Fundamentals of Data Science

The ability to extract value from the large amounts of data that are being captured is immensely important for businesses, industries, and sectors across a multitude of domains.  Data science is the field dedicated to extractinxg insights and value from large volumes of data.  It is multidisciplinary, drawing from computer science, mathematics, statistics, and domain-specific knowledge to analyse the data and uncover patterns, trends, and actionable insights.

To thrive in such a data-rich environment, professionals need a solid foundation in data science, which this module will address. It focuses on building essential software development and subject knowledge skills to implement data science solutions. 

Students will be introduced to the key concepts and techniques used in the data science ecosystem and explore the various stages of the data science lifecycle including data preparation, handling, exploration and visualisation, modelling, and results interpretation.  This will be achieved through the utilisation of a suitable programming language such as Python or R, depending on the student’s programme.

Throughout this module, students will be exposed to real-world practical examples and problems and learn how data science can be used to tackle them and gain insight from the available data. 

The syllabus of this module will include the following (Python version): 

Data Science Introduction

• Definition and rationale
• Software tools: Jupyter Notebooks, Numpy, SciPy, ScikitLearn, Matplotlib, Pandas 

Mathematical and Statistical Foundations

• Matrices, Vectors and the implementation of operations on those using suitable scientific computing tools such as NumPy
• Introduction to statistics: population, sample, central tendency (mean, mode, median), variability (range, standard deviation, variance), hypothesis testing 

Data Science Lifecycle:

• Data collection and analysis: data sources, data types, exploratory data analysis (EDA), and data visualisation
• Data preprocessing: data manipulation, feature selection and engineering
• Data modelling: statistical models, line fitting, forecasting
• Evaluation: performance metrics and visualisation 

Machine Learning

• Classification, Regression
• Supervised Learning and Unsupervised Learning

At the end of this module the student will be able to:

• Discuss the suitability of real-world data sources for processing within the data science lifecycle
• Demonstrate an understanding of the mathematical and statistical foundations used in data science and applied to data
• Select and apply suitable data preprocessing, visualisation and modelling techniques using a suitable programming language
• Demonstrate an understanding of the various steps within the data science lifecycle and the tools and techniques used within each step
• Implement data science methods effectively across the data science lifecycle

This is an SCQF Level 9 module, and upon successful completion, participants will be awarded 20 credits.

 

Delivery 

To be confirmed.

 

Course presenter

This module will be delivered by Dr Tahir Mahmood.

 

Funding

This course may be available on a fully funded basis to some delegates.  Further details, including regarding eligibility, are available under Funding Support.

If you have any questions, please contact us at cpd@uws.ac.uk.

 

NOTE: This is a university module and upon approval of your application, you will be invited to register and then supported to complete enrolment. To enrol on the university system, the first step involves security set-up using the Microsoft Authenticator app; you will need to ensure that you have a compatible smartphone.

Further information is available at the Student Information Portal.

To access this module via the CPD route, individuals should be ordinarily resident in Scotland.  If you do not meet this criteria, please enquire here.