Module: Programming for Data Analysis

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Programming for Data Analysis
Module Code	CSC1184
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	7.5

Description

Learning Outcomes

1. Use core Python structures, file handling, and foundational libraries (NumPy, pandas) to load, parse, and manipulate tabular datasets.
2. Formulate and answer data-centric questions by applying vectorised operations, grouped aggregations, and data merging techniques.
3. Identify and execute appropriate data cleaning strategies, including handling missing values, standardizing formats, and transforming data types.
4. Explain the key statistical concepts underlying data analytics, including descriptive statistics and principles of effective visualisations (DACS4)
5. Construct and evaluate simple predictive models (e.g., linear regression, classification) and interpret their outputs and limitations in plain language.
6. Demonstrate the ability to find, access and organise appropriate information and resources in digital environments (DACS1) and effectively engage with emerging technologies and their applications in order to improve and update one's digital competence (DACS5).
7. Execute an end-to-end data analysis pipeline from raw data ingestion to final summary and communicate findings clearly.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	Weekly 2-hour lecture covering core concepts, examples, and worked solutions.
Laboratory	12	Weekly 1-hour practical lab session for guided programming exercises and tasks.
Independent Study	75.5	Independent review of lecture materials, weekly formative exercises, and ongoing coding practice.
Independent Study	76	Preparation and practice for the two comprehensive continuous assessment lab exams.
Total Workload: 187.5

Section Breakdown
CRN	21461	Part of Term	Semester 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Hossein Javidnia	Module Teacher	Stephen Blott

Type	Description	% of total	Assessment Date
Assessment Breakdown
Loop Quiz	A practical, timed laboratory examination assessing the first half of the module. Students will be evaluated on their ability to ingest, parse, manipulate, and clean tabular datasets using core Python, NumPy, and pandas.	50%	Week 6
Loop Quiz	A comprehensive practical laboratory examination assessing the second half of the module. Students will be evaluated on their ability to perform descriptive statistics, generate visualisations, fit simple predictive models, and execute an end-to-end data analysis pipeline.	50%	Week 12

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Python Foundations and Data Analysis Mindset
Introduction to data-centric programming. Representing datasets using core Python structures (lists, dictionaries). Reading, parsing, and summarising simple tabular data (CSV) without external libraries.

Vectorised Operations and Data Manipulation
Transitioning from standard Python to NumPy arrays for efficient numeric storage and vectorised operations. Introduction to pandas DataFrames. Selecting, filtering, and sorting data. Performing grouped aggregations and joining multiple datasets.

Data Cleaning and Tidy Data
Identifying and handling missing values, inconsistent entries, and incorrect data types. Implementing strategies for data imputation and standardisation to achieve a 'tidy data' structure.

Exploratory Data Analysis (EDA) and Visualisation
Choosing and generating appropriate visualisations (histograms, boxplots, scatter plots, bar charts) using pandas and matplotlib. Interpreting visual trends, central tendencies, and outliers.

Descriptive Statistics and Inference
Computing and interpreting mean, median, variance, and standard deviation. Generating correlation matrices. Understanding populations versus samples, sampling variability, and the conceptual foundations of hypothesis testing and confidence intervals.

Predictive Modeling and Pipeline Execution
Introduction to simple predictive models, distinguishing between linear regression and basic classification (e.g., logistic regression, k-NN). Interpreting model outputs (R², confusion matrices, accuracy). Executing an end-to-end mini-project from raw data ingestion to final summary.

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None