An introduction to automating computer tasks using scripting languages and solving problems using programming languages, with a focus on data.

1. Automate computer tasks using a scripting language.
2. Write configuration files for a variety of software applications.
3. Setup and configure a software development environment and toolchain.
4. Develop an algorithm to solve a computational problem.
5. Write a computer program in a high-level programming language.
6. Construct a complex computer program from a series of simpler computer programs.

An introduction to the principles and fundamentals of data analytics, including the acquisition, cleaning and exploration of data sets.

1. Source and investigate sets of data.
2. Programmatically explore and visualise data.
3. Apply basic mathematical data analysis techniques to data sets.
4. Model real-world problems for analysis by computer.

5. Provide evidence in a decision-making process using a data set.

6. Appreciate the limitations of graphical representations in data intensive workflows.

A comprehensive primer on databases, with a focus on data analysis. Connections, creation, retrieval, update and deletion of both structured and unstructured data will be covered for modern database systems and architectures.

1. Create, retrieve, update and delete data in a variety of modern database management systems.
2. Determine the correct data to select from a database in order to perform a given data analysis.
3. Select an appropriate interface to access a database for a given application.
4. Determine the best balance between application and database logic for a given data analysis process.
5. Determine the correct connection details to access a database.

6. Reverse engineer a database structure.

An introduction to and overview of web applications and services – accessing and consuming them and their common architectures.

1. Describe common architectures of web services and web applications.

2. Create a simple web service.

3. Programmatically access a web service.

4. Construct a data set by querying a web service.

In this module, students develop their programming skills towards the effective use of data analytics libraries and software. Students learn how to select efficient data structures for numerical programming, and to use these data structures to transform data into useful and actionable information.

1. Programmatically create plots and other visual outputs from data.
2. Design computer algorithms to solve numerical problems.
3. Create software that incorporates and utilises standard numerical libraries.
4. Employ appropriate data structures when programming for data-intensive applications.

A practical module in computer infrastructure covering traditional computers, servers, and cloud infrastructure. Students will learn how to interact with a variety of computer infrastructure.

1. Use, configure, and script ina command line interface environment.

2. Manipulate and move data and code using the command line.

3. Compare commonly available software infrastructures and architectures.

4. Select appropriate infrastructure for a given computational task.

An introduction to machine learning and data modelling in the context of data analysis.

1. Source documentation to use machine learning libraries and packages in computer programs.

2. Pre-process a data set for use in a machine learning context.

3. Select an appropriate mathematical model of a real-world problem.
4. Select an appropriate cost function for a given machine learning task.
5. Apply an optimisation technique to the parameters of a model.
6. Use a trained model to make a prediction.

An introduction to statistics in the context of data analysis.

1. Describe the stochastic nature of real-world measurements.
2. Source documentation to programmatically perform a statistical test.

3. Select an appropriate statistical test to investigate a claim.

4. Perform a statistical test on a data set.

This module provides the student with the opportunity to demonstrate their ability to apply the theory and practice they have learned in a workplace or workplace like setting.

1. Identify real-world problems to which data analysis skills and knowledge can be applied.

2. Formulate real-world problems in terms amenable to analysis using a computer.

3. Solve real-world data-centric problems.

4. Collaborate and communicate in an effective and efficient manner.

5. Present ideas, concepts, and solutions to technically proficient colleagues.

6. Articulate a vision for future concepts, ideas, and directions they would like to work on.