sme-dle-case-study-application

For this application stage, we will ask you to create a screencast, one corresponding explorable exercise for a specific portion of your case study outline, and a wireframe for your entire outline.

Getting Started

To begin the audition portion, fork this repo so that a copy of this repo lives on your GitHub account.

Once you have completed the following steps, notify Instructor Success with the link to your forked copy. Make sure that we can view the repo using appropriate access settings.

Step 1: Create an Explorable Exercise

An explorable exercise (EE) is a learning exercise, an advanced exercise type leveraged heavily within DLE case studies. The objective of an EE is to provide the learner with a more interactive experience and achieve a deeper understanding of conceptual topics without being required to do any coding or utilize a tool.

An explorable exercise consists of the following:

• The measurable learning objective that this exercise assesses

• A context that engages the learner and explains why they are doing this exercise.

• 3 - 6 bulleted instructions

• A question that assesses whether the learner followed the instructions correctly.

• An interactive visualization for the learner to utilize:

• You can generate these visualizations with multiple tools. The overall goal of the case study and question itself determines their complexity and scope.

• The visualization should require learners to complete 3-6 actions to uncover the answer

In our Understanding Data Visualization course, you will find many explorables that test various skills.

Here are a few examples:

• Adjusting bin width

• Ordering box plots

• Dual axes

ℹ️ Read this before getting started

• The goal of exercises in a case study is for learners to apply what they learned in previous courses to new problems or situations. Application is the best pedagogical practice for retaining and building skills. Since the courses covered are conceptual, students will rely on manipulating the interactive visualizations you create to practice their skills.

Supported visualization tools for explorable exercises

The DataCamp platform supports a variety of tools to engage learners. Utilizing multiple tools is greatly encouraged but optional for a successful case study. Each technology has a folder with more detailed instructions for submitting your sample exercise.

• R with R-Shiny: Complete the R-template in the Explorable/ folder.

• Python with Plotly: Complete the Plotly-template in the Explorable/ folder.

• Tableau Dashboard: Complete the Tableau-template in the Explorable/ folder.

Step 2: Explorable Screencast

Screencasts in Data Literacy and Essential (DLE) courses demonstrate to learners how to achieve tasks on the explorable exercise you create. They are, not used in case studies but will allow us to evaluate your exercise as you intended. You open your sample dataset, give some background on the problem you are trying to solve and walk us through the steps required to find the solution. You can look at this exercise for an example problem you can solve in your screencast.

Tips for recording the screencast: ensure the audio narration is clear and fast enough to follow. It’s often easier to first record the audio and then record your screen while listening to the audio.

• Upload your screencast to your platform of choice (e.g., Dropbox, Google Drive) and paste the link here:

Step 3: Create a wireframe for your outline

Now that you have created an explorable revisit your outline and develop it further into a wireframe. This wireframe aims to show how you can link the lessons and exercises together to build a comprehensive case study. The format of your wireframe is totally up to you.

• Upload your wireframe to your platform of choice (e.g., Dropbox, Google Drive) and paste the link here:

Guidelines and Requirements:

• Chapter 1

  • Consists of two lessons. Each lesson is comprised of one brief (3-4 minute) video exercise and at least three other exercise types. In addition to explorables, other exercise types include drag and drop (Classify or ordering) and multiple choice questions.
  • Each Lesson should have at least one explorable exercise.
  • Exercises should introduce the case study dataset and objective and refresh any required conceptual topics.

• Chapter 2

  • Consists of three lessons with the same exercise distribution as chapter 1. One video exercise followed by three other exercises.
  • Exercises should primarily focus on applying the conceptual tasks refreshed in chapter 1 to carry out the objective of the case study.

• At a high level, your wireframe should demonstrate how the students will utilize the knowledge gained from one exercise to the next exercise. To achieve this, you must summarize each exercise and the outcome the learner will need for success in the next exercise.

• You do not need to develop your exercises at this time fully. Rough outlines are sufficient to understand the flow of the course.

• Be creative with your wireframe. Below is a basic idea, but you are greatly encouraged to add pictures, notes, and whatever you feel necessary to show the vision for your case study.

Example Lesson for a case study on exploratory data analysis:

• Exercise 1 (Video): Exploring Retail Sales Opportunities

  • Objective: The learner will be introduced to a brick-and-mortar retailer looking to become more data-driven. They have large amounts of data but need help determining what is useful for their future growth.

  • Outcome: The learner will receive a summary of the measures of the dataset and the problem the retailer is looking to resolve. This should guide learners toward a set of variables that may be useful

• Exercise 2 (Explorable): Working with raw data

  • Objective: The explorable will allow learners to work with the retailer's raw data and sort/filter it. The previous exercise will guide them toward essential variables in the raw dataset and ask them to determine a KPI.
  • Outcome: Learners will be confronted with how challenging working with raw data is and a handful of variables of interest to consider for their next phase of analysis.

• Exercise 3 (Multiple Choice): Subject Matter Expertise

  • Objective: This question will introduce additional context to the case study and present background for the variables of interest from a Subject Matter Expert (SME) working within the organization. The Learner will need to select the most critical variable/measure/metric based on the feedback from the SME to continue the investigation.
  • Outcome: The feedback from the SME will give the learner a clearer vision of the path ahead and allow them to focus their exploration around the key variable/measure/metric.

• Exercise 4 (Explorable): A clear focus

  • Objective:: This explorable will present the same data as exercise 2 but allow the learner to visualize relationships between variables/measures/metrics instead of working directly with the raw data. The learner will be reminded of the outcome of the previous lesson and be able to understand the influence other variables/measures/metrics have.
  • Outcome: The learner should understand how much easier it is to explore data graphically than in its raw form and see the value that incorporating subject matter expertise has for conducting analysis. This exercise will prepare learners for more advanced exploratory data analysis in future lessons.