ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Data Analytics
Code ACFI130
Coordinator Professor C Wese Simen
Finance and Accounting
C.Wese-Simen@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 4 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24

 12

         36
Timetable (if known)              
Private Study 114
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Assessment 1: Group Project Assessment Type: Coursework Weighting: 40 % Size: 1200 Words Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous Assessm  0    40       
Assessment 2: Individual Project Assessment Type: Coursework Weighting: 60 % Size: 1200 Words Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous As  0    60       

Aims

This module aims to develop students’ ability to analyse data. Upon successful completion of this module, the students will be able to read Python code and write codes to analyse datasets. They should also be able to review existing codes, identify problems, and propose potential improvements.

Learning Outcomes

(LO1) Students will be able to import and export data using the programming language.

(LO2) Students will be able to to write programs in order to solve a specified problem set.

(LO3) Students will be able to understand and explain the objective of a code.

(LO4) Students will be able to review and improve a code.

(S1) Resiliency and Adaptability

(S2) Communication

(S3) IT Literacy

(S4) Lifelong Learning

(S5) Numeracy

(S6) Problem Solving

(S7) Team-working

(S8) Organisation

(S9) Leadership

Teaching and Learning Strategies

Teaching Method - Lectures
Description: Lecture (12 Lectures of 2 hours each)
Scheduled Directed Student Hours: 24 hours
Attendance Recorded: Yes
Students will attend the weekly 2 hours lecture during which the key concepts will be introduced.

Teaching Method – Seminar (6 seminars of 2 hour each)
Description: Face to face sessions
Scheduled Directed Student Hours: 12 hours
Attendance Recorded: Yes
The seminar will take place over 6 weeks. During these sessions, students will get exposure to python.

Self-Directed Learning Hours: 114 hours
Description: These independent learning hours are aimed at supporting the directed student learning. The module leader will provide guidance in the form of suggested readings or topics to complete with the expectation that students are well prepared to contribute to the tutorial activities and to understand the content of lectures.

This module is a pre-requisite for the following modules:
ACFI132 Computational Methods
ACFI233 Econometrics for Finance II
ACFI231 Theory of Finance I
ACFI234 Theory of Finance II
ACFI235 Financial Data Visualisation
ACFI232 Database Management

Skills/Other Attributes Mapping

Skills / attributes: Resiliency and adaptability
How this is developed: By learning to write computer programs in Python that handle a range of scenarios during the lectures and seminars.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Communication
How this is developed: By contributing to in-class discussions (lectures and seminars), and by preparing the project reports.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: IT Literacy
How this is developed: By developing computer programming skills in lectures and seminars. The students will also develop their skills by using digital tools and specialist software to engage wi th the course material, to collaborate and communicate with others, e.g. Jupyter notebook.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Lifelong Learning and Research
How this is developed: In lectures and seminars by critically thinking about how to use computer codes to automate tasks.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Analytical
How this is developed: During the lectures & seminars, the students will be analysing new problems and writing computer codes to analyse datasets.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Problem solving
How this is developed: During the lectures & seminars, the students will gather and synthesise information, compare different approaches to solve problems, and use their knowledge of programming to arrive at a recommendation.
Mode of assessment (if applicable): Gro up and individual projects

Skills / attributes: Team-working
How this is developed: In lectures & seminars, the students will work in teams to complete the assigned tasks. They will also work together to complete the group project. In doing so, they will understand the importance of teamwork, manage the interaction and relationships with other group members, gain experience in negotiation, persuasion, influencing and managing conflict.
Mode of assessment (if applicable): Group project

Skills / attributes: Organization
How this is developed: In lectures & seminars, the students will learn to manage their time carefully by prioritising and completing tasks within specific deadlines. They will also develop their organization skills by working on group and individual projects.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Leadership
How this is developed: During the lectures & seminars, the studen ts will have the opportunity to lead team activities. For instance, they will have the opportunity to plan the tasks, identify the resources need to complete the task, monitor the progress of the group and review the plan if needed.
Mode of assessment (if applicable): Group project

Syllabus
 

This module will introduce programming using the Python language for data analytics. The module covers the topics below:

• Introduction to programming
• Overview of Data types/structures
• Reading and writing data
• Mathematical and logical operators
• Flow control, and exception handling
• Functions for analytics
• Data exploration for data analytics
• Data wrangling

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.