Study information

Introduction to Data Science - entry

MODULE TITLEIntroduction to Data Science CREDIT VALUE15
MODULE CODECOM2015 MODULE CONVENERMr Paul Hewson (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated)
DESCRIPTION - summary of the module content

In this module, you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling and visualisation, machine learning, statistical modelling, social network analysis, text mining. You will also explore the ways in which data science is transforming business and society, and learn about ethical and governance aspects of data science. Practical exercises, individual study and group work will consolidate your learning and provide the foundations for later study.

 

AIMS - intentions of the module

This module will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will evolve to reflect current issues in data science, providing student with the tools to formulate data science problems and construct pipelines to begin to solve them technically.

 Lectures will be accompanied by data analysis exercises and seminar discussions. A series of guided practical exercises will develop skills in programming (in Python), data handling and visualisation.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Design a data science pipeline for a given problem in a chosen domain.
2 Investigate a dataset using mathematical and visualisation tools.

Discipline Specific Skills and Knowledge

3. Apply principles of statistical pattern recognition to a given problem.
4. Articulate a decision problem to be solved with data science affecting business or society.

Personal and Key Transferable / Employment Skills and Knowledge

5. Critically read and report on research papers.
6. Present the results of a piece of data science work in the form of a report.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

Example topics (with associated exercises and seminar discussions)

  • Data in society and business
  • Statistics
  • Linear algebra
  • Probability
  • Data visualisation
  • Clustering, classification and regression
  • Decision trees

 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 117 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 33 Lectures, Practicals, Seminars
Guided independent study 50 Assessment
Guided independent study 67 Self-study and background reading

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Workshop exercises 2 hours per week All Model answers and verbal feedback
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 20 Written Exams 80 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Exam 80 1 hours 1-4 Written
Continuous Assessment 20 49 hours All Written
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Exam/assessment Coursework All Completed over the summer with a deadline in August
       
       

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework in the failed or deferred element only. For referred candidates, the module mark will be capped at 40%. For deferred candidates, the module mark will be uncapped.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener
  • Downey, A.B., Think Stats, O'Reilly Media, 2014.
  • Mayer-Schonberger V. & Cukier K., Big data: a revolution that will transform how we live work and think, John Murray, 2013.
  • Marr, B., Big Data in Practice, Wiley, 2016.
  • Downey, A.B., Think Python, Green Tea Press/O’Reilly, 2015.
  • Schutt, R. and O’Neill, C., Doing Data Science: Straight Talk from the Frontline, O’Reilly, 2014.

 

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 5 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Wednesday 13th March 2024 LAST REVISION DATE Tuesday 10th September 2024
KEY WORDS SEARCH Database; design; modelling

Please note that all modules are subject to change, please get in touch if you have any questions about this module.