavor that is safe to eat if they have allergies.

Ben & Jerry's website is in HTML, so we should be able to gather a lot of information by webscraping via Alteryx.

First, we'll find links to each flavor, and then find the relevant allergen and ingredient info. I thought the short and long descriptions of the flavors would be fun too!

That gives us a data set with a row for ]]></description><link>https://www.thedataschool.co.uk/amanda-rodriguez/webscraping-for-visuals-creating-a-dashboard-that-explores-allergens-in-ben-jerrys-ice-cream-flavors</link><guid isPermaLink="false">https://www.thedataschool.co.uk/amanda-rodriguez/webscraping-for-visuals-creating-a-dashboard-that-explores-allergens-in-ben-jerrys-ice-cream-flavors</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Amanda Rodriguez</dc:creator><image/></item><item><title><![CDATA[Dashboard Week 3: Tableau]]></title><description><![CDATA[It’s the last day of Dashboard Week! It’s been a pretty intense week, full of big projects and time crunches. I’ve had a lot of fun experimenting with different tools, but for the last day we took it back to Tableau. 

Our coach sent us to the Ben & Jerry’s website, and told us to use whatever tools we wanted to build a dashboard. Of course, this meant we would have to webscrape.

After looking around at the website, I found a page which contained their menu, along with descriptions of the ice c]]></description><link>https://www.thedataschool.co.uk/gabriel-bryan/dashboard-week-3-tableau</link><guid isPermaLink="false">https://www.thedataschool.co.uk/gabriel-bryan/dashboard-week-3-tableau</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Gabriel Bryan</dc:creator><image>https://www.thedataschool.co.uk/content/images/2025/08/dd.png</image></item><item><title><![CDATA[Dashboard of the Summer]]></title><description><![CDATA[DSNY9’s Dashboard Week was only three days, due to scheduling conflicts.

Day 1, we worked through rebuilding a Tableau dashboard in Tableau Next. 

Day 2, we rebuilt a Makeover Monday project of our choosing using Power BI. 

We had confirmation that our final day would be returning to Tableau, but we got started with a surprise- before we could build in Tableau, we had to webscrape Ben and Jerry’s website using Alteryx. 

This wasn’t quite what we expected, but of all the websites to scrape, I]]></description><link>https://www.thedataschool.co.uk/eliza-hokanson/dashboard-of-the-summer</link><guid isPermaLink="false">https://www.thedataschool.co.uk/eliza-hokanson/dashboard-of-the-summer</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Eliza Hokanson</dc:creator><image/></item><item><title><![CDATA[Dashboard Week: Day 3]]></title><description><![CDATA[Last Day!

It's crazy to be sitting here writing this blog on DSNY9's last day of training. I feel like the last 4 month were both so long yet went so fast. Last night I spent some time thinking about my application and interview process and the person I was then would be so proud to be where I am today. If you've read my application to The Data School blog you would know that I had my eye on this company forever and to be moving forward into client work

Today our task is to find data on the Be]]></description><link>https://www.thedataschool.co.uk/brandon-traditi/dashboard-week-day-3-73</link><guid isPermaLink="false">https://www.thedataschool.co.uk/brandon-traditi/dashboard-week-day-3-73</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Brandon Traditi</dc:creator><image/></item><item><title><![CDATA[Dashboard Week Day 5: SQL, SQL, SQL]]></title><description><![CDATA[The final day of dashboard week has arrived, but it has not been very friendly.

It does make sense that the last day would be the toughest, and today definitely came with some challenges.


The Brief



We got provided with 12 tables all to do with lego sets. Our goal was to take this data and transfer it into snowflake, making our own schemas and cleaning it with SQL.

The main aim was to delve into the data and choose a summer theme - e.g. beach lego sets, or holiday lego sets, and produce a ]]></description><link>https://www.thedataschool.co.uk/louis-phipps/dashboard-week-day-5-sql-sql-sql</link><guid isPermaLink="false">https://www.thedataschool.co.uk/louis-phipps/dashboard-week-day-5-sql-sql-sql</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Louis Phipps</dc:creator><image>https://images.unsplash.com/photo-1633412802994-5c058f151b66?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDF8fHNxbHxlbnwwfHx8fDE3NTQwNjY1NDl8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week Day 3 - Tableau]]></title><description><![CDATA[Day 3 of Dashboard Week was a really fun challenge.

We were given the Ben & Jerry's website and given free reign to make any form of dashboard we wanted! All we had to do was get the data ourselves.

Looking through the website, my first idea was to look at the flavors of the Flavor Graveyard and compare them to the 'Living' flavors to see if there were specific things that Ben & Jerry's kept trying or that were just unpopular...

Unfortunately I had a secondary challenge: I had 4 hours before ]]></description><link>https://www.thedataschool.co.uk/miles-cumiskey/dashboard-week-day-3-74</link><guid isPermaLink="false">https://www.thedataschool.co.uk/miles-cumiskey/dashboard-week-day-3-74</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Miles Cumiskey</dc:creator><image>https://www.thedataschool.co.uk/content/images/2025/08/Screenshot-2025-08-01-123610.png</image></item><item><title><![CDATA[Lego meets Snowflake]]></title><description><![CDATA[The task for the final day of Dashboard Week involved uploading Lego data around the theme of Summer into Snowflake, and then cleaning, joining and creating views all in Snowflake. Once the views were created, the task was to create a dashboard using either Tableau or PowerBI. I planned to create a PowerBI dashboard using data related to Spring, Summer, Autumn and Winter, to see how the trends varied against the different themes.

Once I had made a plan of what data I would bring into the dashbo]]></description><link>https://www.thedataschool.co.uk/agnes-amer/lego-meets-snowflake</link><guid isPermaLink="false">https://www.thedataschool.co.uk/agnes-amer/lego-meets-snowflake</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Agnes Amer</dc:creator><image>https://images.unsplash.com/photo-1644175897056-50f4d3a9a827?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDI2fHxsZWdvfGVufDB8fHx8MTc1NDA2NDUyNHww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Automating Store-Level Reporting with Batch Macros in Alteryx]]></title><description><![CDATA[A macro in Alteryx is essentially a reusable workflow. Instead of repeating the same logic multiple times, you wrap it into a macro and call it when needed — just like a custom tool. Macros can help you:

 * Simplify repetitive tasks
 * Organize complex logic
 * Improve performance and scalability

There are three main types of macros:

 * Standard Macros – Run once on the entire dataset
 * Batch Macros – Run once per group or value
 * Iterative Macros – Loop until a condition is met

In this po]]></description><link>https://www.thedataschool.co.uk/david-gandary/intro-to-macros-2</link><guid isPermaLink="false">https://www.thedataschool.co.uk/david-gandary/intro-to-macros-2</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>David Gandary</dc:creator><image>https://images.unsplash.com/photo-1590060846796-0418842f3908?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDF8fG1hY2Fyb25pfGVufDB8fHx8MTc1NDA2NTEzMHww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week: Day 5]]></title><description><![CDATA[Snowflake, SQL & LEGO Data

“And just like that, we reached the final day of training…”

Here we are: August 1st, 2025. The final day of training for my cohort.

What a ride it’s been - from early Alteryx puzzles to 10-minutes-before-presentation Tableau tweaks, we’ve covered a lot. But today wasn’t about nostalgia. Today was about showing what we’ve learned.

And I, naturally, chose mermaids.

This blog post isn’t a sentimental farewell (well, maybe just a little). It’s a look into my final Das]]></description><link>https://www.thedataschool.co.uk/kristupas-justas-jucaitis/dashboard-week-day-5-42</link><guid isPermaLink="false">https://www.thedataschool.co.uk/kristupas-justas-jucaitis/dashboard-week-day-5-42</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Kristupas Justas Jucaitis</dc:creator><image>https://www.thedataschool.co.uk/content/images/2025/08/ChatGPT-Image-Aug-1--2025--05_18_03-PM.png</image></item><item><title><![CDATA[Dashboard Week: Final Day]]></title><description><![CDATA[The Last Challenge

The final challenge of this tough week was to use SQL to clean and prep data ready for visualisation in PowerBI. Having not used SQL since the learning sessions it took some time to remember and figure out features, syntax and logic in order to achieve what I wanted.

To clean the data I first had to understand how each table within the schema needed to be related to one another and how the join statements needed to be written in order to extract all the data from each table.]]></description><link>https://www.thedataschool.co.uk/kieran-joymungul/dashboard-week-day-5-41</link><guid isPermaLink="false">https://www.thedataschool.co.uk/kieran-joymungul/dashboard-week-day-5-41</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Kieran Joymungul</dc:creator><image/></item><item><title><![CDATA[Dashboardweek - personal project]]></title><description><![CDATA[Over the last weeks we had timeslots here and there to work on a personal project.
We were free to chose what the topic was and which tools we use.

As I am an information hungry person, my topic of choice was something with Wikipedia.
The general idea was to see roughly to which topic an article is connected to while reading it.

sketch of my first dashboard idea.

Sketch of how the connected topics could be visualized.

To gather the neccessary data, I had to wrap my head around how to call th]]></description><link>https://www.thedataschool.co.uk/stefan-ladwig/dashboardweek-personal-project</link><guid isPermaLink="false">https://www.thedataschool.co.uk/stefan-ladwig/dashboardweek-personal-project</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Stefan Ladwig</dc:creator><image>https://images.unsplash.com/photo-1565462905102-140e712045aa?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDN8fHdpa2lwZWRpYXxlbnwwfHx8fDE3NTQwNjM4NjV8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week Day 5: SQL with Lego Data]]></title><description><![CDATA[For the final day of dashboard week, we were asked to perform our data preparation in SQL and take a look at LEGO data from Rebrickable to create a dashboard in a different software to what we used on Monday, which meant I would be building it in Tableau.


The Brief


The Process

I was a bit apprehensive about using SQL again after a while of not looking at it. With this, I didn't have a concrete plan in mind but took each step as I went. In hindsight, planning everything would have been best,]]></description><link>https://www.thedataschool.co.uk/hannah-norfolk/dashboard-week-day-5-sql-with-lego-data</link><guid isPermaLink="false">https://www.thedataschool.co.uk/hannah-norfolk/dashboard-week-day-5-sql-with-lego-data</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Hannah Norfolk</dc:creator><image>https://images.unsplash.com/photo-1709547228697-fa1f424a3f39?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDF8fGNhdCUyMGNvZGluZ3xlbnwwfHx8fDE3NTQwNjI5MTN8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Woche letzter Tag]]></title><description><![CDATA[Der letzte Tag der Dashboard Woche war für unser Personal Project reserviert. Ein Projekt, an dem wir in den letzten 4 Wochen regelmäßig arbeiten sollten.

Mein Projekt ist der Binge Calculator. Ein Tool für den leidenschaftlichen Seriengucker, der seine Zeit optimal planen möchte oder einfach um zu wissen, wie lange man für eine Serie braucht.Man soll einstellen können, wie viele Episoden man an einem Tag schauen möchte und dann berechnet der Binge Calculator, wie lange man anhand der Auswahl f]]></description><link>https://www.thedataschool.co.uk/birthe-claussen/dashboard-woche-letzter-tag</link><guid isPermaLink="false">https://www.thedataschool.co.uk/birthe-claussen/dashboard-woche-letzter-tag</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Birthe Claussen</dc:creator><image>https://images.unsplash.com/photo-1467991521834-fb8e202c7074?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDN8fHR2fGVufDB8fHx8MTc1NDA2MzUwM3ww&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item><item><title><![CDATA[Dashboard Week Day 5 – LEGO Summer Sets (Snowflake + Power BI)]]></title><description><![CDATA[For today’s challenge, we were asked to dig into LEGO data from Rebrickable with a summer twist. The task involved uploading datasets into Snowflake, building SQL views to clean and structure the data, and then connecting to that Snowflake data in the visualisation tool we haven't used this week — in my case, Power BI.


The Brief

Objectives:

 * Upload all provided tables to your own Snowflake schema
 * Clean, join and create structured SQL views
 * Identify summer-themed LEGO sets using keywo]]></description><link>https://www.thedataschool.co.uk/ollie-ross-russell/dashboard-week-day-4-lego-summer-sets-snowflake-power-bi</link><guid isPermaLink="false">https://www.thedataschool.co.uk/ollie-ross-russell/dashboard-week-day-4-lego-summer-sets-snowflake-power-bi</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Ollie Ross Russell</dc:creator><image>https://www.thedataschool.co.uk/content/images/2025/08/713Avenue_LegoMinifigures_Group01--1-.jpg</image></item><item><title><![CDATA[Dashboard Week Final Day: Utilising SQL Queries to Join Data tables and Creating PowerBI Dashboards]]></title><description><![CDATA[For my final project this week, I worked with LEGO-related datasets to practice data integration and visualization. The first step involved using SQL queries in Snowflake to explore and join multiple data tables, ensuring the data was clean, well-structured, and ready for analysis. Once the data model was complete, I imported it into Power BI, where I designed and built an interactive dashboard. This dashboard allows users to explore various aspects of the LEGO dataset through dynamic visuals an]]></description><link>https://www.thedataschool.co.uk/harry-caplin/dashboard-week-final-day-utilising-sql-queries-to-join-data-tables-and-creating-powerbi-dashboards</link><guid isPermaLink="false">https://www.thedataschool.co.uk/harry-caplin/dashboard-week-final-day-utilising-sql-queries-to-join-data-tables-and-creating-powerbi-dashboards</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Harry Caplin</dc:creator><image>https://www.thedataschool.co.uk/content/images/2025/08/SQL-and-powerbi.png</image></item><item><title><![CDATA[Spatial Analysis in Market Research Example]]></title><description><![CDATA[Spatial refers to information associated with specific locations on the earth’s surface. Analysing spatial data can help to gain insights, spot patterns and make informed decisions about geographic phenomena.

In this analysis, we’ll use spatial data to identify the three London boroughs with the fewest Sainsbury’s stores. This can provide valuable insight for location planning and resource allocation such as:

 * Store Location Planning: Identify optimal locations for new stores based on popula]]></description><link>https://www.thedataschool.co.uk/andrew-buchanan/spatial-analysis-in-market-research-example</link><guid isPermaLink="false">https://www.thedataschool.co.uk/andrew-buchanan/spatial-analysis-in-market-research-example</guid><pubDate>Fri, 01 Aug 2025 00:00:00 GMT</pubDate><dc:creator>Andrew Buchanan</dc:creator><image>https://images.unsplash.com/photo-1550989460-0adf9ea622e2?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wxMTc3M3wwfDF8c2VhcmNofDN8fG1hcmtldHxlbnwwfHx8fDE3NTQwNjI0MzJ8MA&amp;ixlib=rb-4.1.0&amp;q=80&amp;w=2000</image></item></channel></rss>