Weeks 1 & 2
Weeks 1 & 2 [TL;DR]
The first week of The Data School started off with a bang—by way of ancient Greece (or Rome, depending on who you ask)—with a toga-themed graduation party for the previous cohort.
Although our official start date was the following Monday, it offered a small preview of what was to come.
During Week 1, we met much of the New York team and spent several days getting clear on:
- Professional expectations, team structure, and our company mission
- The tools we use, why, and how (Tableau, Alteryx, etc.)
- Foundational principles of modern data analytics
In Week 2, we dove into Tableau Desktop with a bottom-up introduction led by our valiant coach, Val (VALiant—get it?).
💫 I also had the chance to lead the application process segment of DSNY’s August Meet and Greet for prospective applicants. Since the experience was still fresh in my mind, I was excited to be a resource now on the other side (!!) of the process.
Week 1: Building Foundational Data Prep Skills in Tableau Prep
What Did You Learn?
In the first week, we explored Tableau Prep through group and individual challenges using Preppin’ Data. Tableau Prep is a data preparation tool that helps standardize and clean data, among other things. In simple terms: Tableau Prep is like a toy box that helps you clean up your messy LEGO blocks (data) so you can play with them better.
A key learning moment was discovering how to pivot data not only from columns to rows — which I had done before — but also from rows to columns. I hadn't realized how intuitive and efficient this process could be until using Tableau Prep.
Why Does It Matter?
Understanding both pivot directions unlocks a lot of flexibility when reshaping raw data into a usable format. In real-world datasets, the data's structure isn't always ideal for pulling out the information you need. Knowing how to make needed changes permits meaningful analysis and clear visualization.
Use Case
This can be useful when preparing monthly or regional data for reporting. For instance, turning individual transaction rows into summarized monthly columns can streamline trend analysis.
Week 2: Understanding Calculations, Aggregation in Tableau Desktop
What Did You Learn?
One of the many lessons I kept re-learning this week was the difference between row-level calculations and aggregations in Tableau Desktop. I was able to connect this to familiar SQL concepts — like CASE statements for row-level logic, and GROUP BY for aggregation. It clarified how Tableau handles calculations based on data granularity.
Why Does It Matter?
Choosing the wrong calculation type can significantly skew the insights presented in a dashboard. Understanding this difference helps avoid misleading conclusions and ensures that visualizations reflect the real story in the data.
Use Case
If you're building a dashboard to show average of sales by customer, you'll need to use aggregation correctly to avoid inflating the numbers. Similarly, row-level calculations can help break data into sections dynamically, such as identifying orders for each customer that exceed a certain value.