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Programming in Python (Level 8)

Duration i year
Stage Award
NFQ Level Level 8
ECTS Credit 10

This module aims to enable learners how to program a computer using a popular programming language, that is, Python. The learner will get exposure storing, manipulating, managing and processing data to solve problems using python as the programming language.

This module will familiarise the learners with the fundamental of python programming including but not limited to variables, loops, conditional statements so on and so forth along with familiarising the learners with a range of key topics in the emerging field of data analytics through the use of Python programming.

Furthermore, the module will also introduce statistical and numerical libraries that can be applied to analyse complex data sets. Additionally, data visualisation using python will be covered in this module.

This module has a strong practical programming focus and students will be expected to complete two individual coursework assignments, each involving implementing a Python solution to a data analytics task.

Tasks will include variables, strings, loops, terminal and file I/O, functions, etc.; confidently write computer programs in the language they have learned during the course; run programs to produce results.

The objectives of this module are to enable the learner to:

  1. Use personal reflection to heighten awareness of their knowledge and skills as it continues to develop, to gain a greater understanding of themselves, how they learn, and the role of self-direction in their learning and professional development.
  2. Source correct materials, information, and academic articles for formulating an academic argument – written and oral and Articulate a balanced argument in written and verbal format.
  3. Think critically and examine the nuance in arguments made by colleagues and academics and formulate an articulate, informed response to arguments presented.
  4. Correctly credit authors, researchers, and other various forms of media for ideas generated and discussed in the learner’s arguments/responses.
  5. Networking and personal branding, in the career development process.
  6. Commitment to toward Continuing Professional Development through enhancement of personal skills and proficiency throughout their careers.
  7. Use of planning, objective setting and action plans to achieve personal and professional development goals.
  8. Work life balance in coping with stress, setbacks, personal and professional pressure.
Programming in Python (Level 8)

Indicative Syllabus

Introduction to Programming Concepts

  • Overview of the terminology and applications of python programming in the area of Data analysis. Learners will be introduced to Importance of data analytics in industry.

Python Syntax

  • Introduction to python syntax: Creating your first programs and familiarize yourself with Python’s basic data types. Learn how to use python variables, comments, lists, dictionaries, sets, error checking and debugging.
  • Processing data structures: Conditionals and Loops. Efficient code structure: functions, modules, packages and files.

Advance Concepts

  • Object Oriented Programming (OOP) concepts, objects, classes. Introduction to version control, repositories, branching, commits and merging.

Data manipulation using Python

  • Introduction to popular python packages like Pandas, NumPy, SciPy, Matplotlib, Seaborn, that are widely used in data analytics tasks.
  • Importing data from various sources in different formats. Applying pivoting, cleaning, array-based indexing, aggregation, merging, transforming, reshaping, joining, filtering, and grouping.

Visualisation

  • Visualise and interpret the results of data analysis procedures with of a range of visualisation techniques such as heatmaps, histograms, scatter plots, boxplots, and using Matlplotlib and Seaborn libraries.

Module Details

  1. Demonstrate an understanding of the fundamental principles of computer programming using python.
  2. Write and debug python programs to store, manage and manipulate datasets.
  3. Develop python programs to solve data-driven problems by applying the learned concepts.
  4. Effectively utilise introduce python programming libraries to analyse datasets.
  5. Using the learning techniques to clean, transform data and visualise data.

This module is assessed via 100% continuous assessment. The continuous assignment involves weekly assignments, quizzes, and supervised project reports.

Sample assessments are included in Appendix with a draft Assessment Schedule.

Method of Assessment Percentage Weighting Learning outcome
Formative assessment

 

50%

 

1,2,4
Reflective Activities

Class activities including

quiz and discussion,

weekly assignments

50%

 

 

1,2,4
Summative assessment 50% 1,2,3,4,5
Project Report and code 50% 1,2,4,3,5

 

Marks distribution for Formative Assessment: Quiz 25% (Three quizzes : 5%, 10%, 10% ) + Assignments 25% (Weekly assignment 5% *5).

Where the marks of the assessment do not reach the pass mark the learner will be required to repeat the element of assessment that they failed. Reassessment materials will be published on Moodle after the Examination Board Meeting and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances. 

Programming in Python (Level 8)

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Programming in Python (Level 8)