module specification

This module develops students’ foundation of programming principles through the introduction of application programming for data analytics. The module covers common programming data structures, flow controls, data input and output, and error handling. In particular, the module places emphasis on data manipulation and presentation for data analysis.  A substantial practical element is integrated into the module to enable students to use a programming language (e.g. Python) to prepare data for analysis and develop data analytical applications.

The aims of this module are to:
• enable students to gain understanding of programming principles,
• develop students’ knowledge and skills in programming design and coding,
• develop students expertise in data manipulation and presentation for data analysis,
• develop students with practical skills in data analytical applications development, and
• enhance students skills for integrative reasoning, problem-solving and critical thinking.

1. 1. Understanding programming [LO1],[LO2],[LO5]
   • Classes, objects, sequences, decisions, loops
   • Collections and common data structures
   • File & database
   • Error handling, exceptions
   • Searching, sorting, recursion
   • Object oriented design patterns
   2.  Data programming [LO3],[LO4],[LO5]
   • Data loading & storage
   • Programmatic data transformations
   • Programmatic data visualization
   3. Programmatic data analysis [LO3],[LO4],[LO5]
   • Aggregation & grouping
   • Correlation
   • Linear regression
   • Matrix operations

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:

- Lecture (2 hour / week):
Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies

- Workshop (2 hour / week):
Practical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students' development of skills with powerful interactive data analysis package.
.
- Blended learning:
Using the University’s VLE and online tools to deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.

On successful completion of this module the student should be able to:
[LO1] Demonstrate a systematic understanding of programming language and specialised packages for data analysis
[LO2] Prove sufficient level of knowledge and proficiency in programming design and coding.
[LO3] Develop substantial programming practical skills to solve research or real world problems in data manipulation and presentation for data analysis independently.
[LO4] Comprehensive understanding of some current developments of programming tools in data analytics.
[LO5] Demonstrate higher order skills for integrative reasoning, problem-solving and critical thinking.

Reading list available at:http://https://rl.talis.com/3/londonmet/lists/D784D6C7-81BA-CF36-018D-E37DA96A2093.html?lang=en-US

1. Wes McKinney (2012) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly Media. [Core]
2. Cody Jackson, (2014) Learning to Program Using Python, 2nd Edtion, CreateSpace Independent Publishing Platform [core]
3. Fabio Nelli (2015) Python Data Analytics: Data Analysis and Science using PANDAs, matplotlib and the Python Programming Language, Apress
on-line resources 
     CoLab - Google Colaboratory allows you to write and execute Python in your browser
https://colab.research.google.com/notebooks/ [Last accessed: 15/01/2021]