southampton

Module overview

This course provides essential knowledge and skills required to support students’ future learning in future data analytic modules.

Without prerequisite knowledge in data analytics, this module will cover spreadsheet analytics and relational database tools used in many business cases.

The spreadsheet modelling includes an introduction to basic spreadsheet functions, moving onto a range of more complex functions and a variety of data manipulation methods, including producing appropriate graphs. Students will experience the range of what is possible within spreadsheets through learning specific techniques.

The relational database section starts with database modelling, database implementation, query execution and business problem solving using Structured query language (SQL). The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.

Aims and Objectives

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

use spreadsheets to perform data manipulation;
create relational databases and execute queries;
work with relevant software packages to develop analytic solutions.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

self-manage the development of learning and study skills;
plan and control effectively for successful completion of a personal workload, including working within a team;
communicate effectively.

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

the underlying principles for spreadsheet and database design;
basic concepts of spreadsheet and databases;
how to provide solutions specifically designed for implementation in databases and spreadsheets.

Learning and Teaching

Teaching and learning methods

Teaching methods include:

Lectures
Computer labs
Directed reading
Private/guided study

Learning activities include:

Introductory lectures
Instructions to work through in computer labs
Group assignment
Case study / problem solving activities
In class debate and discussion
Private study
Use of video and online materials

Study time
Type	Hours
Practical classes and workshops	22
Completion of assessment task	46
Preparation for scheduled sessions	20
Follow-up work	40
Revision	8
Lecture	11
Total study time	147

Resources & Reading list

Textbooks

Winston, Wayne L (2014). Marketing Analytics: Data-Driven Techniques with Microsoft Excel. John Wiley & Sons.

Winston, Wayne L (2019). Microsoft Excel 2019 Data Analysis and Business Modeling (Business Skills). Microsoft Press.

Date, Chris (2012). Database Design and Relational Theory: Normal Forms and All That Jazz. O’Reilly Media, Inc.

Shmueli, Galit et al (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.

Dick Kusleika (2021). Data Visualization with Excel® Dashboards and Reports. John Wiley & Sons.

Michael Alexander ; Dick Kusleika; John Walkenbach (2019). Excel 2019 Bible. John Wiley & Sons, Inc..

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Class participation

Assessment Type: Formative
Feedback:
Final Assessment: No
Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Multiple choice Test	20%
Group Assessment	80%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Report	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Report	100%

Repeat Information

Repeat type: Internal & External

Analytics Implementation I: Spreadsheets and Databases