Building the Analytics Artefact

In this lesson, you'll create your analytics artefact by planning its structure, integrating data and algorithms, developing visualisations, enhancing usability, testing functionality, and refining based on review. By the end, your artefact will clearly present your analysis and insights.

Learning Goals Learning Outcomes Teacher Notes

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1 Introduction
2 Artefact Format
3 Plan Artefact Structure
4 Integrate Data and Algorithms
5 Enhance Usability and Presentation
6 Test Functionality Internally
7 Refine Based on Self-Review

1 - Introduction

In this lesson, you will build your analytics artefact. This involves planning the structure, integrating data and algorithms, developing core features like visualisations, enhancing usability, testing internally, and refining based on review.

By the end, your artefact will effectively showcase your analysis and address your hypothesis.

This lesson is adaptable to tools like Python with libraries such as Matplotlib or Pandas. It should take about 240-320 minutes over a few sessions.

2 - Artefact Format

Consider the format in which you will present it. The artefact can take different forms depending on your project needs and skills. Here are some options:

A web page: Create an interactive website using HTML, CSS, and JavaScript to display your visualisations, data summaries, and insights. This is great if you want users to interact with the data online.
A document: Use a tool like Microsoft Word, Google Docs, or a PDF to compile your charts, explanations, and conclusions into a static report. This format is straightforward for sharing detailed written analysis.
An interactive program: Develop a standalone application, such as a Python script with a graphical interface, where users can input data or explore simulations. This suits projects needing dynamic interaction.

Tip: If using a web page, ensure it links back to any databases from previous tasks for a seamless experience.

3 - Plan Artefact Structure

Start by outlining the overall design of your analytics artefact. This includes deciding on components like visualisations, reports, or interfaces that will showcase your analysis and directly address your hypothesis from previous lessons.

Steps to Follow:

Review your hypothesis and processed data from earlier lessons (e.g., if your hypothesis is about trends in student performance, plan to include charts showing mean scores over time).
Sketch a high-level structure: List key sections such as an introduction to the hypothesis, data summary, visualisations (e.g., bar charts for frequency, line graphs for trends), and a conclusion with insights.
Choose the format for your artefact (e.g., a Python script generating plots, or a simple web page if integrating with prior web skills).

Tip: Draw a rough diagram of your artefact's layout and save it for documentation. This step supports algorithmic thinking by planning how data flows into visual outputs.

4 - Integrate Data and Algorithms

Incorporate your processed data and the algorithms you implemented (e.g., for frequency, mean, median, mode) into the artefact's framework. This ensures your artefact is built on a solid foundation of cleaned data and functional code.

Steps to Follow:

Load your processed data from the previous lesson (e.g., a CSV file containing your dataset).
Integrate the algorithms: Copy and adapt your code for calculations like mean or mode into the artefact script.
Structure the data flow: Ensure algorithms process the data correctly (e.g., sort data for median calculation).
Test integration briefly: Run a small section to confirm data loads and algorithms output expected results.

5 - Enhance Usability and Presentation

Add explanations, labels, and user-friendly features to make your artefact clear and engaging. This improves how effectively it communicates your insights.

Steps to Follow:

Label all elements: Add titles, axis labels, and legends to charts.
Include explanations: Write text sections describing what each visualisation shows and how it relates to the hypothesis.
Enhance usability: Use colours for clarity, add navigation if it's a multi-page artefact, or include tooltips.
Ensure accessibility: Choose high-contrast colours and simple language suitable for your audience.

Tip: Preview your artefact as if presenting it to someone unfamiliar with your project.

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Building the Analytics Artefact