This module is designed to give you a beginner’s understanding of using Machine Learning for quantitative data analysis.
- Unlock and Embrace Your Data Potential with Machine Learning: Stay Ahead of the Curve
- Discover and Harness the Power of WebR and Data
- Acquire Analytical Skills and Navigate Expertly in a Sea of Data
This module is designed to give you a beginner’s understanding of using Machine Learning for quantitative data analysis. We’ll cover descriptive statistics including averages, interquartile range, standard deviation, and variance. Additionally, we’ll explore correlation analysis and our frst statistical test, the student t-test. You’ll also be introduced to basic linear regression and the normal linear model. Basic applications from the business world will accompany the theory behind these concepts.
At the module’s conclusion, you will be able to understand and collaborate effciently with economists and data analysts. You will have the foundational knowledge needed to embark on a quantitative data analytics journey.
We’ll utilise the R software environment for learning data analysis via the WebR REPL project. WebR, an open-source tool, boasts immense power and comprehensive functionality.
Participants will receive a Certifcate of Attendance.
Delivery
Delivery will comprise 3 hour-long online
sessions on Wednesday evenings
(5.30 – 8.30pm), for 3 weeks.
Dates and Times
• Wednesday 30th October 2024, 5.30pm –
8.30pm
• Wednesday 6th November 2024, 5.30pm –
8.30pm
• Wednesday 13th November 2024, 5.30pm –
8.30pm.
Target Audience
Executives, middle-level managers, think tank
researchers and career changers.
Entry/Technical Requirements
• GCSE score of at least a C/4 in Maths.
• We would also consider applicants with
some experience in handling numbers and
data.
• No specifc software required, just access
to internet and a web browser.
Cost
£360 per participant.
How to apply
To apply or for more information please
contact:
The PLD Team
Liverpool Hope University
Hope Park, Liverpool, L16 9JD.
Tel: 0151 291 3061/3834
Email: pld@hope.ac.uk
http://www.hope.ac.uk/pld