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Home Course Data Analysis and Visualisation Using Excel (DAVE)

Data Analysis and Visualisation Using Excel (DAVE)

About the Course

In the rapidly evolving landscape of business and research, the ability to analyse and visualise data is a critical skill for researchers and data analysts of all disciplines. Excel is the most widely used data tool in research and professional environments across Africa and beyond, yet most practitioners use only a fraction of its analytical capability. DAVE is designed for individuals who want to move past the basics and work with data more rigorously, efficiently, and insightfully.

This course is designed to enable participants to maximise their use of Excel and to provide a comprehensive understanding of data analysis techniques and the art of creating impactful visualisations. The course builds directly on foundational Excel knowledge — basic formulas, sorting, filtering, and simple charting — and introduces the techniques that researchers, business analysts and data professionals rely on for real-world analysis. Participants move from manual, error-prone data handling to structured, formula-driven workflows that save time and improve the reliability of their outputs.

Because most organisations already use Microsoft Excel as their primary data tool, this training enables participants to extract significantly more value from the software they and their institutions already have without requiring investment in new platforms or tools.

Course Objectives

The course equips participants with six core capabilities:

  • Data cleaning and preparation: Participants will identify and resolve common data quality errors using structured cleaning techniques and functions, and apply logical functions including IF, IFS, AND, and OR to categorise and flag data programmatically.
  • Lookup and reference functions: Participants will use VLOOKUP and XLOOKUP to enrich datasets by drawing information from multiple sources and understand when and how to apply each function appropriately.
  • Descriptive statistics: Participants will learn the appropriate statistics to use for their data depending on the measurement and distribution of data. They will calculate and interpret measures of central tendency, spread, etc., directly within Excel, and summarise data subsets using SUMIFS, COUNTIFS, and AVERAGEIFS.
  • PivotTables: Participants will build, customise, and interpret PivotTables to summarise and cross-tabulate large datasets and configure value display options to derive new metrics from aggregated data.
  • Conditional aggregation: Participants will learn different approaches for subgroup analysis, including filter, subgroup menu, crosstabulation and slicers.
  • Data visualisation: Participants will create publication-quality charts and apply data visualisation principles to communicate findings clearly and accurately. They will learn Excel updates for automating their charts when they receive new data. 

Course Outcomes

Upon completion of this training, participants will have significantly enhanced their data management and Excel capabilities. They will be equipped with the practical skills needed to manage, analyse and present research data in Excel at an intermediate to advanced level. This will enable them to work more independently and efficiently, generating accurate, professional-quality outputs that meet the standards expected in research, monitoring and evaluation, and academic environments.

Course Content

Day 1: Data Cleaning and Logical Functions
  • Introduction to data and data formats in Excel
  • Understanding functions and formulas
  • Data management and cleaning
  • Identify and resolve data quality issues using structured cleaning techniques and text functions
  • Apply logical functions to categorise, flag, and transform data programmatically
  • Enrich datasets using VLOOKUP and XLOOKUP to draw from multiple sources
  • Calculate descriptive statistics and summarise data subsets using menus and functions
  • Learn creative ways to summarise data efficiently 
  • Build and interpret PivotTables and add calculated fields to derive new metrics
  • Subgroup analysis (conditional aggregation)
  • Create publication-quality charts and implement data validation rules to protect data integrity
  • Create Pivot charts and update with new data
  • Create Excel dashboards
Week 1: Data Cleaning and Logical Functions
  • Introduction to data and data formats in Excel
  • Understanding functions and formulas
  • Data management and cleaning
  • Identify and resolve data quality issues using structured cleaning techniques and text functions
  • Apply logical functions to categorise, flag, and transform data programmatically
  • Enrich datasets using VLOOKUP and XLOOKUP to draw from multiple sources
  • Calculate descriptive statistics and summarise data subsets using menus and functions
  • Learn creative ways to summarise data efficiently 
  • Build and interpret PivotTables and add calculated fields to derive new metrics
  • Subgroup analysis (conditional aggregation)
  • Create publication-quality charts and implement data validation rules to protect data integrity
  • Create Pivot charts and update with new data
  • Create Excel dashboards

Who Should Attend

  • Researchers
  • Data analysts
  • Programme officers
  • Administrators
  • Programme managers
  • Postgraduate students
  • Market researchers
  • Government officials
  • Scientists

Price Includes

In-Person:

  • Course attendance
  • Full refreshments (lunch, welcome tea, two tea breaks)
  • Course lecture notes and training manual
  • Complimentary parking
  • Certificate of attendance

Virtual:

  • Access to interactive webinars

  • Videos for each interactive webinar session

  • All pre-reading materials

  • E-certificate of attendance.

Course Details

In-Person
  • 3 days
  • R8,500
Virtual
  • 3 weeks
  • R7,000
Enrol Now

For more details about our services contact:

Dimakatso Mofokeng
info@cesar-africa.com
+27 11 403 1411