MATH07033 2020 Data Management

General Details

Full Title
Data Management
Transcript Title
Data Management
Code
MATH07033
Attendance
N/A %
Subject Area
MATH - Mathematics
Department
ESCI - Environmental Science
Level
07 - NFQ Level 7
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2020 - Full Academic Year 2020-21
End Term
9999 - The End of Time
Author(s)
Cian Taylor
Programme Membership
SG_SECOL_B07 202000 Bachelor of Science in Environmental Science with Ecology SG_SECOL_H08 202000 Bachelor of Science (Honours) in Environmental Science with Ecology
Description

This module is designed to introduce the learner to the subject of data management as applied to topics in environmental science. The
 module will first cover data handling, representation and interpretation. The module will introduce
descriptive statistics before moving on to cover inferential statistics.

Learning Outcomes

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

1.

Demonstrate knowledge of data handling, representation and interpretation.

2.

Make use of descriptive statistics to summarise a data set in an appropriate manner.

3.

Apply inferential statistics to make robust judgements about a population of interest by analysis of sample data from the
population.

4.

Choose an appropriate statistical approach (descriptive or inferential) relevant to a particular data set in order to make robust conclusions from it.

Teaching and Learning Strategies

This module will be delivered full-time. This will include lectures, computer practicals, and will be augmented by independent learning and directed learning. This approach is expected to address student learning needs. Moodle will be used as a repository of educational resources and as a means of assessment (e.g. quizzes, uploading assignments). 

Module Assessment Strategies

This module is 100% Continuous Assessment.

The continuous assessment will take place in a computer practical lab and will consist of two aspects:

1. Use of statistical software to demonstrate achievement of learning outcomes.

2. Completion of Moodle quizzes to demonstrate knowledge as it is accumulated during the semester. This portion of the CA will take place on an ongoing basis over the semester and will consist of 6 separate Moodle quizzes. Each quiz will be opened when the relevant topics are covered and will remain open thereafter for the rest of the semester.

 

Repeat Assessments

Repeat Continuous Assessment.

Indicative Syllabus

Demonstrate knowledge of data handling, representation and interpretation.

  • Data types.
  • Entering data into statistical software package.
  • Sorting, filtering, scrubbing and formatting data using statistical software.
  • Graphical representation and interpretation of data using statistical software: bar charts, trend charts, box plots.
  • Working with statistical distributions: normal, binomial, poisson.

Make use of descriptive statistics to summarise a data set in an appropriate manner.

  • Generating summary statistics by calculator and statistical software: mean, median, mode, standard deviation, variance, range. 
  • Representing summary statistics graphically using statistical software: bar chart with error bars, box plot.

Apply inferential statistics to make robust judgements about a population of interest by analysis of sample data from the
population.

  • Sampling schemes and random sampling.
  • Use of a random number generator for sample selection.
  • Generation of confidence intervals, standard error.
  • Bivariate linear regression using statistical software.
  • t-test, F-test, ANOVA using statistical software.
  • Introduction to non-parametric testing.

Choose an appropriate statistical approach (descriptive or inferential) relevant to a particular data set in order to make robust conclusions from it.

  • Hypothesis testing.
  • Use and interpretation of t-test, F-test, ANOVA, non-parametric testing using software by p-value approach.
  • Application of statistics to final year project scenarios.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
100 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Moodle Quizzes Continuous Assessment Open Book Exam 30 % OnGoing 1,2,3
2 Computer Practical Test Continuous Assessment Closed Book Exam 10 % Week 3 1,2
3 Computer Practical Test Continuous Assessment Closed Book Exam 15 % Week 6 1,2,3
4 Computer Practical Test Continuous Assessment Closed Book Exam 20 % Week 9 1,2,3,4
5 Computer Practical Test Continuous Assessment Closed Book Exam 25 % End of Semester 1,2,3,4

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Computer Laboratory Lecture and Computer Practical 3 Weekly 3.00
Independent Learning Not Specified Independent Learning 4 Weekly 4.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Required & Recommended Book List

Recommended Reading
2002-02-28 Practical Statistics for Environmental and Biological Scientists Wiley
ISBN 0471496650 ISBN-13 9780471496656
Recommended Reading
2006-03-01 Using Statistics Gill Education
ISBN 0717140229 ISBN-13 9780717140220

Using Statistics Containing practical worked examples and problems with answers, in an Irish context, this book provides an insight into the various processes in statistics. It is suitable for undergraduate students of business, science and engineering, post-graduate students, researchers and professionals. Full description

Recommended Reading
2008-05-30 Student Projects in Environmental Science Wiley
ISBN 9780470845660 ISBN-13 9780470845660
Required Reading
2015-05-26 An Introduction to Statistics using Microsoft Excel 2nd Edition ACPIL
ISBN 1910810169 ISBN-13 9781910810163

The handling of numbers in arithmetic and the progression into the more abstract field of mathematics and statistics is generally approached poorly in our education system. The inadequacy is not necessarily in the teaching techniques or the books and other text used but rather in the attitude towards these subjects. These subjects are seen as something which has to be taught because it is part of a preordained curriculum rather than a set of tools which are available to help people live a fuller, more productive and more interesting life. It is so enlightening when one hears people say, "I thought that when I left school I was leaving all the maths stuff behind me!" or "I was bored witless by all those numbers and formulas [sic] that were forced down my throat." This book was written out of a frustration at seeing statistics taught through formal methods using large scale statistic software packages. It seemed to me that very little was learned by this process and quite often both the teachers and the students were in denial. It is true that the students were generally able to pick up enough knowledge to pass an examination or to complete a piece of research. But I seldom saw anything which could be regarded as deep learning and the little which had been learned did not stay for any length of time in the heads of these learners. I know people who have passed several university level courses in statistics and they can hardly recall never mind use any of what was taught to them.

Module Resources

Non ISBN Literary Resources

As per book list.

Journal Resources

NA

URL Resources

http://www.statisticalsolutions.ie/downloads/ (e-Book)

https://statistics.laerd.com/

https://stattrek.com/

https://www.excel-easy.com/

https://www.excel-easy.com/data-analysis.html

 

 

Other Resources

Excel with Data Analysis ToolPak.

SPSS software.