Teaching Responsibility
LJMU Schools involved in Delivery:
Computer Science and Mathematics
Learning Methods
Lecture
Practical
Module Offerings
5124COMP-SEP-CTY
Aims
To contextualize the place of data science and the data analysis process in the organization
To introduce the hierarchy and uses of different analytical approaches
Learning Outcomes
1.
Distinguish between the component parts of enterprise information management systems and the place and purpose of data analytics and data science within those systems.
2.
Identify and differentiate the component parts of the data analysis process.
3.
Differentiate between the types of analytic approaches available and what each can produce
Module Content
Outline Syllabus:Enterprise information management systems
Enterprise Information Management (EIM)
Enterprise Data Analytics (EDA)
Enterprise Data Science (EDS)
Enterprise Architecture (EA)
Enterprise solutions (ES)
Data Analysis Process
Data Analytics Types
Descriptive Analytics – summarize “historical” data
Decision Analytics – distil data into manageable sets to optimise decision-making
Predictive Analytics – forecast future outcomes
Prescriptive Analytics – identify possible future actions and their effects
Detailed examples of descriptive and decision analytics in practice
The data science process
Raw data collection
Data cleansing
Exploratory data analysis
Machine learning, algorithms, statistical models
Communicate, Visualization, Report findings
Build data product
Cases studies in data analytics/science
Module Overview:
To contextualize the place of data science and the data analysis process in the organization To introduce the hierarchy and uses of different analytical approaches This module contextualises the roles of data science and data analytics in organisations to demonstrate their differing contributions to those organisations. In doing so, a hierarchy differing type of analytics are introduced and differentiated from data science in terms of purpose. This module lays the groundwork for the development of these areas in future modules, although descriptive and decision analytics are covered in detail here.
To contextualize the place of data science and the data analysis process in the organization To introduce the hierarchy and uses of different analytical approaches This module contextualises the roles of data science and data analytics in organisations to demonstrate their differing contributions to those organisations. In doing so, a hierarchy differing type of analytics are introduced and differentiated from data science in terms of purpose. This module lays the groundwork for the development of these areas in future modules, although descriptive and decision analytics are covered in detail here.
Additional Information:This module contextualises the roles of data science and data analytics in organisations to demonstrate their differing contributions to those organisations. In doing so, a hierarchy differing type of analytics are introduced and differentiated from data science in terms of purpose. This module lays the groundwork for the development of these areas in future modules, although descriptive and decision analytics are covered in detail here.