ANALYTICS TOOLS FOR BUSINESS - 2021/2
Module code: MAN2189
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice during the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
The business world is inundated with data with the difference between a successful business and another is its ability to understand this data. Analytics Tools for Business will first familiarise students with the practical aspects of Artificial Intelligence (AI) and Machine Learning (ML). It will teach the underpinning statistical concepts and elementary programming for non-technical students. The students will learn how to undertake an analytics project using these concepts with the taught analytics software tools. Vendor, semi-commercial and open-source software tools will be introduced and used by students to construct and maintain a robust data workflow, analyse data and turn it into compelling insights to produce business reports. Indicative tools include IBM SPSS Modeller, Microsoft Azure, R language/markdown, Weka, Microsoft Azure and Tableau (in common use within industry). This module is software tools based and requires significant practical support. The teaching is based on real-world datasets, business case studies and industry recognised best practice approaches.
Business Analytics (BA) is about helping business to make evidence-based decisions using data
collected from sources from inside and outside a company. This data is often diverse, complex, inconsistent and needs to be processed, analysed and modelled to gain business insights for decision making. Best practice-based techniques will be used to visualise and create ML models to predict, estimate or classify in real-world examples to aid decision making based on imprecise, real-world sets of data using software tools. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology will be used throughout which provides the framework for undertaking a real-world business analytics project using analytics software tools. This includes six key steps: (1) Problem Understanding, (2) Data Understanding, (3) Data Preparation, (4) Modelling, (5) Evaluation, (6) Deployment. The student will learn how to use the range of analytics software tools provided in the labs, within the CRISP-DM framework to create a data pipeline; including data management and analysis, training models to predict, estimate or classify and visualisation for decisions.
Surrey Business School
EMROUZNEJAD Ali (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 117
Lecture Hours: 11
Laboratory Hours: 22
Prerequisites / Co-requisites
The module content will focus on a selected set of critical areas in data analytics and the software tools used. As an indication of the kind of concepts that will be covered, below is an indicative set of topics:
· Ingredients of analytics / key concepts / Taxonomy of algorithms. Types of analytics
· Classification / Clustering / Regression
· Identifying business applications / feasibility / High level design
· Using software tools
· Introduction to R Language
· IBM SPSS Modeller / Weka / etc. and high-level applications
· Linear / Non-Linear regression for Prediction
· Evaluating models & Visualising Performance
· Cross Validation / Measures / Visualising performance
· Project Life-Cycle
· Knowledge Discovery / Analytics / Decision Support
· Preparing data
· Decision Tree (ID3), Top Down / Entropy / Random Forest
· Shallow MLP & Deep Neural Networks
· Specifying the training algorithm / Gradient Descent
· Evaluating classifier performance
· Unsupervised learning / Hierarchical Cluster Analysis / Partitional / K-means / Choosing k
· Neural Network / Competitive Learning / Self-Organising Map
· Association Rule Mining / apriori algorithm
· Visualisation / The four pillars to inform
· Human perception / Design Choices / Getting it right
· Infographics / Dashboards
|Assessment type||Unit of assessment||Weighting|
|Coursework||Analytics Tools Group coursework||60|
|Examination||Analytics Tools examination||40|
An alternative to the group project is a defined individual project using a given dataset and problem description (1500 words for the individual).
The assessment strategy is designed to provide students with the opportunity to demonstrate:
· Knowledge of the entire data analytics workflow process
· Appreciation of the objectives for robust data analytics workflow processes
· Ability to apply the theories, conceptual frameworks and methodologies that underpin data analytics in common software tools and languages
Thus, the summative assessment for this module consists of:
· Analytics Tools Group coursework 3000 words (60%)
For the project coursework, the students will work either individually or in small groups, to apply and deepen their knowledge with the criteria related to learning outcomes (1) to (5). The project will include the analysis of a set of real-world data using data analytics techniques within the taught software tools and visualising the results appropriately to produce a written report. Evidence of background reading must be provided. the project provides an opportunity for the student to experience the whole process of data analytics using machine learning by using the taught software tools using the approaches introduced in the labs, to undertake all necessary steps using the CRISP-DM methodology. The student will present the project findings and conclusions in a written report, give an oral presentation and provide code used, all of which will be marked. Students will be informed about the coursework topic and individual/group at the beginning of the semester.
· Analytics Tools examination (40%)
The 2-hour examination will be a standard closed book examination, with material coming directly from the module content. The broader topics for the questions will be covered during the taught and laboratory sessions. The problems will be similar to those discussed in the taught component, computer laboratory or indicated in the essential reading material. The answers will mainly be discursive and will be in alignment with the criteria related to learning outcomes.
· Online assessments tied to lab sessions
· Group feedback on lab session results – common errors, examples of good practice.
· Individual feedback after online assessment covering key concepts.
- · Introduce students to the practical elements of the data workflow (e.g. data management,
data analysis) that turn diverse data sources into useful insights for decision-making.
- · Teach students how to conduct key exercises along the data workflow process (e.g. how to clean data, how to visualise data).
- · Introduce software tools that can be used for each part of the data workflow process (e.g. R for data analysis, Tableau for creating data dashboards).
|001||Students will know the key steps of the data workflow process||KP|
|002||Students will learn the key methods (e.g. data cleaning, data visualisation) for turning diverse data into useful insights||KPT|
|003||Students will learn which software can be used for each step of the data workflow process||KPT|
|004||Students will learn how to construct a data workflow from real data sources||CPT|
|005||Students will critically reflect on which tools better serve different data workflow requirements and decision-making objectives||CKPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
· encourage a critical understanding of the importance of a robust data workflow that translate diverse, often messy data sources into useful insights for decision-making.
· cultivate an understanding of the main issues and challenges; provide a coherent conceptual framework; develop a critical awareness of the various approaches of machine learning.
· introduce students the concept of a robust data workflow and to teach the basics of the various tools that enable operational delivery of data insights in business. The intention is to teach the objectives of the data workflow process (e.g. clean data, simple to understand insights) and how that relates to the decisions to be made off the data, rather than an exhaustive list of prospective software.
The course will be practical, encouraging students to get hands-on with example data analytics software tools that allow them to progress through all stages of the data workflow process, giving them an appreciation of the operational processes and software required to deliver effective operational data processes in business.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MAN2189
Programmes this module appears in
|Business Management (Business Analytics) BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2021/2 academic year.