MATH C8Z12 - Spreadsheet Data Analytics

Module Details

Module Code: MATH C8Z12
Full Title: Spreadsheet Data Analytics
Valid From:: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 5
Module Owner:: Kevin McDaid
Departments: Unknown
Module Description: This module will provide the learner with an introduction to the field of data analytics together with an understanding of the potential of Spreadsheet technology in this area. On completion of the module the learner should understand the role of data analytics in modern business. The learner should also have a working knowledge of Excel and be capable of running an advanced statistical analysis of static data; from generating descriptive statistics to building models and creating advanced data visualisations. In addition, linking with their learning in the parallel module Statistics in R, the learner will create R based scripts for the repeated analysis of data.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Describe and critique the field of data analytics and its function in modern business.
MLO2 Implement basic statistical analysis of static data using Spreadsheet technology.
MLO3 Design and build appropriate data models using spreadsheet technology.
MLO4 Create and interpret effective Data Visualisations.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named DkIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
 
Module Indicative Content
Introduction to Data Analytics
Overview of Data Science. Types of Data/ Big Data, the role of Data Analytics in decision-making and data analytics lifecycle.
Introduction to Spreadsheets
Reading data into Excel; Basic data arithmetic & manipulation: formulas, functions and referencing; Organising data: IF/ Nested_IF’s/ VLOOKUP/ HLOOKUP; Filtering & Pivot Tables; Solver Plug-in.
Descriptive Statistics
Measures of Centrality, Variability & Correlation in excel.
Probability in Excel
Probability distribution functions in excel; Discrete (Binomial, Poisson) and Continuous (Normal) distributions.
Spreadsheet Data to Business Models
Introduction to model building in excel; Simple Linear Regression models.
Data Visualisation
Creating and interpreting appropriate data visualisation in excel; Bar Charts, Histogram, Scatterplots, Sparklines. BI reporting and visualisation tools. Dashboards.
Module Assessment
Assessment Breakdown%
Course Work30.00%
Project50.00%
Practical20.00%
Module Special Regulation
 

Assessments

Full Time On Campus

Course Work
Assessment Type Continuous Assessment % of Total Mark 30
Marks Out Of 0 Pass Mark 0
Timing n/a Learning Outcome 2,3,4
Duration in minutes 0
Assessment Description
A set of practical exercises, including short answer question exercises, small scale statistical analysis using excel, interpretation and in-class discussion of findings.
Project
Assessment Type Project % of Total Mark 50
Marks Out Of 0 Pass Mark 0
Timing Week 12 Learning Outcome 1,2,3,4
Duration in minutes 0
Assessment Description
Data Project 1. Data analysis project involving all stages of the Data Analytics Lifecycle. (From pre-processing data, generating descriptive statistics creating appropriate visualisations and building simple models, to interpreting output and communication of findings)
Practical
Assessment Type Practical/Skills Evaluation % of Total Mark 20
Marks Out Of 0 Pass Mark 0
Timing Week 6 Learning Outcome 4
Duration in minutes 0
Assessment Description
Practical Test in Excel
No Final Examination

Part Time On Campus

Course Work
Assessment Type Continuous Assessment % of Total Mark 30
Marks Out Of 0 Pass Mark 0
Timing n/a Learning Outcome 2,3,4
Duration in minutes 0
Assessment Description
A set of practical exercises, including short answer question exercises, small scale statistical analysis using excel, interpretation and in-class discussion of findings.
Project
Assessment Type Project % of Total Mark 50
Marks Out Of 0 Pass Mark 0
Timing Week 12 Learning Outcome 1,2,3,4
Duration in minutes 0
Assessment Description
Data Project 1. Data analysis project involving all stages of the Data Analytics Lifecycle.
(From pre-processing data, generating descriptive statistics creating appropriate visualisations and building simple models, to interpreting output and communication of findings)
Practical
Assessment Type Practical/Skills Evaluation % of Total Mark 20
Marks Out Of 0 Pass Mark 0
Timing Week 6 Learning Outcome 4
Duration in minutes 0
Assessment Description
Practical Test in Excel
No Final Examination

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Practical Contact There will be three-hours of lab-based classes per week. In these theory / practical sessions, the delivery of theory will be integrated with the practical implementation of that theory. Every Week 3.00 3
Directed Reading Non Contact Guided preparation, reading and exercises. Every Week 2.00 2
Independent Study Non Contact Independent reading and practical work. Every Week 3.00 3
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 3.00
Workload: Part Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Practical Contact There will be three-hours of lab-based classes per week. In these theory / practical sessions, the delivery of theory will be integrated with the practical implementation of that theory. Every Week 3.00 3
Directed Reading Non Contact Guided preparation, reading and exercises. Every Week 2.00 2
Independent Study Non Contact Independent reading and practical work. Every Week 3.00 3
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Alberto Cordoba. (2014), Understanding the Predictive Analytics Lifecycle, Wiley, [ISBN: 1118867106].
Supplementary Book Resources
  • Gregory S. Nelson. (2018), The Analytics Lifecycle Toolkit: A Practical Guide for an Effective Analytics Capability, Wiley, [ISBN: B07BB63MXH].
  • Michael Alexander. (2010), Excel Dashboards and Reports, Microsoft Press, [ISBN: 9780470620129].
  • John Walkenbach. (2015), Excel 2016 Bible, Wiley, [ISBN: 9781119067511].
  • Judith Hurwitz. (2013), Big Data For Dummies, For Dummies, [ISBN: 9781118504222].
  • Peter Kalmstrom. (2015), Excel 2016 from Scratch: Excel course with demos and exercises, kalmstrom.com Business Solutions, [ISBN: 1515134156].
This module does not have any article/paper resources
Other Resources