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COM6062 Data Science for Business - Leeds Trinity University

Learning Outcome 1: Collect meaningful customer data to inform business goals and strategies while understanding the legal and ethical requirements surrounding its storage, transfer, processing and analysis;
Learning Outcome 2: Prepare raw data for analysis, including the transformation, interrogation and cleansing of data from multiple sources;
Learning Outcome 3: Apply computational techniques and statistical methods to the analysis of business data;
Learning Outcome 4: Produce information-rich data visualisations and derive meaningful business insights;
Learning Outcome 5: Understand basic concepts underpinning distributed data processing and utilise a range of big data tools.

This module teaches students the fundamental principles of data science while equipping them with the analytical thinking needed to extract business value from data. The module aims to prepare students for roles which combine data analysis with data-driven decision making. It recognises the role of big data analytics in business by introducing students to the basics of big data processing. Knowledge from previous modules will inform students' approaches and strengthen their business insights.

Topics covered on the module include:

1. Collection, cleaning and storage of data, including the mining of relevant information from large and complex data sets. Student must choose the source of data set carefully. Moreover they can combine various related datasets to produce complex one.

2. Legal and ethical requirements concerning the storage and handling of customer data

2. Analysis of data using statistical toolkits. Learner are free to use toolkits. But it is strongly recommended that they will choose from toolkits like R, Python, SPSS, MATLAB and SAS

3. Creation of reports, dashboards and data visualisations to inform decision making.

Learning and Teaching Strategies

Through a mixture of tutorials and seminars students will develop an understanding of the role of data analytics in business and how it influences business decision making. Case studies are used as a vehicle for content delivery through which tools and techniques for working with data are introduced. Case studies will be taken from local and large-scale businesses with input from local industry. Examples of case studies to illustrate the required level and scope, include:

Assignment:

1. A project aim and objective will be setup that must have the business context
2. Select a dataset source and justify your selection
3. Introduce the Analytical Technique used in the report and justify
4. Use two statistical tools to retrieve data from your data set and compare them based on a framework (compare between Python and Orange or R) which one is better and why?
5. Conclusion and recommendation
6. Produce a presentation on your outcomes
7. Must use IEEE format

Attachment:- Data Science for Business.rar