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Training

DMSA has developed a number of business-oriented courses for non-financial people who need to understand and interpret data and numerical information as well as surveys and the survey process.

Data Science in Excel


Data Science in Excel – 1 day

This course is presented in conjunction with Data Science Consortium, a company focused on providing executives with the skills they need to turn data from a by-product of business into an asset.

What you will learn

  • How Excel can be used as a powerful data science tool
  • Which data science algorithm to use to achieve your objective
  • How data science is applied to a vast range of data sets and problems
  • How to build proof of concept data science solutions in Excel
  • Why data science is about creating impact irrespective of what tool is used

Course overview

  • Introduction to Data Science
  • Cleaning and Structuring Data
  • Outlier Detection
  • Linear Regression and Logistic Regression
  • Cluster Analysis
  • Classification Modelling
  • What’s Next?

Who should attend?

  • Data analysts and anyone working with data
  • Junior data scientists and those moving into a data science role
  • Managers who manage data science teams
  • Managers who wish to understand the potential impact of data science
  • People working with data sets who want to create impact
  • Excel lovers looking for innovative ways in which Excel can be used

For more information and booking for the course, please see www.datasciencecourse.co.za

Frequently asked questions

Q: How long is the course?

A: It is a one day course.

Q: Are there any pre-requisites?

A: Participants should have a basic understanding of Microsoft Excel, datasets and maths.

Q: Do you run the training course for a single organisation?

A: If you would prefer for the courses to be facilitated it your own venue, on date that suit you, this can be arranged depending on the number of delegates.  Some organisations prefer this as it means that they can work with proprietary information without confidentiality concerns.