About LearnItPedia Technologies Limited

LearnItPedia Technologies Limited is an online computer training school focused on practical digital and technology education. Its training areas include data analysis, programming, web design, digital marketing, artificial intelligence and other digital skills.

The organisation aims to help learners across Africa develop practical skills that can support career development, business operations and independent projects. Data analysis training can help learners organise information, identify patterns, communicate findings and make better-informed decisions.

About the Role

The Data Analysis Course Creator will help plan and develop course materials that guide learners through the data analysis process: understanding a business question, collecting or importing data, cleaning and preparing it, analysing results, building visualisations and presenting findings.

Depending on the agreed scope, course content may include spreadsheet exercises, dashboard demonstrations, data-cleaning examples, written lessons, assignments, quizzes, case studies and portfolio projects.

Key Responsibilities

Proposed Data Analysis Course Curriculum

The following is a suggested curriculum framework. The final scope may focus on selected tools and modules depending on the course brief.

1. Introduction to Data Analysis

• What data analysis is and why organisations use it.
• Understanding structured data, records, fields and variables.
• Descriptive, diagnostic, predictive and prescriptive analysis concepts.
• Defining a business question before analysing data.
• Understanding data quality, privacy and responsible data use.

2. Microsoft Excel for Data Analysis

• Importing, organising and formatting datasets.
• Sorting, filtering, tables and data validation.
• Common formulas and functions for analysis.
• Conditional logic, lookup functions and text/date functions.
• PivotTables, PivotCharts and summary reports.
• Creating charts and presenting spreadsheet insights.

3. Data Cleaning and Preparation

• Identifying missing, duplicate and inconsistent records.
• Standardising dates, text, categories and numeric values.
• Detecting outliers and checking suspicious entries.
• Combining datasets and checking data types.
• Documenting assumptions and transformation steps.
• Validating the cleaned dataset before analysis.

4. Power BI and Business Intelligence

• Importing data from spreadsheets and other supported sources.
• Using Power Query for data transformation.
• Building relationships between tables and understanding data models.
• Creating measures and calculations with DAX where appropriate.
• Designing interactive reports, charts and dashboards.
• Using filters, slicers and drill-down features.
• Publishing and sharing reports with appropriate permissions.

5. Tableau and Data Visualisation

• Connecting to supported data sources.
• Understanding dimensions, measures and chart selection.
• Creating bar charts, line charts, tables and other visualisations.
• Combining visualisations into dashboards.
• Using filters and interactive dashboard elements.
• Choosing appropriate visualisations for the analytical question.
• Avoiding misleading scales, clutter and unsupported conclusions.

6. SQL Fundamentals for Data Analysis

• Understanding tables, rows, columns and relational data.
• Retrieving records using SELECT and WHERE.
• Sorting, grouping and aggregating data.
• Using common aggregate functions.
• Joining tables and understanding keys.
• Checking query results and interpreting findings.

7. Python for Data Analysis

• Introduction to Python and its role in data analysis.
• Working with notebooks and data files.
• Using pandas to inspect and manipulate tabular data.
• Cleaning data and handling missing values.
• Summarising data and calculating descriptive statistics.
• Creating charts with appropriate visualisation libraries.
• Exporting results and documenting the analysis process.

8. Statistics and Analytical Thinking

• Mean, median, mode, range and standard deviation.
• Understanding distributions and variation.
• Interpreting percentages, rates and trends.
• Correlation versus causation.
• Sampling, bias and limitations in datasets.
• Explaining uncertainty and avoiding unsupported claims.

9. Reporting and Communicating Insights

• Turning analysis into a clear business narrative.
• Identifying key performance indicators.
• Writing concise findings and recommendations.
• Designing readable reports and dashboards.
• Tailoring presentations to technical and non-technical audiences.
• Explaining limitations and documenting assumptions.

10. Practical Portfolio Project

• Choose a realistic business question and suitable dataset.
• Inspect and clean the data.
• Perform analysis using the selected tools.
• Build a report or dashboard that answers the question.
• Summarise findings and propose evidence-based recommendations.
• Document the workflow so another person can understand the results.

The topics above are proposed options, not a confirmation that every tool must be covered. The agreed course scope should specify which applications and skills are essential.

Who Can Apply?

This opportunity may suit data analysts, business intelligence professionals, reporting analysts, Excel trainers, Power BI developers, Tableau practitioners, SQL users and Python data professionals who can create structured learning materials.

• Practical experience analysing and interpreting datasets.
• Ability to explain data preparation, analysis and visualisation.
• Proficiency in at least one relevant analytical tool.
• Strong written communication and instructional skills.
• Attention to detail and a commitment to accurate calculations.
• Ability to create reproducible exercises and practical projects.
• A portfolio, sample dashboard, report or teaching example, where available.

Preferred Skills and Experience

• Microsoft Excel formulas, PivotTables and reporting.
• Power BI, Power Query and DAX.
• Tableau dashboards and data visualisation.
• SQL queries and relational data concepts.
• Python and pandas for data manipulation.
• Basic statistics and analytical reasoning.
• Data storytelling and stakeholder reporting.
• Screen recording, online teaching or instructional writing.

Applicants do not need to be experts in every listed technology. Relevant experience should match the course modules they propose to develop.

Expected Course Deliverables

The selected creator may be asked to provide deliverables confirmed in the project agreement, such as:

1. A course outline with modules, lesson objectives and prerequisites.
2. Lesson scripts, slides, notes or recorded demonstrations.
3. Practice datasets that are original, public or properly licensed.
4. Step-by-step exercises and quizzes.
5. Spreadsheet files, SQL examples, notebooks or dashboard files, as agreed.
6. A capstone project and assessment criteria.
7. References, assumptions and notes explaining analytical decisions.
8. Testing, review and agreed revisions before final submission.

How the Collaboration May Work

1. Application: Submit your details and relevant experience through the application page.
2. Review: LearnItPedia may review your portfolio or request a sample lesson or analysis.
3. Discussion: Confirm the intended audience, tools, curriculum, deliverables and commercial terms.
4. Planning: Agree on the course outline and production milestones.
5. Development: Prepare the agreed lessons, examples and exercises.
6. Review: Validate calculations, test project files and complete agreed revisions.

Applying does not guarantee selection or an engagement. Any collaboration depends on the review process and agreement on the final terms.

Compensation and Agreement

Compensation should be discussed and confirmed before course production begins. Depending on the project, the parties may agree on a fixed fee, revenue sharing or another mutually acceptable structure.

Any written agreement should explain payment calculations, deliverables, deadlines, payment timing, revision limits, content ownership or licensing, and each party’s responsibilities. This announcement does not guarantee a specific payment amount or percentage.

Why Consider This Opportunity?

• Share your expertise: Help learners understand practical data analysis workflows.
• Build your teaching portfolio: Demonstrate your ability to explain analytical methods and tools.
• Support applied learning: Develop projects that learners can use to practise and demonstrate their skills.
• Contribute to digital education: Help expand access to practical data skills for learners across Africa.

These are potential benefits, not promises of guaranteed income, reach or future work.

How to Apply for the Data Analysis Course Creator Job

If you have relevant data analysis experience and can develop accurate, practical training materials, visit the official application page. Explain which tools you can teach, the types of analysis you have performed and how you would help learners progress from basic concepts to real projects. Include a sample report, dashboard or portfolio if requested.

Application page: learnitpedia.com/data-analysis-course-creator

Frequently Asked Questions