Flexible learning
Online postgraduate courses
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.
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- 20 Credits
- 200 hours of study
- 25 contact hours
- 175 hours for private study
Faculty
Structure
Structure
Foundations of Data Science 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.
Python
Module Programme
Introduction to Data Science
- What is data science and why is it important?
- The role of data in decision-making
- The principles of statistical thinking
Data types
- Importing and cleaning data using popular tools and techniques
- Basic data visualisation using graphs and plots
- Introduction to the Pandas library for data manipulation
Statistics and Machine Learning Basics
- Supervised vs. unsupervised learning
- Important data science methods for prediction and classification
- Basic machine learning algorithms and their applications
Model Evaluation and Selection
- Measuring the performance of machine learning models
- Choosing the appropriate model for a given problem
- Avoiding common pitfalls in model evaluation and selection
Prerequisites
English Language Requirements
Both Programmes are open to applicants anywhere in the world. We may ask applicants to provide a recognised English language qualification, dependent upon their nationality and where they have studied/worked previously.
The requirement is an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We will also consider other English language qualifications. If their score is below our requirements, they may be eligible for one of Lancaster University's pre-sessional English language programmes.
Academic Requirements
Applicants to the Postgraduate Certificate of Achievement, Postgraduate Certificate, Postgraduate Diploma or full MSc in either programme require either an upper second-class degree in economics, econometrics or related subjects.
Learning Outcomes
- 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 andclassifications
- 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.
- Demonstrate a sound knowledge of applied econometric principles and basic quantitativetechniques
- Demonstrate a sound knowledge of supervised and unsupervised learning techniques
- Present, interpret and analyse information in numerical form and use econometric and otherpackages effectively
- Understand the relevance of different econometric approaches to specific applications ineconomics
- Select relevant information from large amounts of data;
Frequently Asked Questions
All sessions are conducted live and online at a scheduled time, but are also recorded. Students may attend live and watch the recordings back to recap the material or watch the recordings only if unable to attend live. We always advise students to attend live where possible as this will allow them the best opportunity to engage with the content and ask the lecturer's questions.
All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.
No, all necessary software is provided to students.
All courses are recorded and available on the LUMS internet platform throughout the current academic year. They can therefore be viewed 24 hours a day.
Timberlake Consultants and Lancaster University Management School (LUMS) Economics department have a longstanding partnership; combining 40+ years of industry expertise with over 50 years of academic excellence. We are delighted to build on this with our micro-credential postgraduate courses.
Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education
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Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education