---
title: Dashboard Foundations
tags: DF
description: Exploring Dashboards as an End-User
---

# Dashboard Foundations
###### tags: `DF` `SLD`


> Version 6.0 - April 2022
> [name=Rafael J. BARROS]

[ToC]

## New to MS Teams?

Teams is great for coolaboration.
Check these [guidelines](https://support.microsoft.com/en-us/office/get-started-with-microsoft-teams-b98d533f-118e-4bae-bf44-3df2470c2b12) to learn more about this tool.

## The DATA Process

## Week 1: Dashboard Foundations


:::info
:mortar_board:
Dashboard introduction. Experience using dashboards as an end user.
:::

:::success
:notebook: Week 1: [Practicing Markings and Filters](https://bit.ly/DF6W01)

:snowflake: Icebreaker: Share your card at Microsoft Teams
:::

### Examples from Slides DF-02: Dashboard
* https://www.tibco.com/covid19
* https://bit.ly/DS-TSXI-Operational
* https://bit.ly/DS-TSXI-Social
* https://bit.ly/DS-Rubric

### Examples from Slides DF-03: Marking and Filter

* https://bit.ly/DS1-Markings
* https://community.tibco.com/wiki/spotfire-enablement-hub
* https://bit.ly/DS-Filters

## Explore dashboards on your own

TIBCO Spotfire Overview:
{%youtube VO9PlN3dE8Q %}

* https://bit.ly/DF6W01 [Up to 100 points]

## Ice-Break activity
Create your personal presentation card. The objective of this activity is to participate in the online ice-breaker activity and get familiar with the Microsoft Team Channels.

Your card must have the following data:
* Preferred Name
* Country of birth
* Country of residence
* Three sentences about you, one false and two truths.

## Week 2: Deduce & Acquire

### **Deduce** Analytical Questions

### **ABCDEF** Process: Deduce Template

### **Acquire** the best Data available

### Basic Visualizations

## Week 3: Tidy Data

Data cleaning is the process of wrangling and unifying messy and complex data sets for easy access and analysis.

### Data structure changes [Form]

About the transformation of shape of the underlying data table.

Usual examples is to add or remove columns (width); add or remove rows (Height)


### Data semantic changes [Meaning]

About changing the way we interpret the data. This is about the values in the cells.

### The rules

1. Each type of observational unit forms a table.
2. Each variable forms a column.
3. Each observation forms a row.


Codd, E.F. "Further Normalization of the Data Base Relational Model."(Presented at Courant Computer Science Symposia Series 6, "Data Base Systems," New York City, May 24th–25th, 1971.) IBM Research Report RJ909 (August 31st, 1971). Republished in Randall J. Rustin (ed.), Data Base Systems: Courant Computer Science Symposia Series 6. Prentice-Hall, 1972.

## Example 1: Structural changes

For this example we use the [RD-SLD-DF-OS.xlsx](https://docs.google.com/spreadsheets/d/1QE5K86but33RhtmUb_iZRYf3VPQScHoP/edit?usp=sharing&ouid=107734312994547870391&rtpof=true&sd=true) Raw Data file.

Also available [the DXP sample file exercise](https://drive.google.com/file/d/1aAEAf5X_HB-VocLrH2a_PrWnvIlY3CJ4/view?usp=sharing)


### Step 1: Split the column

At the Data in analysis flyover, select the column you want to split and press the Action: **Split column...**


Verify the recommended suggestion is what you want:


:::info
:bulb:
You may change any of the parameters, for example the separator.
:::

### Step 2: Create a new data table to enable unpivot

:::warning
:bulb:
We can't do unpivot on calculated columns like **Split([column],"|",1)**
:::