Introduction to Data Science

Do you ever find yourself asking any of the following questions when at work:
    •    Which of two versions of a web page will attract more readers?
    •    What's our likely web traffic next year?
    •    Where should we position our new warehouse?
    •    Which of our customers might sell more to?
    •    What can we do that will most improve customer satisfaction?

If you do, but you don't know the answers then you may need data science. This course teaches the key elements of data science, allowing business generalists to solve real business problems. It is an accelerated way for those interested in data science to improve their abilities.

The course teaches the analytical and statistical skills to allow students to turn data into actionable insights. It also covers how to use an analytical toolkit consisting of widely available or free software (principally Microsoft Excel and the R programming language), to allow statistical analysis and visualization.


Discover the skill set of a Data Scientist, a new role meeting the increased demands and opportunities of the web and modern technology:
    •    Use your analytical skills to manipulate data
    •    Develop business acumen, so  findings are applicable in the real world
    •    Master statistics, to separate vital signals from irrelevant noise


Course Content

Course Content

Course Content

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Section 1: Introduction


Unit 1.1 - Introduction    

An introduction to the course providing you with an understanding of what data science can do and the skills involved.

Section 2: Software Tools


Unit 2.1 - Software Tools Overview And Setup      

The rationale for using Excel and R and how to set up so you are ready to work with them.


Unit 2.2 - Basics of Excel    

Discover using Excel as a toolkit for the data scientist.


Unit 2.3 - Basics of R    

Discover using R as a toolkit for the data scientist.


Unit 2.4 - Section Summary

A section review on the key elements of Excel and R that will enable you to manipulate and analyse data to develop insight.

Section 3: Understanding Data


Unit 3.1 - Initial Appraisal of a Data Set    

How to get to grips with a new data set.


Unit 3.2 - Handling Big Data

Use R to examine a big data file in order to understand it, clean it and retrieve the information you are looking.


Unit 3.3 - Characterising a Data Set        

How to characterise / summarise a new data set.


Unit 3.4 - Probability    

The basics of probability, and how to calculate and combine probabilities.


Unit 3.5 - Section Summary

A section review on how we can understand, describe and interpret a data set.

Section 4: Inferences from Data


Unit 4.1 - Visualisation    

How to understand a whole population by looking at sample data from it.


Unit 4.2 - Making Predictions    

How to present data visually in order to allow for a greater understanding and insight.


Unit 4.3 - Decision Making    

How to use data to inform decision making.


Unit 4.4 - Section & Course Summary

A section and course review on how to draw robust business conclusions from data.


Pricing is for 12 months access.


  • No technical, software or analytical knowledge is assumed beyond a grounding in basic maths.

Completion Time: 3 hours 55 minutes (average)