Module Code: H9PFDS
Long Title Programming for Financial Data Science
Title Programming for Financial Data Science
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Rohit Verma
Module Author: Andrea Del Campo Dugova
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Lecturer    PhD/Master’s degree in a computing or cognate discipline. May have industry experience also.
Tutor    PhD/Master’s degree 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 Investigate and evaluate key concepts and assess how to apply and utilise appropriate scientific libraries to perform computational analyses on complex datasets and practical problem domains.
LO2 Design and implement programs to solve different mathematical problems using real-world financial data.
LO3 Critically assess different data analytics approaches to apply to the data and draw appropriate conclusions from the data analytics results.
LO4 Critically assess different data analytics approaches to apply to the data and draw appropriate conclusions from the data analytics results.
LO5 Critically review and apply appropriate data mining and machine learning methods to the real-world FinTech problems
Dependencies
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

Programme entry requirements must be satisfied.
 

Module Content & Assessment

Indicative Content
Basic programming concepts
Data types and structures. Loops and conditional statements. Custom functions. Structured programming.
Practical applications of programming
Data input and output. Learn different file formats. Sources of data, data repositories. Understanding data characteristics (continuous, discrete, nominal, binary, structured, unstructured, time-series data) and applying manipulation techniques to data.
Libraries for scientific computing and data analysis I
Advanced data manipulation techniques.
Libraries for scientific computing and data analysis II
Discretisation and binning. Transformation Strategies. Scaling (normalisation, standardisation); dealing with categorical data. Feature selection.
Libraries for scientific computing and data analysis III
Transformation Strategies. Dealing with outliers, Data Splitting. Dealing with missing values. Handling class imbalance.
Visualization and Exploratory Data Analysis
Understand trends, outliers, and patterns in data through appropriate visualisations such as scatter plots, histograms, boxplots, pie charts, bar charts, overlayed bar charts, clustered bar charts, line charts, heatmaps, etc. Measures of central tendency (mode, median, mean) Measures of dispersion (range, variance, standard deviation)
Statistical Analysis -Hypothesis & Inference
Statistical analysis, different kinds of hypothesis tests, Standard Errors Hypothesis Testing, Parametric Tests (e.g., T-Test, ANOVA, regression), Non-parametric Tests (e.g., chi-square tests) Correlation, Z-statistic, Distributions, Sample size, Confidence intervals, significance levels, p-values, effect size
Classification and Evaluation
Concept of classification and its role solving real-world (FinTech) problems. Splitting a dataset, training, testing and validation, cross validation. Resampling methods. Model Evaluation. Performance measure: Accuracy, Prediction Score, Confusion Matrix, ROC curve, AUC, Precision, Recall, F1. Sample size.
Classification and Financial Text data
Vectorisation. TF-IDF weighting. Sparse vectors. Dense vectors - Word Embedding Vectors.
Regression
Linear Regression (LR). Multiple LR. LR for finance data. Regularisation. Evaluating regression models.
Time series Analysis
Smoothing data, Analysing time series, curve fitting, seasonality. Moving averages, ARIMA (Seasonal, Non-seasonal)
Clustering
K-means, Density-based clustering.
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed:  
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: 20
Assessment Date: n/a Outcome addressed:  
Non-Marked: No
Assessment Description:
Assessment will be through an in-class, open book test, that will require learners to retrieve, extract, manipulate and present data. Learners will be also asked to make statistical inferences and draw conclusions about a population.
Assessment Type: Project % of total: 80
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 analytic tasks upon a large dataset (or a collection of datasets that are somehow related or complement each other), utilising appropriate tools and techniques for data extraction, processing, analysis and critical evaluation. The final submission will consist of an academic research paper style report as well as the implemented data analytics artefact. It is also expected students to present and communicate the results/insights of their study.
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 repeat strategy for this module is by a project that covers all learning outcomes.

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 and demonstrations 24 Per Semester 2.00
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Tutorial Independent learning 89 Per Semester 7.42
Total Weekly Contact Hours 10.42
Workload: Blended
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom and demonstrations 12 Per Semester 1.00
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Directed Learning Directed e-learning 12 Per Semester 1.00
Independent Learning Independent learning 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom and demonstrations 24 Per Semester 2.00
Independent Learning Independent learning 89 Per Semester 7.42
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • John D. Kelleher,Brian Mac Namee,Aoife D'Arcy. (2020), Fundamentals of Machine Learning for Predictive Data Analytics, second edition, MIT Press, p.853, [ISBN: 978-0262044691].
  • Hastie, T., Tibshirani, R., & Friedman, J.. (2016), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Ed. Springer.
  • Aurelien Geron. (2019), Hands-On Machine Learning with Scikit-Learn, Keras & Technology, 2nd ED. O’REILLY.
Supplementary Book Resources
  • Kevin P. Murphy. (2012), Machine Learning: A Probabilistic Perspective, MIT Press, p.1102, [ISBN: 978-0262018029].
  • Ian Goodfellow,Yoshua Bengio,Aaron Courville. (2016), Deep Learning, MIT Press, p.801, [ISBN: 978-0262035613].
  • James, G., Witten, D., Hastie, T., & Tibshirani, R.. (2017), An Introduction to Statistical Learning, Springer, p.426, [ISBN: 978-1461471370].
  • Max Kuhn,Kjell Johnson. (2013), Applied Predictive Modeling, Springer, p.600, [ISBN: 978-1461468486].
  • Shai Shalev-Shwartz,Shai Ben-David. (2014), Understanding Machine Learning, Cambridge University Press, p.415, [ISBN: 978-1107057135].
  • John Hearty. (2016), Advanced Mastering Learning with Python, Packt Publishing, p.278, [ISBN: 978-1784398637].
This module does not have any article/paper resources
Other Resources
Discussion Note: