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Analysing data

Foundations of Data
Science

Please note to take this course you must first have completed Foundations of Econometrics

This course offers a first overview of data science techniques at the graduate level. It takes participants through basic data types and properties, supervised learning techniques, and unsupervised learning methods. The course also addresses prediction and classification techniques using parametric, non-parametric and ensemble methods. Participants understand how to approach data analysis problems, which tools are available to them, and how to address common problems faced in data science applications.

This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program. 

Key Skills

By the end of this course, participants should have knowledge and understanding of: 

Data Fundamentals: Handling diverse data types and understanding their properties.

Machine Learning: Applying supervised and unsupervised learning algorithms.

Prediction and Classification: Using various methods for accurate predictions and classifications.

Analytical Approach: Systematic problem-solving for data analysis.

Tool Proficiency: Competence in common data science tools for analysis and visualisation.

Problem-solving: Addressing challenges in real-world data science applications.

Ensemble Methods: Application of ensemble methods for improved predictions.

Communication Skills: Effectively conveying insights from data analysis.

Desired Skills

By the end of this course, students should be able to: 

Demonstrate a sound knowledge of applied econometric principles and basic quantitative techniques.

Demonstrate a sound knowledge of supervised and unsupervised learning techniques.

Present, interpret and analyse information in numerical form and use econometric and other packages effectively.

Understand the relevance of different econometric approaches to specific applications in economics.

Structure

 

Foundations of Data Sceince is a core 20 credit course and therefore students are expected to input approximately 200 hours of study into the course.

 

The total number of contact hours is 25 hours. This leaves 175 hours for private study.

Lectures

 

This module consists of 4- hour lectures per day for 5 days, plus a 1 - hour tutorial per day. 

 There will be optional clinics on the last day of the course.   

The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact edu@timberlake.co.uk.

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