Module Details

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 Introduction to Data Science B
Code COMM767
Coordinator Dr MA Pogson
Communication and Media
Mark.Pogson@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ First Semester 15

Aims

At the end of the module, students will have a good understanding of the theory and application of major techniques used in data science. They will develop core skills in computer coding which they will make further use of in the rest of their programme. The module will also prepare them for future positions in research and industry, where data science and coding skills are in growing demand. The module will be taught and assessed to reflect the emphasis on gaining practical coding skills, underpinned by theoretical understanding.


Learning Outcomes

(LO1) Demonstrate knowledge of the data lifecycle in the context of data science and communication.

(LO2) Use programming concepts such as variables, conditional statements, loops and functions.

(LO3) Know how to collect and process data from a range of sources.

(LO4) Select and apply suitable methods to analyse and visualise data to investigate and solve problems.

(S1) Numeracy/computational skills - Confidence/competence in measuring and using numbers

(S2) Skills in using technology - Information accessing

(S3) Problem solving – analysing facts and situations and applying creative thinking to develop appropriate solutions

(S4) Communication (oral, written and visual) - Presentation skills - written


Syllabus

 

The module will introduce concepts and techniques to students which will be built on in subsequent optional modules. The emphasis is on foundational skills, particularly coding, and the ability to access more advanced techniques through coding libraries. The module content will be accessible through Canvas, and links will also be provided to other online resources. Students will be expected to read and work through set materials outside of contact hours. They will also be expected to search for supplementary resources, particularly as part of their coursework.

Coding will use a modern scripting language, e.g. Python. The module may include the following indicative content:

BLOCK ONE – INTRODUCTION TO DATA AND CODING
- The data lifecycle and concepts in data, including attributes, distributions, correlations and sampling
- Computer coding environments and using variables, functions, libraries, conditional statements and loops

BLOCK TWO &# x2013; DATA COLLECTION AND VISUALISATION
- Data sources, formats and processing, scraping and APIs
- Fundamentals of data visualisation

BLOCK THREE – DATA ANALYSIS AND INTERPRETATION
- Concepts in data analysis, including explanatory and predictive approaches, interpolation and extrapolation
- Machine learning regression, classification and clustering


Teaching and Learning Strategies

Teaching method: Lecture/Workshop
This activity may be online or on campus and could be subject to changes.
Description: Teaching will be delivered through combined lectures and workshops, which will introduce students to key concepts and techniques, and give students chance to discuss and work on these in a computer lab.
Scheduled directed student hours: 33
Unscheduled directed student hours: 117
Attendance recorded: Yes

Description of how self-directed learning hours may be used:
Students should complete the assigned reading and exercises between taught sessions. They should start working on their assignments as soon as the assignments are introduced in class. It is also a good idea for students to book themselves onto any offline or online tutorials or book individual appointments with the aim of improving their academic writing and critical thinking skills.


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 33

          33
Timetable (if known)              
Private Study 117
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
Annotated computer code and results 800 words (excluding code)  600-1000 words    60       
In-class test  50 minutes    40       

Recommended Texts

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