module specification

The module is designed to introduce data programming through various programming concepts related to data. The module covers data structures, selection, iteration, data input and output with error handling. In particular the module focuses on creating data science solutions for business applications. Programming language Python is integrated into the module practical element to prepare, analyse, process and present data science solutions.

1. Students will gain knowledge and skills of the principles of data programming, design and coding.
2. Develop programming skills for data manipulation and presentation.
3. Develop skills to create data solutions for business applications using programming.
4. Enhance skills for logical reasoning, problem solving and evaluation.

CS4001 Fundamentals of Computing

1. Programming principles for data processing (e.g. Python: NumPy, Panda) LO1, LO4

• Sequence, selection, iteration, Data structures, files, databases, error and exception handling, search, sort, recursion

2. Programming for data preparation, manipulation presentation (e.g. Python: Plotly Matplotlib, geopandas for geo-spatial data)

• Data loading, storing, transformation, visualisation LO2, LO4 

3. Programming for Data analysis (e.g. Python: Scikit, Statsmodels): LO3, LO4

• Descriptive (e.g. summarisation, clustering, association) and predictive statistics (e.g. classification and regression).

Topics will be introduced through the medium of formal lectures, supported by tutorial
and workshop sessions, and blended learning as follows:
- Lecture (1 hour / week):
Introduction of the major topics identified in the syllabus, plus practical exercises,
directed reading and other further studies
- Workshop (2 hour / week):
Programming technical skills will be further developed through lab-based
workshops. Specific practical exercises are set to support students and development of
skills with relevant libraries.
.
- Blended learning:
Using the University’s VLE and online tools to provide deliver content, assessment
and feedback, to encourage active learning, and to enhance student engagement
and learning experience.
Students will be expected and encouraged to produce reflective commen

[LO1] Demonstrate a sound understanding of programming principles for data processing.
[LO2] Present sufficient level of proficiency in problem identification, data preparation, manipulation and presentation in the context of defined data science scenario.
[LO3] Create well-constructed programmes with data analysis for business cases showing the judgement in the selection of libraries and techniques.
[LO4] Evidence logical reasoning, problem solving, and evaluation skills needed for high quality solutions.

The assessments of the module consist of a coursework of (70%) and 30% of a 1.5-hour unseen exam.
The coursework will provide students an opportunity to research on current issues, practical techniques in the programming for data science and its application in the real world [LO1, LO2, LO3, LO4]. It will also improve the application of reasoning, problem solving skills and evaluation skills.

The unseen examination will provide an opportunity for students to demonstrate their understanding of programming principles for data processing of the materials from the module and ability to apply these techniques to a given problem [LO1, LO2].

The link to the following books can be found on https://rl.talis.com/3/londonmet/lists/A76C8BB4-A255-5409-939F-7CCCB6650DC8.html?lang=en-GB&login=1

• Wes McKinney (2017) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media. [CORE]
• Paul J. Deitel and Harvey Deitel (2019) Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud. Pearson. [CORE]
• Jackson, C. (2014) Learning to program using Python. 2nd ed. CreateSpace Independent Publishing Platform
• Fabio Nelli (2015) Python Data Analytics: Data Analysis and Science using PANDAs, matplotlib and the Python Programming Language, Apress