COMP07169 2018 Introduction to Data Analytics

General Details

Full Title
Introduction to Data Analytics
Transcript Title
Introduction to Data Analytics
Code
COMP07169
Attendance
N/A %
Subject Area
COMP - Computing
Department
COMP - Computing & Creative Practices
Level
07 - NFQ Level 7
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_KSMAR_B07 201800 Bachelor of Science in Computing in Smart Technologies SG_KAPPL_B07 201800 Bachelor of Arts in Computing in Application Design and User Experience SG_KAPPL_H08 201900 Bachelor of Arts (Honours) in Computing in Application Design and User Experience SG_KSMAR_H08 201900 Bachelor of Science (Honours) in Computing in Smart Technologies
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.

Learning Outcomes

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

1.

Utilise computer software to load in and manipulate data sets of any size and structure.

2.

Perform statistical analyses on data using appropriate methodology.

3.

Examine the principles of hypothesis testing and be able to identify an appropriate test and interpret the results.

4.

Understand the basic laws of probability and apply different probability distributions.

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. 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

Students will be assessed by continous assessment and a final exam. The final exam will examine the core mathematical concepts while the continuous assessment will be an ongoing project built upon in the labs. The ongoing project will be submitted before the end of term and will consist of analysing multiple sets of given data - applying appropriate statistical analysis and drawing conclusions on that data.

Repeat Assessments

Repeat exam and/or project

Indicative Syllabus

Perform statistical analyses on data using appropriate methodology.

  • A brief recall summary of descriptive statistics e.g. mean, median, standard deviation, interquartile range, as applicable to data analytics.
  • Simple random sampling and confidence intervals.

Describe the principles of hypothesis testing and be able to identify an appropriate test and interpret the results.

  • Draw scatter plots, understand and calculate correlations, linear regression. 
  • Hypothesis testing to verify/disprove assumptions about data sets using real world data.

Utilise computer software to load in and manipulate data sets of any size and structure.

  • Using software packages to perform data analysis and interpreting the results. 
  • Excel as an introduction to data analysis.
  • Another programmable software package and library e.g. Python, R

Understand the basic laws of probability and apply different probability distributions.

  • A look at probability distributions
  • Normal Distribution
  • Binomial Distribution

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 Project analysing a given data set Project Project 50 % OnGoing 1,3
             
             

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 2,3,4
             
             

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lecture 2 Weekly 2.00
Laboratory Practical Computer Laboratory Practical 3 Weekly 3.00
Independent Learning Not Specified Independent Learning 3 Weekly 3.00
Total Full Time Average Weekly Learner Contact Time 5.00 Hours

Required & Recommended Book List

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
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.

Recommended Reading
2017-11-03 Python for Data Analysis, 2e O′Reilly
ISBN 1491957662 ISBN-13 9781491957660

Module Resources