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 2021-22 Level 5 FHEQ First Semester 15

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

ECON113 MATHEMATICAL ECONOMICS; ECON112 STATISTICS FOR ECONOMICS AND BUSINESS; ECON115 SKILLS FOR PROFESSIONAL ECONOMISTS 

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 12

6

      12

6

36
Timetable (if known) 60 mins X 1 totaling 12
 
60 mins X 1 totaling 6
 
      120 mins X 1 totaling 24
60 mins X 1 totaling 6
 
 
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: Individual Project Assessment Type: Coursework Duration/Size: Maximum 2 Weeks Weighting: 100% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty A  Maximum 2 Weeks    100       

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 become familiar with Python as a tool for data analysis.

(LO2) Students will become proficient in producing data visualisations using Python.

(LO3) Students will develop competency with popular Python libraries for data analysis.

(LO4) Students will become familiar with general programming concepts.

(S1) IT skills

(S2) Numeracy

(S3) Lifelong learning skills

(S4) Problem solving skills


Teaching and Learning Strategies

Teaching Method – Online Asynchronous Learning Materials
Unscheduled Directed Student Hours: 12
Attendance Recorded: No

Teaching Method - Synchronous Lecture
Scheduled Directed Student Hours - 12
Attendance Recorded: No

Teaching Method - Seminar
Scheduled Directed Student Hours: 6
Attendance recorded: Yes

Teaching Method: Group Study
Description: Biweekly 1 hour session to foster student community and engagement by working with others on their ‘active learning’ activities
Scheduled Student Hours: 6
Attendance recorded: No

Self-Directed Learning Hours: 114
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.

Costs Information:
Participants on this module may find that a personal laptop/notebook is convenient, due to the large number of hours that will need to be spent at a computer. Software will be available on computers in the Management School, though, and possibly in other PC teaching centres. Additional costs may also include purchase of a recommended text book.

Skills/Other Attributes Mapping

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

Skills / attributes: Numeracy
How this 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
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.     Two important libraries for data analysis: “Numpy” and “Pandas”
·  arrays and indexing
· &# xA0;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
·  reshaping/pivoting
·  replacing values

5.     Further programming concepts
·  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.