Instructional Design Process Document

Project Overview

Course Title: Introduction to Data Analytics
Project Lead: Laena McCarthy
Date: February 15, 2020

Phase 1: Analysis

1.1 Needs Analysis

  • Objective: Identify the need for the course.

  • Stakeholders: IT team, HR department, potential learners.

  • Methods: Surveys, interviews, focus groups.

  • Findings:

    • High demand for data analytics skills.

    • Employees need foundational knowledge to perform data-driven decision-making.

    • Gap in current training offerings.

1.2 Learner Analysis

  • Target Audience:

    • New hires in the IT and business analytics departments.

    • Current employees seeking to upskill.

  • Demographics:

    • Age range: 25-45 years.

    • Educational background: Bachelor's degree in various fields.

    • Prior knowledge: Basic understanding of statistics and Excel.

  • Learning Preferences:

    • Prefer hands-on, practical exercises.

    • Flexible learning schedule.

1.3 Task Analysis

  • Tasks to be Learned:

    • Introduction to data analytics concepts.

    • Data collection and cleaning.

    • Exploratory data analysis.

    • Data visualization techniques.

    • Introduction to statistical analysis.

    • Basics of using data analytics tools (e.g., Excel, Python, R).

  • Skills and Knowledge Required:

    • Analytical thinking.

    • Attention to detail.

    • Technical proficiency with analytics tools.

Phase 2: Design

2.1 Learning Objectives

  • Overall Goal: Equip learners with fundamental skills and knowledge in data analytics.

  • Specific Objectives:

    1. Understand the core concepts of data analytics.

    2. Perform data collection and cleaning tasks.

    3. Conduct exploratory data analysis.

    4. Create effective data visualizations.

    5. Apply basic statistical analysis techniques.

    6. Use data analytics tools (Excel, Python, R) for analysis.

2.2 Instructional Strategies

  • Approach: Blended learning (online modules + live webinars).

  • Content Types: Videos, interactive quizzes, readings, case studies, hands-on projects.

  • Engagement Strategies:

    • Gamification elements (points, badges).

    • Discussion forums.

    • Peer reviews and group projects.

2.3 Course Structure

  • Module 1: Introduction to Data Analytics

    • Overview, key concepts, industry applications.

  • Module 2: Data Collection and Cleaning

    • Data sources, data quality, data cleaning techniques.

  • Module 3: Exploratory Data Analysis

    • Descriptive statistics, data exploration techniques.

  • Module 4: Data Visualization

    • Principles of data visualization, using tools like Excel and Tableau.

  • Module 5: Introduction to Statistical Analysis

    • Basic statistical concepts, hypothesis testing.

  • Module 6: Data Analytics Tools

    • Introduction to Excel, Python, and R for data analysis.

Phase 3: Development

3.1 Content Creation

  • Scripts and Storyboards: Detailed outlines for video content.

  • Multimedia Production: Video recording, editing, graphic design.

  • Interactive Elements: Development of quizzes, hands-on exercises, and projects.

3.2 Technology Integration

  • LMS Setup: Course upload to Learning Management System (e.g., Canvas, Moodle).

  • Technical Requirements: Ensure compatibility with various devices and browsers.

  • Accessibility: Ensure all content meets WCAG 2.2 AA standards.

3.3 Pilot Testing

  • Beta Group: Select a small group of learners to test the course.

  • Feedback Collection: Surveys, usability tests, focus group discussions.

  • Revisions: Implement changes based on feedback.

Phase 4: Implementation

4.1 Course Launch

  • Marketing: Internal communications, email announcements, flyers.

  • Enrollment: Open course registration.

  • Instructor Preparation: Training sessions for instructors on course delivery.

4.2 Learner Support

  • Help Desk: Technical support for learners.

  • Instructor Office Hours: Scheduled times for additional help and guidance.

  • Discussion Forums: Facilitate peer-to-peer interaction and support.

Phase 5: Evaluation

5.1 Formative Evaluation

  • Continuous Improvement: Ongoing feedback collection through surveys and quizzes.

  • Mid-Course Adjustments: Implement changes based on learner feedback.

5.2 Summative Evaluation

  • Course Completion Rates: Analyze data to determine success.

  • Learner Performance: Assessment scores, project outcomes.

  • Feedback Analysis: Post-course surveys, focus groups.

  • Impact Assessment: Measure how the course has improved learner skills and job performance.

5.3 Reporting

  • Summary Report: Compile findings from evaluations.

  • Recommendations: Provide suggestions for future improvements.

  • Presentation: Share results with stakeholders.

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[This sample is adapted with permission from a client project,]

This document outlines the comprehensive instructional design process for developing an "Introduction to Data Analytics" course. It ensures that all aspects of the course, from analysis to evaluation, are meticulously planned and executed to meet the learning needs of the target audience.