ASSIGNMENT COVER SHEET CONTEMPORARY RESEARCH IN BUSINESS ANALYTICS Course COURSE/UNIT INFORMATION Doctorate in Management - UCAM Course Level Doctoral Module Name Faculty Contemporary Research in Business Analytics ASSIGNMENT INFORMATION Student Name Student ID Email ID Date Submitted TO BE FILLED BY THE STUDENT ASSSESSMENT FFEDBACK TO BE FILLED BY THE ASSESSOR NB: The Turnitin will be checked internally by assessors only. Assessment type Marks Marks Awarded Task 1: Descriptive Analytics 20 Task 2: Application of Regression and Classification 40 Technique Task 3: Data Mining Report 40 Overall Marks 100 Overall Grade achieved by the learner Summative Feedback by Assessor for further improvement 70% and above (Distinction) 60 to 69% (Merit) 50 to 59% (Pass) 40 to 49% (Fail/Redo) GRADE DESCRIPTORS The assignment evaluated is of a high to exemplary standard. The work addresses clearly and articulately the assignment requirements and thus meets and satisfies all the learning outcomes (either well or in an exemplary way). The work demonstrates: clear knowledge; references to appropriate academic literature; analysis; critical evaluation; and originality of argument. It is structured and presented to a high (or exemplary) standard. Referencing conventions are fully observed. The assignment evaluated is of a good to a high standard. Substantial knowledge, comprehension and analysis is evident throughout. Arguments presented are clear and focused with a logical structure in place. There is clear evidence of critical evaluation of a wide range of theories/perspectives from academic literature and some independent thought. The work is well-written and addresses well all of the learning outcomes. Referencing conventions are fully observed. The assignment evaluated is of a fair to good standard. Adequate knowledge, comprehension and analysis is evident throughout. The arguments presented I have a logical structure and show some critical evaluation in places, although there may be limited evidence of an independent perspective. There is evidence of some good engagement with some of the appropriate literature. Learning outcomes have been largely met and to an appropriate degree. Referencing conventions are observed. The assignment evaluated is of a basic standard. The arguments presented have some logical structure and are supported by academic literature in most cases. The academic literature used is outside of the suggestions made in the module guide but remains limited. Little critical evaluation is evident, and the work tends more widely towards a descriptive style. Learning outcomes have been addressed in a basic but satisfactory way. Referencing conventions are mostly observed. Fail Grades 30 to 39% (Module retake) 29% and Below (Module retake) The assignment evaluated is of a limited standard. Limited use of academic literature and as such knowledge and argument is very weak. A simple descriptive style with no evidence of critical evaluation throughout. Over- reliance on simplistic, limited sources. Referencing conventions may not be observed. Some learning outcomes met but in a weak and simplistic way. The work is needs to be developed in greater depth and detail to move to a passable standard at this level of study The assignment evaluated is of an unacceptable standard. There is little or no evidence of knowledge and understanding that is required at this level. Referencing is inadequate or non-existent. The learning outcomes have not been addressed fully and the work requires significant modification to bring it to a passable standard. Module Description This course will enable participants to develop data-driven business leadership skills required in a complex knowledge based global business environment. Participants will be introduced to the world of data analytics, with a focus on descriptive, diagnostic, predictive & prescriptive Analytics, Machine Learning & Optimization, Data Visualization, and decision making and leading with data. Participants will also be led to understand application of data analytics in the knowledge economy and disruptive technology as contemporary domains which influence business operations in the global stage. Learning outcomes LO1: To understand descriptive and inferential statistics and application of statistics in business research and decision making. LO2: To understand Machine Learning Algorithms and Optimization concepts including supervised and unsupervised machine learning concepts with regression and classification techniques. LO3: To explore the concept of data mining and challenges associated with application of big data analytics in business. LO4: Understand the application of data analytics in leading contemporary business domains including knowledge based economy and disruptive technology. Expectations 1. Materials Access All learning materials are provided in the form of a module kit and can be accessed from the Learning Management system (LMS) 2. Learning Hours Students need to be aware of their commitment requirements in regard to study time. In order to give you an indication of that, we have based the following information on the United Kingdom (UK) Higher Education Quality Assurance Agency guidelines. “The notional learning hours associated with qualifications, programmes and individual units of study are based on a broad agreement across institutions that students can expect to spend 10 hours learning on average in order to gain one academic credit unit” (QAA 2006). 3. Re-sit If you do not secure a pass, please read closely the feedback and speak with your Course leader(s) or faculty. After consulting the feedback, close attention is essential to rework on the areas of weakness, and then resubmit the work at the next opportunity. As per the QAA requirements, only one REDO is allowed where the marks will be capped at a Pass. 4. Plagiarism All forms of plagiarism are taken seriously, and any suspected cases will be investigated thoroughly. If a case is found proven, then the work will be graded as a fail and the case will be reviewed by the academic committee. The assessment team checks the Turnitin before the evaluation of the assignment is undertaken. 5. Student appeals There are no re-evaluations as the marks are graded and internally verified before release. However, as per our appeals policy, a student can make an appeal to the course leader which will be then reviewed by the academic committee (please check our academic policies and procedures manual for more information) 6. Assignment submission extensions Students can apply for extensions via the LMS based on extenuating circumstances (if any) with evidence (proof) as per our extensions policy. General Guidelines 1. Complete the ‘To be filled by the student section' in the cover page. 2. All assignments must be submitted as an electronic document in MS word via the LMS (Use 12 Times New Roman script with 1.5 spacing between lines) 3. The results are declared only if the student has met the mandatory attendance requirement of 75% and/or minimum 50 % under extenuating circumstances approved and ratified by the academic committee and the examination board. 4. The assignment should not contain any contents with references cited from websites such as ukessays.com, styudymode.com, slideshare.net, scribd.com, Wikipedia but should contain references/citations from credible academic journal and articles. 5. Submit the assignment in MS word document with the file name being: First Name Last Name Module Code Example: John Smith_GM701