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 ANALYSIS OF BIG DATA: PROGRAMMING, DATA MANAGEMENT & VISUALISATION
Code ECON215
Coordinator Dr GD Liu-Evans
Economics
Gareth.Liu-Evans@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 5 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

     10

     34
Timetable (if known)              
Private Study 116
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Assessment 2: Written Exam Assessment Type: Unseen Written Exam, Managed by SAS Duration/Size: 2 hours Weighting: 40% Reassessment Opportunity: Yes Penalty for Late Submission: Standard Uo  2    40       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Assessment 1: Individual Project Assessment Type: Coursework Duration/Size: Maximum 2 Weeks Weighting: 60% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Ap  0    60       

Aims

The module aims to fulfil the needs of two main groups of students:  
Group 1: Data-intensive careers within economics and related areas increasingly require programming skills. Python is growing in popularity within the economics profession, and is one of the most popular languages within data science. The module should be particularly useful for those students whose future careers focus on relatively sophisticated or large scale data analysis and management in business, finance or economics. An aim of the module is for it to serve as an early introduction to the vast capabilities of Python for data analysis, where students can begin to acquire key skills in the area along with valuable transferable skills in computing.

Group 2: Research careers in economics or econometrics very often require general programming skills, and, while geared towards data tasks, this module serves as a first introduction to programming.

Learning Outcomes

(LO1) Students will be able to recognise and relate Python as a tool for data analysis.

(LO2) Students will be able to construct data visualisations proficiently using Python.

(LO3) Students will be able to investigate and analyse data competently using Python libraries.

(LO4) Students will be able to use and explain general programming concepts.

(LO5) Students be able to demonstrate familiarity with SQL as a tool for data analysis. Apply, explain and examine SQL as a tool for data analysis.

(S1) IT skills

(S2) Numeracy

(S3) Lifelong learning skills

(S4) Problem solving skills

Teaching and Learning Strategies

Teaching Method - Lecture
Scheduled Directed Student Hours - 24
Attendance Recorded: No

Teaching Method - PC Lab
Scheduled Directed Student Hours: 10
Attendance recorded: Yes

Self-Directed Learning Hours: 116
Description: Participants should spend time developing their competency in Python particularly the libraries introduced in class for data analysis, data management, and visualisation. Besides reading the relevant sections of the key textbook, participants on the course should spend time modifying and running code from class, exploring the various options and methods related to the class examples, and gain a usable set of Python skills for data analysis.

Skills/Other Attributes Mapping

Skills / attributes: IT skills
How this is developed: During lectures, labs, and self directed study, through the use of Python.
Mode of assessment (if applicable): Individual Project

Skills / attributes: Numeracy
How thi s is developed: During lectures, labs, and self directed study
Mode of assessment (if applicable): Individual Project

Skills / attributes: Lifelong learning skills
How this is developed: By solving lab exercises and via self directed study
Mode of assessment (if applicable)

Skills / attributes: Problem solving skills
How this is developed: During lectures, labs, and self directed study. There are often many ways to solve a Python programming problem, especially one involving data and visualisation, and there will be opportunities to be creative in Lab sessions
Mode of assessment (if applicable)

Syllabus
 

Students will use textbook and web resources to learn how to use Python, in particular the very popular "pandas" library for data analysis and data management, along with "matplotlib" for data visualisation, and “numpy”. The following topics will be covered: 

1.     Introduction to the Python programming language
· a general introduction to programming and Python
· data types, variables, lists, dictionaries
· conditional statements and loops
·  functions
·  reading text and csv files
·  examples, e.g. simple regression using the “statsmodels” econometrics package
·  plotting

2.     . Important Python libraries for data analysis, e.g “Pandas”
· arrays and indexing
· statistical methods
· dataframes, summary statistics
· reading and writing data

3. Plotting and visualising data
· producing figures and subplots using the “matplotlib” graphics library
· scatter plots, function plots, horizontal and vertical bar charts, stacked bar plots, histograms and density plots, scatter plot matrices
· drawing annotated maps using “basemap”

4. Data wrangling
· combining and merging data sets
· grouping and aggregating data
· reshaping/pivoting
· replacing values

5. Database querying via SQL
· Querying single and multiple tables. Joins and important commands
· grouping and aggregating data
· Nested queries
· SQL queries via Python

6. Further programming concepts (time permittin g)
· classes and objects
· creating a simple regression class

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