GV918-7-AU-CO:
Data for Social Data Science

The details
2023/24
Government
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 05 October 2023
Friday 15 December 2023
30
09 February 2023

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

MSC L2I112 Social Data Science,
MSC L2P312 Politics, Communications and Data Analytics

Module description

This module introduces principles and applications of the electronic storage, structuring, manipulation, transformation, extraction, and dissemination of data. In the age of `Big Data`, the vast amount of data is generated in each day, and if equipped with a right set of skills, computational social scientists can obtain valuable insights only attainable through a data-driven approach. This module is aimed to provide an opportunity for learning such skills through programming in Python.

We focus on four key aspects of data management. The first is studying the various types of data, data shapes, and how to clean and transform them to fit for future data analysis. The next key component is the data acquisition. Most data nowadays are stored electronically on the Internet. We will learn what data are available online and how to obtain them through both scraping of websites and accessing APIs of online databases and social network services. The third key component of the module is to learn about the data storage solution, in particular about databases in both relational and non-relational forms. The module covers the fundamental concepts of database and how to create, populate, modify, and query relational databases. Lastly, this module uses a project-based learning approach, including group-based collaboration, essential ingredients of modern data science projects. We will learn various collaboration and management tools, such as the shared computational environment on the cloud and use of version control tools.

Module aims

This module aims to provide the following knowledge and comprehension on the basic of modern data science, through lectures and hands-on coding classes:

1. An overview of the lifecycle of the data in social data science, from data acquisition, pre-processing, storing to analysis
2. Knowledge of collaborative working space such as shared computing environments and version control systems
3. A general review of cloud computing
4. Basic principles of machine learning

Module learning outcomes

By the end of the module, students will be:

1. Able to work with data sets using Python programming language and to summarise and visualise the data
2. Able to work with colleagues securely and effectively using online collaborative working space
3. Familiar to how to set up the cloud computing environment and able to know when to go on the cloud.
4. Capable of implementing online data collection projects for their research and managing/handling large data sets
5. Equipped with the understanding the fundamentals of machine learning, essential to the next steps of their data science learning.

Module information

Advisory Note


In this course, many of the assignments will be done through Python programming, which is very difficult for beginners in programming to follow without some preparation before the start of the term. Students who do not have previous experience in statistical programming are strongly encouraged to consult with the module supervisor.


Students are expected to have a basic understanding of statistical analysis with a successful completion of a module in introductory statistics.

Learning and teaching methods

This module will be delivered over 4 hours per week and will feature a 2hr lecture per week and a 2hr class per week.

Bibliography

The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Assignment 1  02/11/2023  30% 
Coursework   Assignment 2  23/11/2023  35% 
Coursework   Assignment 3  18/01/2024  35% 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Akitaka Matsuo, email: a.matsuo@essex.ac.uk.
Akitaka Matsuo
Module Supervisor: Akitaka Matsuo, a.matsuo@essex.ac.uk

 

Availability
No
No
Yes

External examiner

Dr Damien Bol
King's College London
Senior Lecturer
Resources
Available via Moodle
Of 40 hours, 38 (95%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information
Government

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