Y/617/3035 Advanced Data Analytics, OTHM Level 6 Diploma in Information Technology

Aim: The aim of this unit is to provide learners with the knowledge and skills for advanced data analytics. This unit introduces learners to applied analytical models used in business to discover, interpret and communicate meaningful patterns of data held in silos or data warehouses, and to derive knowledge for competitive advantage. Learners are assumed to have some programming knowledge.

Learning Outcome 1: Understand the theoretical foundation of data analytics used in business decision- making.
Learning Outcome 2: Understand issues in preparing a large data set for use in an applied analytical model.
Learning Outcome 3: Be able to apply a range of descriptive analytic and/or statistical techniques to convert data into actionable insight.

Case Scenario

Oxbridge Analytica is a UK consulting firm which combines data mining, data brokerage, and data analysis with strategic communication. Oxbridge Analytica's unique fusion of market knowledge with optimal sourcing ability enables them to maintain the rigorous levels of accuracy and service quality expected by their long-standing list of clients. This is why many consider Oxbridge Analytica to be the leading supplier of data insight. Oxbridge Analytica also offers a variety of bespoke recruitment solutions for analytical professionals.

You have recently joined Oxbridge Analytica as a Deputy Director of Operations. Your COO, Mitchel Wright has assigned you a number of tasks to be completed and presented to a group of trainee system analysts.

Task 1 Prezi Presentation and speaker notes

Mitchel has asked you to create a Prezi presentation for the trainee analysts that will give them a thorough knowledge of data analytics. Whole presentation will be conducted in two sessions. You will provide speaker notes at the end of the session.

Instructions
a. In your Prezi presentation, you need to explain common terminologies used in 'data analytics' by FTSE100 companies.
b. Follow your presentation with a critical evaluation of the use of common data analytic methods.
c. In the final part of the presentation, summarise the importance of data analytics for businesses who deals with Oxbridge Analytica.

Task 2 Guidebook
During the presentation you felt that the trainees need to further understand issues in preparing a large data set for use in an applied analytical model. You decided to create a guidebook that will cover the most important topics related to analytical model.

Instructions
Your guidebook will have the following:

a. In the first part of the guide, evaluate analytical model data preparation processes.

b. In the second part of the guide, critically evaluate potential issues in the preparation of data for use in an applied analytical model.

c. In the final part of the guide, critically evaluate methods to visualise the output from an applied analytical model.

Task 3 - Lab demonstration
All the trainee analysts are ready for the next level of engagement and you are assigned to demonstrate use of various analytical techniques. Your lab instructor will take the role of a Client Relationship Manager and will assign you specific business related scenarios that needs solving using an appropriate analytical technique.

Instructions
a. Apply an appropriate programming language or tool to demonstrate how descriptive analytic techniques contribute to decision-making in Small and medium-sized enterprises.
b. Apply an appropriate programming language or tool to demonstrate how predictive analytic techniques are used in forecasting future events in Food and Beverages industry.
c. Apply an appropriate programming language or tool to demonstrate how prescriptive analytic techniques are used to find the best course of action in a situation such as optimising production.

Assessment Criteria
1.1 Explain common terminology in 'data analytics'.
1.2 Critically evaluate the use of data analytic methods.
1.3 Summarise the importance of data analytics for businesses.
2.1 Evaluate analytical model data preparation processes.
2.2 Critically evaluate potential issues in the preparation of data for use in an applied analytical model.
3.1 Critically evaluate methods to visualise the output from an applied analytical model.
3.2 Apply an appropriate programming language or tool to demonstrate how descriptive analytic techniques contribute to decision-making.
3.3 Apply an appropriate programming language or tool to demonstrate how predictive analytic techniques are used in forecasting future events.
3.4 Apply an appropriate programming language or tool to demonstrate how prescriptive analytic techniques are used to find the best course of action in a situation.