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BMG880 Data Analytics and Business Intelligence

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Task:

Aims

The aim of this module is to provide students with the knowledge and practical skills for applying business intelligence and data analytics principles to support management decision making in a business context. The module also equips students' with quantitative analysis and data visualisation skills to derive valuable insights from the data in a business context.

The module adopts 'learn by doing' approach to implement relevant features of a performance dashboard as part of the design and implementation of performance management system for a case organisation.

Learning Outcomes

Successful students will be able to:

1.Demonstrate in-depth knowledge and understanding of business intelligence and data analytics methods, tools and technologies within a business context

2.Analyse business data using appropriate statistical techniques

3. Show competence in the use of business intelligence and analytics software

4. Design, implement and evaluate an appropriate business analytics solution within the context of business performance management.

5. Interpret outputs from data analysis and communicate findings in a professional manner

BMG880 Data Analytics and Business Intelligence

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American Diabetes Association

Executive Summary In general, by the dataset that has been provided for use, the report will be largely based on matters diabetes and how itaffects the health of different individuals that turn out to be positive from itafter before and after tests and even over aperiod of time. This research stu ...

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Gestational Diabetes

Age Gender Pregnancies Glucose BloodPressur SkinThicknes Insulin BMI 40 Male 2 138 62 35 0 33.6 58 Male 0 84 82 31 125 38.2 41 Male 0 145 0 0 0 44.2 45 Male 0 135 68 42 250 42.3 60 Male 1 139 62 41 480 40.7 55 Male 0 173 78 32 265 46.5 57 Male 4 99 72 17 0 25.6 66 Male 8 194 80 0 0 26.1 67 ...

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Syringe and Needle

MIS 749 BUSINESS ANALYTICS WEEK 2 Let’s open up R Copyright © 2014 EMC Corporation. All Rights Reserved. Five Things to Remember About R 1. (Almost) everything is a object 2. (Almost ) everything is a vector  Example: x < - 3 -- x is a vector of length 1 v < - c(2,4,6,8,10) -- ...

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