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 2020-21 | 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 |
7 |
5 |
24 12 |
48 | |||
Timetable (if known) |
120 mins X 1 totaling 10
|
120 mins X 1 totaling 24
|
|||||
Private Study | 102 | ||||||
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 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 Delivery: Mixed, hybrid delivery, with social distancing on campus. Teaching Method 1 – Online Asynchronous Learning Materials Teaching Method: Group Study Self-Directed Learning Hours: 102 Costs Information: Skills/Other Attributes Mapping Skills / attributes: IT skills Skills / attributes: Numeracy Skills / attributes: Lifelong learning skills Skills / attributes: Problem solving skills |
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: 2. Two important libraries for data analysis: “Numpy” and “Pandas” 3. Plotting and visualising data 4. Data wrangling 5. Further programming concepts |
Recommended Texts |
|
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |