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UFCFVQ 15 M

UFCFVQ-15-M Programming for Data Science

Overview

This module introduces you to principles of programming with a range of applications to data science in appropriate domains.

This module is intended for students with little or no programming experience but also for those keen to improve their programming skills. It aims to provide students with an understanding of the role computation can play in solving problems and, regardless of their background, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals.

Objectives

  1. Explain, demonstrate and critically evaluate programming constructs and data structures.
  2. Develop, document and rigorously test functions for solving data science problems, demonstrating best practices for code maintainability.
  3. Select, integrate and compare software libraries for data manipulation, data analysis and visualisation.

Curriculum

The module will cover the following indicative topics:

Introduction:

  • Introduction to the field of data science;
  • Developing a working knowledge of common development tools used in the context of data science (such as Jupyter Notebooks, Visual Studio Code and Git).

Basic Programming and Algorithm Design:

  • Principles of programming through the Python programming language (such as variables, flow control and data structures);
  • Use of appropriate IDE or an approved virtual programming environment for the practical sessions;*
  • Procedural implementation of statistical algorithms.

Data analytics practices:

  • Introduction to popular data analysis and statistical learning tools and frameworks (such as Pandas, Numpy and Matplotlib);
  • Develop an understanding the concept of data structures (such as Lists, Dictionaries, Sets, Arrays and DataFrames);
  • Learn how to read in data into Pandas' Data Frames, how to query these structures, how to generate summary tables, data groupings, and how to clean and manipulate data;
  • Develop skills in Data Visualisation to support numerical data analysis.

Assessment

  1. Practical Skills Assessment (100%)