Weeks 1 & 2
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.