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MATH09002 2019 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 - 0541 Mathematics
Department
ESCI - Environmental Science
Level
09 - NFQ Level 9
Credit
05 - 05 Credits
Duration
Semester
Fee
€
Start Term
2019 - Full Academic Year 2019-20
End Term
9999 - The End of Time
Author(s)
Padraig McGourty
Programme Membership
SG_SWATE_M09 201900 Master of Science in Water Services Management SG_SWATE_O09 201900 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/Short Answer Test - % OnGoing 1,2,3,4,5,6

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 1,2,3,4,5,6
             
             

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

Required & Recommended Book List

Recommended Reading
2016-11-07 Environmental Data Analysis de Gruyter
ISBN 3110430010 ISBN-13 9783110430011

Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields. Contents: Preface Time series analysis Chaos and dynamical systems Approximation Interpolation Statistical methods Numerical methods Optimization Data envelopment analysis Risk assessments Life cycle assessments Index

Recommended Reading
1994-12-20 Environmental Statistics and Data Analysis CRC Press
ISBN 0873718488 ISBN-13 9780873718486

This easy-to-understand introduction emphasizes the areas of probability theory and statistics that are important in environmental monitoring, data analysis, research, environmental field surveys, and environmental decision making. It communicates basic statistical theory with very little abstract mathematical notation, but without omitting important details and assumptions. Topics include Bayes' Theorem, geometric distribution, computer simulation, histograms and frequency plots, maximum likelihood estimation, the tail exponential method, Bernoulli processes, Poisson processes, diffusion and dispersion of pollutants, normal distribution, confidence intervals, and stochastic dilution; gamma, chi-square, and Weibull distributions; and the two- and three-parameter lognormal distributions. The author also presents the Statistical Theory of Rollback, which allows data analysts and regulatory officials to estimate the effect of different emission control strategies on environmental quality frequency distributions. Assuming only a basic knowledge of algebra and calculus, Environmental Statistics and Data Analysis provides an outstanding reference and collection of statistical procedures for analyzing environmental data and making accurate environmental predictions.

Recommended Reading
2009-12-16 Scientific Data Management Chapman and Hall/CRC
ISBN 1420069802 ISBN-13 9781420069808

Dealing with the volume, complexity, and diversity of data currently being generated by scientific experiments and simulations often causes scientists to waste productive time. Scientific Data Management: Challenges, Technology, and Deployment describes cutting-edge technologies and solutions for managing and analyzing vast amounts of data, helping scientists focus on their scientific goals. The book begins with coverage of efficient storage systems, discussing how to write and read large volumes of data without slowing the simulation, analysis, or visualization processes. It then focuses on the efficient data movement and management of storage spaces and explores emerging database systems for scientific data. The book also addresses how to best organize data for analysis purposes, how to effectively conduct searches over large datasets, how to successfully automate multistep scientific process workflows, and how to automatically collect metadata and lineage information. This book provides a comprehensive understanding of the latest techniques for managing data during scientific exploration processes, from data generation to data analysis. Enhanced by numerous detailed color images, it includes real-world examples of applications drawn from biology, ecology, geology, climatology, and more. Check out Dr. Shoshani discuss the book during an interview with International Science Grid This Week (iSGTW): http://www.isgtw.org/?pid=1002259

Module Resources

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Additional Information

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