Teaching Responsibility
LJMU Schools involved in Delivery:
Computer Science and Mathematics
Learning Methods
Lecture
Practical
Module Offerings
6107STATS-SEP-CTY
Aims
The Introduction to Data Science module aims to introduce students to the Data Science field, providing them with a wide range of methods and state-of-the-art technologies that are on demand in the job market.
Learning Outcomes
1.
Conceptually understand the theoretical basis of key data science methods.
2.
Apply state-of-the-art data science technologies to develop rigorous and creative solutions.
3.
Critically evaluate the quality and suitability of the solution provided.
4.
Create effective data visualisations and data transformations.
5.
Demonstrate effective written and oral communication skills and an ability to confidently present data science solutions to a variety of audiences.
Module Content
Outline Syllabus:Introduction to the Data Science area and its context, challenges and the opportunities it brings.
Theoretical basis of key data science methods for exploratory and predictive analysis.
Application and evaluation of data science models.
State-of-the-art data science technologies.
Effective data visualisations and data transformations.
How to structure a Data Science project.
Additional Information:This module will start by introducing the theoretical basis of key data science methods using previously learnt programming languages and tools, while gradually moving on to introduce other state-of-the-art data science technologies. The delivery will be supported by a wide range of examples and practical activities.
The portfolio assessment is composed of two activities: 1) an in-class test (part A) and 2) a data science project (part B). Both activities will be individual and tutor assessed. Part A will assess the student comprehension of the module topics via solving quick practical data science problems; while part B will assess the student ability of producing an integrated solution to a closer-to real-world data science project.
Introduction to Data Science will provide an opportunity to learn the full cycle of a data science project solution.