COMP08143 2024 Data Analytics
Full Title
Data Analytics
Transcript Title
Data Analytics
Code
COMP08143
Attendance
N/A %
Subject Area
COMP - 0613 Computer Science
Department
COEL - Computing & Electronic Eng
Level
08 - Level 8
Credit
05 - 05 Credits
Duration
Semester
Fee
€
Start Term
2024 - Full Academic Year 2024-25
End Term
9999 - The End of Time
Author(s)
Mr. John Kelleher, Stephen Kohlmann
Programme Membership
SG_KSODV_H08 202400 Bachelor of Science (Honours) in Computing in Software Development
SG_KSODV_K08 202400 Bachelor of Science (Honours) in Computing in Software Development
SG_KSFTD_K08 202400 Bachelor of Science (Honours) in Computing in Software Development
SG_KCMPU_H08 202400 Bachelor of Science (Honours) in Computing
SG_BBUSJ_H08 202300 Bachelor of Business (Honours) in Business with Supply Chain Management
SG_KSECU_E08 202400 Certificate in Secure IT and Deep/Machine Learning
SG_KSOFT_E08 202400 Certificate in Software Development
Description
The module is intended to help students to understand the necessary skills to interpret numerical and graphical information and describe data appropriately. It will introduce some basic concepts for statistical inference and utilise computer software to interpret data. Additionally, it will introduce students to the concept of Big Data.
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Appraise Data Analytics and the emergence of big data.
2.
Analyse results from data using appropriate statistical methodology.
3.
Examine and implement different hypothesis testing techniques for decision making.
4.
Develop computer software for the solution of statistical problems.
Teaching and Learning Strategies
A practical approach to teaching and learning will be used. Problem-based learning will be used where possible. The lectures will be used to introduce core mathematical concepts required for data analysis and the differences between big data. The lab practicals will be used to run statistical analysis software on a collection of data sets to see the practical applications of the core concepts.
Module Assessment Strategies
The students will be assessed by an end of term in-class assessment contributing to 30% of their final grade. A project worth 60% will be submitted before the end of the term and will consist of analysing multiple data sets - applying appropriate statistical analysis and drawing conclusions from that data. This project will be worked on throughout the semester with milestones applied throughout. Additionally, 10% of the grade will take the form of an in-class test in the middle of the semester, to provide ongoing feedback to the students.
Repeat Assessments
Repeat in class assessment and/or project.
Indicative Syllabus
Appraise Data Analytics and the emergence of big data.
- Statistics and sampling as it relates to Data Analytics
- Big Data and its implications.
- Possibilities and ethics of big data collection.
- Random sampling.
Analyse results from data using appropriate statistical methodology.
- Linear regression and correlations.
- Implement a big data programming model such as MapReduce.
Examine and implement different hypothesis testing techniques for decision making.
- Hypothesis testing to verify/disprove assumptions about data sets using real world data.
- p-values
- Null hypothesis
- Confidence Interval
Develop computer software for the solution of statistical problems.
- Big Data Distributed system such as Hadoop.
- Big Data Query Language such as Hive.
- Python and/or R to develop software.
Coursework & Assessment Breakdown
Coursework & Continuous Assessment
100 %
Coursework Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | In Class Practical Exam | Practical | Assessment | 30 % | End of Semester | 1,3,4 |
| 2 | Data Project | Coursework Assessment | Project | 60 % | OnGoing | 2,3,4 |
| 3 | In Class MCQ | Coursework Assessment | Assessment | 10 % | Week 8 | 1,2 |
Full Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Lecture Theatre | Lecture | 2 | Weekly | 2.00 |
| Practical / Laboratory | Computer Laboratory | Practical | 2 | Weekly | 2.00 |
| Independent Learning | Not Specified | Independent Learning | 4 | Weekly | 4.00 |
Total Full Time Average Weekly Learner Contact Time 4.00 Hours
Online Learning Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Online | Online Delivery | 1.5 | Weekly | 1.50 |
| Independent Learning | Not Specified | Independent Learning | 4.5 | Weekly | 4.50 |
| Directed Learning | Not Specified | Directed Learning | 1.12 | Weekly | 1.12 |
Total Online Learning Average Weekly Learner Contact Time 1.50 Hours
Required & Recommended Book List
Recommended Reading
2017-04-21 Big-Data Analytics for Cloud, IoT and Cognitive Computing Wiley-Blackwell
ISBN 1119247020 ISBN-13 9781119247029
2017-04-21 Big-Data Analytics for Cloud, IoT and Cognitive Computing Wiley-Blackwell
ISBN 1119247020 ISBN-13 9781119247029
Recommended Reading
2015-07-02 OpenIntro Statistics OpenIntro, Inc.
ISBN 194345003X ISBN-13 9781943450039
2015-07-02 OpenIntro Statistics OpenIntro, Inc.
ISBN 194345003X ISBN-13 9781943450039
Recommended Reading
2017-10 Python for Data Analysis O'Reilly Media
ISBN 1491957662 ISBN-13 9781491957660
2017-10 Python for Data Analysis O'Reilly Media
ISBN 1491957662 ISBN-13 9781491957660
Recommended Reading
2016 Python Data Science Handbook O'Reilly Media
ISBN 1491912057 ISBN-13 9781491912058
2016 Python Data Science Handbook O'Reilly Media
ISBN 1491912057 ISBN-13 9781491912058
Recommended Reading
2015-04-25 Data Science from Scratch O'Reilly Media
ISBN 149190142X ISBN-13 9781491901427
2015-04-25 Data Science from Scratch O'Reilly Media
ISBN 149190142X ISBN-13 9781491901427
Module Resources
Non ISBN Literary Resources
Journal Resources
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Additional Information
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