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UNIT TITLE: Data Analytics CREDIT POINTS:15 FHEQ LEVEL: 7 UNIT DESIGNATION: Traditional UNIT CODE: COM719 ACADEMIC SCHOOL: Media Technology Delivering School: Media Technology Date validated: September 2016 Date last modified: N/A Unit delivery model: CD Max & Min Student No: N/A Arts and Arts and TOTAL STUDENT WORKLOAD Students are required to attend and participate in all the formal scheduled sessions for the unit. Students are also expected to manage their directed learning and independent study in support of the unit. PRE-REQUISITES AND CO-REQUISITES: None UNIT DESCRIPTION Effective and efficient data analysis techniques help business organisations understand and report on their past, measure their current market state and predict the future. In today business world, data analysis is increasingly becoming an indispensable tool for business corporates to understand their priorities. Students taking this unit will be provided with the theoretical and practical skills necessary to understand and implement the most current data analysis principles in the business context. Data analysis aims at defining business success measurements, introduce new improvements, identify risks related to stakeholders and technological assets such as online data, financial data, stock data and all other data driven business aspects. This unit introduces the key principles, theories and techniques that relate to Business and Marketing Data Analysis. This unit will be focused on the structured process of data collection, analysis and forecast business and marketing data in order to generate useful insights about both business and customer data. It will provide student with the knowledge needed to work with modern analytical tools currently used to analyse business data. The unit will also provide students with the ability to report on business data and present valuable information to stakeholders, it is aimed directly at web data analysis. This unit will draw upon some of the existing web data analysis platforms, including and not restricted to, Google Analytics, Clicky, Mint, Church Analytics, Open Web Analytics. LEARNING OUTCOMES On successful completion of the unit, students should be able to: Knowledge and Understanding K1 Understand the main statistical frameworks and core analysis techniques and their relation to business data analytics. K2 Synthesise theory from data mining and data warehousing, reporting and data visualisation in order to achieve a critical perspective on inform critical business and marketing decisions. Cognitive Skills C1 Independently analyse online business data statistics and report about online consumer behaviour and design customer acquisition journey, C2 Design the most effective digital marketing initiatives and evaluate business website traffic patterns and trends which give insights into what customer segments should be valuable. Practical and Professional Skills P1 Demonstrate a competency in using multiple web analytics platforms, practical methods to collect actionable business and marketing data. P2 Create different types of analytical reports, including but not restricted to, Audience Reports, Acquisition Reports, Behaviour Reports and Customer reports and Dashboards. Transferable and Key Skills T1 Apply business and marketing data analytics to improve business drivers and solve business practical problems T2 Work collaboratively with other to produce a presentation on a given research topic AREAS OF STUDY The unit will cover the business digital data analytics and the principles of web data mining and management. Web Analytics It is imperative for online business to assess and review web content issues they may have, web analytics if the examination of online website patterns and trends. This particular area will be looking at techniques that should be employed in order to collect, analyse, measure and report website data, this will be achieved by using tactical approach to data collection and report generations. Actionable Data Collection with multiple platforms and dashboards The main focus of this area will be placed on introducing practical data analytics frameworks, where students will familiarise themselves with different dashboards and learn how to collect, read and analyse data. This is considered the first step towards an intensive hand-on experience to google analytics. Student journey will go through creating accounts to review structures and setting up filters and goals. Web Data Analysis Techniques Exploring the web analytics process is vital for this unit, and therefore, the focus will be shifted to understand the common professional techniques required to process collected data to information, this should also focus on how to develop a KPI to infuse business strategies. Reporting This will help student to develop the necessary skills to be able to create different types of analytical reports, including but not restricted to, Audience Reports, Acquisition Reports, Behaviour Reports and Customer reports and Dashboards LEARNING AND TEACHING STRATEGY This unit will be delivered by a mixture of class-room based lecture and seminar sessions and more practical work, using the usability lab for data collection and analysis, where students will be provided with a hands-on experience of the standard and commercial tools for data collection and analysis. Formal tutor-led presentations will cover the essential topics and will provide an opportunity for students to encounter and explore a wide range of theoretical and practical content. They will then be expected to apply their learning for specific client and end user requirements, both independently and in seminars. Working in groups students will be assigned to a particular platform/context and will seek to apply the theories and practical skills that they have learned to a specific interface design problem. Regular student-led presentations will provide students with opportunities to see and review the work of other groups and will allow the tutor to monitor and feedback on student work. This approach is intended to promote positive outcomes in terms of employability and an inclusive approach, by encouraging professional approaches to team work and by ensuring regular, formative interaction between students and between students and their tutor. It is expected that students will actively seek to establish effective collaboration and to use diversity within the group as a stimulus for creativity and effective problem solving. ASSESSMENT STRATEGY Assessment for this unit consists of an exam and a presentation. The exam is designed to test the student knowledge of digital data analytics, through which students are expected to critically analyse and answer a combination of multiple choice and true/false questions. The exam will cover all areas of studies in this unit, it will designed to not only achieve the unit objectives but also to prepare students for the other accredited professional certificate such as Google Analytics IQ Certificate Exam. The presentation should capture the data patterns and trends applied to a certain case study selected by the student. As part of the assessment strategy of this unit, student should demonstrate a competency in applying data analytics techniques to certain business context, where an online business case study will be selected, an account need to be created and a successful implementation of what the process of web data analytics entail should be evidenced. This activity will be performed in group context, where student gets a real world hand on experience in working collaboratively with groups. Through engagement with the formative group-based development work, students should have contributed to and be able to access a group case study containing detailed research, evidence of planning, models and actionable data, testing data and a fully detailed report. They will also have had ample opportunity to have received feedback on this and will have seen the work of other groups. Formal assessment on this unit requires each group to perform a group presentation using their collected data, where they showcase their how the collected data will improve the business and potentially will contribute to create an online business strategy. This presentation should be delivered as if it were a pitch to a client. Each member of the team is expected to contribute to the presentation equally and will be assessed on their individual contribution. In addition to this each student should submit a copy of the presentation in which they provide a detailed assessment of their contribution. In doing so they should, justify key analytical decisions, with reference to theory and relevant secondary literature, provide an assessment of the strengths and weaknesses of the collected data and how data analysis will inform more effective business strategy. AE1 weighting: assessment type: length/duration: online submission: grade marking: anonymous marking: 40% Group Presentation Up to 10 mins per member including questions No Yes No AE2 weighting: assessment type: length/duration: online submission: grade marking: anonymous marking: 60% Time Constrained Assignment 2 Hours No Yes No Aggregation of marks The marks for each element of assessment will be aggregated to give an overall mark for the unit. Re-assessment Arrangements Students will undertake re-assessment in the University re-assessment period. Re-assessment in AE1 will require the student to present their element of the group presentation to meet the required standard. In the event of referral in AE2, students will be required to revise and resubmit their original submission in light of tutor feedback. Unit Author: Dr.Mohammed Al-Husban Date of version: September 2016