MATH09002 2018 Data Analysis and Information Management

General Details

Full Title
Data Analysis and Information Management
Transcript Title
Data Analysis and Information
Code
MATH09002
Attendance
N/A %
Subject Area
MATH - Mathematics
Department
ESCI - Environmental Science
Level
09 - NFQ Level 9
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2018 - Full Academic Year 2018-19
End Term
9999 - The End of Time
Author(s)
Padraig McGourty
Programme Membership
SG_SWATE_M09 201800 Master of Science in Water Services Management SG_SWATE_O09 201800 Postgraduate Diploma in Science in Water Services Management
Description

This module will equip the learner with the skills to professionally analyse, intrepret and communicate technical information in the area of the biological and environmental sciences. Includes data gathering, data management, data visualization, design and analysis of scientific experiments, hypothesis testing, linear modelling and data analysis using appropriate statistical software.

Learning Outcomes

On completion of this module the learner will/should be able to;

1.

Organise and Manipulate data using appropriate software

2.

Demonstrate that they can graphically display and numerical summarise data using appropriate descriptive statistics

3.

Apply probability and probability distributions to data analysis

4.

Choose and apply appropriate tests of hypotheses based on a research problem and the characteristics of a dataset

5.

Model the relationships between variables using regression analysis

6.

Use an appropriate statistical software package to perform statistical analysis of data

Teaching and Learning Strategies

The teaching methods used will be a combination of online lectures, self study, on line tutorials, problem solving exercises and computer based learning.

Module Assessment Strategies

The student will be assessed by means of both summative and formative assessment. The summative assessment will consist of three practical based projects where the student will be examined on both their theoretical knowledge of data analysis and statistics and their use of statistical analysis software to apply this knowledge with the emphasis on the practical application of statistics. They will also take an end of module exam which will concentrate on their theoretical knowledge

The student will also have access to online self-assessment quizzes as part of the formative assessment. These quizzes will allow the student to monitor their own progress on the module as well as identify any knowledge gaps they may have.

Repeat Assessments

The student will be given a oppurtunity to do a repeat practical project covering the learning outcomes assessed in the projects detailed in the assessment strategy if they do not meet the requirements to pass the module and their project work was of a sub standard level.

Indicative Syllabus

Descriptive Statistics

  • Classification of data into types. 
  • Graphical Representation of data including frequency tables and charts 
  • Measures of Central Tendency, Position and Dispersion. 
  • Skewness 

Probability

  • Laws of Probability
  • Algebra of Events 
  • Mutually Exclusive Events 
  • Independent Events 
  • Probability Distributions
  • Normal Distribution 
  • Binomial Distribution 
  • Poisson Distribution 


Sampling Theory

  • Sample selection methods based on study design
  • Sample Size
  • Estimation 
  • Point and Interval estimates 


Hypothesis Tests for Means and Proportions

  • Introduction to Hypothesis Testing 
  • One Sample, Independent Samples and Paired Samples t-tests 
  • One-Way ANOVA and related Post Hoc Tests 
  • Repeated Measures ANOVA and related Post Hoc Tests 

Correlation and Regression

  • Pearson's Correlation Co-efficient 
  • Significance of the correlation co-efficient 
  • Relationship Modelling 


Non Parametric Tests

  • Introduction to Non-Parametric hypothesis tests 
  • Chi-Square test for association 
  • Mann-Whitney test 
  • Kruskal Wallis test 
  • Wilcoxon signed-rank test 
  • Spearman's Rho 


Use of statistical Packages for data analysis

  • Introduction to SPSS
  • Introduction to R

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
50 %
End of Semester / Year Formal Exam
50 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Practical Project - Descriptive Statistics Project Individual Project 15 % Week 5 1,2,6
2 Practical Project - Hypothesis Tests Project Individual Project 20 % Week 9 3,4,6
3 Practical Project - Regression Analysis Project Individual Project 15 % Week 13 5,6
4 Moodle Quizzes Formative Multiple Choice - % OnGoing

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final Exam Final Exam Closed Book Exam 50 % End of Semester  
             
             

Online Learning Mode Workload


Type Location Description Hours Frequency Avg Workload
Online Lecture Distance Learning Suite Lecture 1 Weekly 1.00
Tutorial Distance Learning Suite Practical Tutorial 1 Weekly 1.00
Total Online Learning Average Weekly Learner Contact Time 2.00 Hours

Module Resources

Non ISBN Literary Resources

N/A