COMP08143 2018 Data Analytics

General Details

Full Title
Data Analytics
Transcript Title
Data Analytics
Code
COMP08143
Attendance
N/A %
Subject Area
COMP - Computing
Department
COEL - Computing & Electronic Eng
Level
08 - NFQ Level 8
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2018 - Full Academic Year 2018-19
End Term
9999 - The End of Time
Author(s)
Fran O'Regan, John Weir, Donny Hurley
Programme Membership
SG_KSECU_E08 201800 Certificate in Secure IT and Deep/Machine Learning SG_KSOFT_E08 201900 Certificate in Software Development SG_KSFTD_K08 201900 Bachelor of Science (Honours) in Computing in Software Development (Add On) SG_KSODV_K08 201900 Level 8 Honours Degree Add-on in Software Development SG_KSODV_H08 201900 Bachelor of Science (Honours) in Computing in Software Development SG_KCMPU_H08 201900 Bachelor of Science (Honours) in Computing
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 two hour lectures will be used to introduce core mathematical concepts required for data analysis and the differences with 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.

Additional 1.5hr to support student enquiries, online lecture setup, emails.

 

Module Assessment Strategies

The students will be assessed by a final exam contributing to 60% of their final grade. An ongoing project will be submitted before the end of term and will consist of analysing multiple sets of given big data sets - applying appropriate statistical analysis and drawing conclusions on that data. This project will be worked on throughout the semester with milestones applied throughout.

Repeat Assessments

Repeat exam 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

End of Semester / Year Formal Exam
100 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Data Project Continuous Assessment Project 40 % OnGoing 2,3,4
             
             

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final Exam Final Exam Closed Book Exam 60 % End of Semester 1,2,3
             
             

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Lecture Theatre Lecture 1 Weekly 1.00
Laboratory Practical 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 3.00 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 2.62 Hours

Required & Recommended Book List

Recommended Reading
2017-09-29 An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) Springer
ISBN 1461471370 ISBN-13 9781461471370

An Introduction to Statistical Learning This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.

Recommended Reading
2017-01-26 Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R Springer

This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital.

The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R as well as supplementary material that will enable the reader to quickly adapt all methods to their own applications.

Recommended Reading
2012-12-22 MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems O'Reilly Media
ISBN 1449327176 ISBN-13 9781449327170

Design patterns for the MapReduce framework, until now, have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you're using. Each pattern is explained in context, with pitfalls and caveats clearly identified - so you can avoid some of the common design mistakes when modeling your Big Data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. Hadoop MapReduce code is provided to help you learn how to apply the design patterns by example. Topics include: Basic patterns, including map-only filter, group by, aggregation, distinct, and limit Joins: traditional reduce-side join, reduce-side join with Bloom filter, replicated join with distributed cache, merge join, Cartesian products, and intersections Binning, sharding for other systems, sorting, sampling, unions, and other patterns for organizing data Job optimization patterns, including multi-job map-only job folding, and overloading the key grouping to perform two jobs at once

Recommended Reading
2017-04-21 Big-Data Analytics for Cloud, IoT and Cognitive Computing Wiley-Blackwell
ISBN 1119247020 ISBN-13 9781119247029
Recommended Reading
2015-11-04 Getting Started with Python Data Analysis Packt Publishing

Learn to use powerful Python libraries for effective data processing and analysis

About This Book

  • Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib
  • Create, manipulate, and analyze your data to extract useful information to optimize your system
  • A hands-on guide to help you learn data analysis using Python

Who This Book Is For

If you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.

What You Will Learn

  • Understand the importance of data analysis and get familiar with its processing steps
  • Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis
  • Create effective visualizations to present your data using Matplotlib
  • Process and analyze data using the time series capabilities of Pandas
  • Interact with different kind of database systems, such as file, disk format, Mongo, and Redis
  • Apply the supported Python package to data analysis applications through examples
  • Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python library

In Detail

Data analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.

With this book, we will get you started with Python data analysis and show you what its advantages are.

The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.

Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.

Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn.

Style and approach

This is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.

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