代写ITAO2009 Data Analytics for Business Academic Year 2024-2025代写数据结构语言

ITAO2009

Data Analytics for Business

Academic Year 2024-2025

Module Description

Increasingly, organisations are relying on data analysis to interpret corporate information when making business decisions. Indeed, timely and appropriate use of data analytics is considered a crucial component among organisations that are committed to achieving business success. This module explores basic methods and concepts in data analytics for analysing and interpreting data. The module takes both a theoretical and practical approach to the use of data analytics in practice.

A highlight of the module is the use of KNIME software to analyse data for decision making and evaluative purposes. Students who successfully complete the module will be able to signal to potential employers that they have the theoretical, practical plus industry-standard software skills to compete.

Module Content

The module is taught in two types of lectures, class lectures and computer sessions.  The first type (i.e., class lectures) will take place in class where the theoretical background on data analytics will be covered. It takes a holistic approach to understanding data analytics - the maturity of data analytics in industry; uncover where, when, and how it is being used; and identify whether or not its use results in greater effectiveness, efficiency and performance returns.

Indicative class lectures contents include:

•    Small and Big Data - Case study (e.g., Netflix, Facebook etc.)

•    Descriptive and inferential analytics

•    Applications of data analytics in business

•    The concept of confidence intervals and hypothesis testing

•    Simple and multiple linear regression analysis

Computer sessions focus on data analysis, covering both descriptive and predictive analytics, emphasising on methods, such as correlation analysis and regression analysis.  Computer sessions will be taught through instructor led computer workshops using KNIME software.

Indicative computer sessions contents include:

•    Introduction to KNIME

•    Descriptive analytics and visualisation

•    Correlation analysis

•    Performance of linear regressions

Learning Outcomes

On successful completion of this module students will be able to:

Subject Specific

1. Demonstrate an understanding of the role and impact of data analytics in dealing with a variety of business problems.

2. Demonstrate an ability to summarise, analyse and present data effectively to others.

3. Employ statistical techniques to draw well founded inferences from quantitative data.

4. Demonstrate an ability to use appropriate software.

5. Demonstrate an ability to understand the scope and limitations of quantitative methods.

6. Identify sources of published analytics, understand their context and report on their wider relevance.

7. Interpret and disseminate research results and findings.

General

1. Apply critical analytical skills and problem-solving skills to a variety of different situations.

2. Synthesize, analyse, interpret and critically evaluate information from a variety of different sources.

3. Work effectively as an individual and as part of a team.

Course Schedule

This module is taught in class lectures and computer sessions, and it will include group work, lectures, and computer practical. Classes will be a combination of the traditional lecture, discussion, and interactive student-led sessions. It is imperative that students undertake preparatory work before coming to each class. The itinerary for each session is provided in Table 1 of this document. Computer sessions will focus on the practical implementation of marketing analytics using KNIME software.

TABLE 1: ITAO2009 DATA ANALYTICS FOR BUSINESS SCHEDULE 2024/25

Lecture 1

Topic / Activity •    Introductions •    Discussing the module’s Outline and Assessments •    Explain how the module’s assessments are meticulously aligned with the module’s learning outcomes •    A brief introduction to Data Analytics for Business Main Textbook •    Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making. Cengage Learning, Inc. (Chapter 1).

Lecture 2

Topic / Activity •    Introduction to Business Analytics Main Textbook •    Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making. Cengage Learning, Inc. (Chapter 1). •    Koole, G. (2019). An Introduction to Business Analytics. Lulu. com. (Chapter 1). Other Suggested Reading •    Chahal, H., Jyoti, J., & Wirtz, J. (2019). Business analytics: Concept and applications. Understanding the Role of Business Analytics: Some Applications, 1-8. •    Power, D. J., Heavin, C., McDermott, J., & Daly, M. (2018). Defining business analytics: an empirical approach. Journal of Business Analytics, 1(1), 40-53. •    Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2-12. •    Schläfke, M., Silvi, R., & Möller, K. (2012). A framework for business analytics in performance management. International Journal of Productivity and Performance Management, 62(1), 110-122. •    Yin, J., & Fernandez, V. (2020). A systematic review on business analytics. Journal of Industrial Engineering and Management (JIEM), 13(2), 283-295. •    Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673-686.

Computer Session 1

Topic / Activity •    Introduction to Knime Main Textbook •    Knime training manual and lecture slides issued by course instructors. Other Suggested Reading •    Acito, F. (2023). Introduction to KNIME. In Predictive Analytics with KNIME: Analytics for Citizen Data Scientists (pp. 21-52). Cham: Springer Nature Switzerland.

Canvas

Canvas will be used to post summary lecture notes. The module coordinator/ lecturer(s) will also use Canvas to communicate with the class, so it is important that students check Canvas and their University email account on a regular basis.

Assessment & submission deadlines

The assessment for the module consists of two assignments:

1.         Individual essay, due by 15 November 2024, worth 60% of the final grade.

2.         Individual analytics project in KNIME, due by 10 December 2024, worth 40% of the final grade.

Assignment 1: Individual Essay (60%):

Drawing on relevant academic and practitioner literature, critically evaluate the role of data analytics in the success of business activities in an industry or business context of your choice. (Hint! You may consider factors covered in the lectures, such as the data used and how it is used, benefits and limitations of big data (or any technique) application to business activities etc.)

The maximum word count for the individual assignment is 2,000 words (excluding tables, figures, references, and appendices). 2% of the maximum obtainable mark will be deducted for every 100 words over the word limit. The assignment must be submitted via university portal by 11.59pm, 15 November 2024. Students must ensure their name and student ID is included on the title page of their individual assignment.

The assessment sheet for this assignment will be provided in a supplementary file during the semester.

Please note that the School has a number of policies governing the submission of student  work. For all elements of assessment associated with this course you must be familiar with the School’s policies on:

•    ‘Participation, Preparation for Classes and Private Study’;

•    ‘Preparation and Submission of Assessed Work’; and

•    ‘Plagiarism, Collusion and Fabrication’ .

These policies are detailed in the Queen’s Business School Undergraduate Student Handbook.

Assignment 2 : Individual Analytics Project (40%):

Business Scenario

The estate agency Property Sales Ltd generates most of its profits from commission earned on residential property sales (i.e., the higher the sales price, the greater the commission earned from a sale). Working for the company, you have been tasked with producing a report on residential property sales handled by the company in the past 2 years.

Your analysis involves choosing variables to analyse which, based on evidence from a review of academic literature, have been shown to be linked to property prices.  The report aims include an analysis of sales data from the past 2 years, determining e.g., the distribution of sales in terms of your chosen variables.  The report will also aim to assist decision-making and planning for improved performance e.g., in directing what types of property the company might focus upon going forward.  Clear communication of the research findings is essential in reporting.

Data

Several datasets containing customer details will be provided to students.  All analyses must be performed using KNIME.

Analysis

You have been asked to provide a report for senior management with the following main sections:

1.  You must first assess the situation and consider all the various analytics approaches that could be useful for the business problem described above. Then, you need to proceed with the research question (s), variable selection, and hypothesis.

2.  Provide a data quality report based on descriptive statistics for each of the variables in the dataset  (use  both  statistical  and  graphical  output).  Comment  on  anything   unusual  or noteworthy that you see in the data.

3.  Use the dataset to proceed with the appropriate statistical analysis by selecting relevant variables (Hint! The analysis may include correlation analysis, and the final model should be a multiple linear regression model). Justify your choice of inputs & final solution.  Describe the final solution and provide the necessary intuition.

4.  Create a report for senior management outlining how your findings could be used to solve the business problem, by extensively discussing and interpreting your findings. The report must reference 1-3, above.

Suggested approaches, structure and marking criteria for the individual analytics assignment will be discussed during the workshop.

The maximum word count for the group assignment associated with the assignment is between 1,500 and 2,000 words (excluding tables, figures, references, and appendices). 2% of the maximum obtainable mark will be deducted for every 100 words over the word limit.

Students are required to submit the analytics assignment via university portal by 11.59pm, 10 December 2024.  Students must ensure their name and student ID is included on the title page of their individual assignment.

The assessment sheet for this assignment will be provided in a supplementary file during the semester.