Module Code: H9DAP
Long Title Database and Analytics Programming
Title Database and Analytics Programming
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: Arghir Moldovan
Module Author: Arghir Moldovan
Departments: School of Computing
Specifications of the qualifications and experience required of staff

PhD/MSc in a computing or cognate discipline. May have industry experience also.

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Analyse, compare, contrast and critically evaluate the characteristics of programming languages, programming environments and database systems commonly utilised for data analytics solution implementation.
LO2 Critically assess the challenges associated with processing big data datasets and compare and contrast programming for big data vis-à-vis programming for conventional datasets.
LO3 Evaluate tools and techniques for managing the data pipeline and preparing data for further analysis through data wrangling, cleaning, and validation.
LO4 Critically assess methods and practices for software development in order to design and implement data programming requirements.
LO5 Evaluate, design and implement solutions for processing datasets by using key programming patterns and constructs for data analytics, relevant programming languages, and suitable database systems.
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

A level 8 degree or its equivalent in any discipline


Module Content & Assessment

Indicative Content
Module IntroductionIntroduction to Data Programming
Overview of programming languages, tools and frameworks for data analytics, and productionalizing tools (e.g., GitHub). Programming types (imperative, declarative, functional, logic, etc.);. Data analytics methodologies;. Algorithm design, Program I/O
Overview of the data programming language
Syntax and semantics, expressions and statements, basic data types, conversion and coercion, built in data structures (arrays, matrices, lists, etc.), indexing data structures, program flow control and iteration
Input/Output and Functions
Input/output data from structured/semi-structured file formats (csv, xml, json);. Input data from the Internet (e.g., web scraping);. Defining functions;. Lambdas for functional programming
More Advanced Data Operations
Dealing with NA values;. Catching exceptions;. Use of support libraries (e.g., Pandas, Numpy, dfply);. Regular Expressions; . Text analytics
Database Programming – Relational Databases
Database system technologies;. Programmatically connecting to databases;. Create/Read/Update/Delete (CRUD) Operations;. SQL Optimization, Indexing and Normalization
Database Programming – NoSQL Databases, Data Lakes
NoSQL Overview and Data Models; Document Model, Key-Value Model, Column Family, Aggregates, Graph Model, Triple Stores);. NoSQL Data Modelling Concepts;. Query Languages for Data in NoSQL; NoSQL systems
ETL and Data Pipelines Data Cleaning, wrangling and validation
Developing programs for data processing activities (e.g., data extraction, cleaning, merging, aggregation, analysis, reporting) . Data wrangling techniques
Data plotting and visualisation
Plotting and visualisation principles;. Plotting libraries (e.g., Matplotlib, ggplot);. Dashboard frameworks (e.g., Plotly)
Big Data Programming
Challenges associated with programming for big data;. Parallelism for computational processes;. Distributed computing platforms for big data processing
Design patterns
Data science patterns;. Design patterns for big data processing
Data streaming
Stream input sources, live data stream, window-based transformations, combination of batch and stream computations
Productionalizing Data Analytics
Tools, testing, Portable Format for Analytics (PFA),. Integrating machine learning models into production,. Data Security
Assessment Breakdown%


Full Time

Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Continuous Assessment % of total: 30
Assessment Date: n/a Outcome addressed: 4,5
Non-Marked: No
Assessment Description:
This assessment will consist of practical tasks in the form of an in-class test. This will assess learners’ knowledge and competences on programmatically processing and analysing datasets including operations with database connectivity.
Assessment Type: Project % of total: 70
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
The terminal assessment will consist of a project that will evaluate all learning outcomes. Learners will have to identify and carry out a series of analyses of a large dataset (or a collection of large datasets that are somehow related or complement each other), utilising appropriate programming languages, tools and techniques (e.g., data wrangling) for data preparation, programming environments and database systems. The final submission will consist of an academic research paper style report as well as the implemented data analytics artefact.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
The reassessment strategy for the Database and Analytics Programming module will consist of a project that will assess all learning outcomes. Students who fail the module will be afforded an opportunity to do the repeat project over the Summer months.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Every Week 24.00
Tutorial Other hours (Practical/Tutorial) 24 Every Week 24.00
Independent Learning Independent learning (hours) 202 Every Week 202.00
Total Weekly Contact Hours 48.00

Module Resources

Recommended Book Resources
  • Todd Morley. (2019), Data Science Design Patterns, 1st edition. Addison-Wesley Professional, p.512, [ISBN: 9780134000053].
  • Bill Chambers,Matei Zaharia. (2018), Spark: The Definitive Guide, Big Data Processing Made Simple,, O'Reilly Media, [ISBN: 978-1491912218].
  • Thomas A. Runkler. (2012), Data Analytics, Springer Science & Business Media, p.137, [ISBN: 978-3834825889].
  • Wes McKinney. (2017), Python for Data Analysis, O'Reilly Media, p.550, [ISBN: 978-1491957660].
Supplementary Book Resources
  • Paul Teetor. (2011), R Cookbook, "O'Reilly Media, Inc.", p.413, [ISBN: 978-0596809157].
  • Nathan Marz,James Warren. (2015), Big Data, Manning Publications Company, p.328, [ISBN: 978-1617290343].
  • Tom White. Hadoop, O'Reilly Media, [ISBN: 9781491901687.].
  • Donald Miner,Adam Shook. (2016), Mapreduce Design Patterns, O'Reilly Media, p.275, [ISBN: 9781491927922].
This module does not have any article/paper resources
Other Resources
Discussion Note: