The Postgraduate Diploma in Data Science is a fully rounded programme covering the key skills and knowledge needed by data scientists to develop insights and solutions across a wide range of industries and domains.
It has been developed by practising data scientists with experience working with major international firms across a wide range of industries.
They have identified key skills required for data scientists and have also ensured the course content conforms to the Edison European Data Science Framework’s Body of Knowledge (DS-BoK).
Who should take this course?
The Postgraduate Diploma in Data Science is an advanced qualification designed for graduates with at least a first degree or equivalent in a numerate discipline from a recognized institution. It is suitable for both those wishing to move into data science from other areas and experienced data analysts and data science professionals.
Those who have completed the institute’s Postgraduate Certificate in Data Science can transfer 24 ECTS credits towards the Postgraduate Diploma in Data Science.
Postgraduate Diploma Modules
Exploratory Data Analysis
Most industry analysis starts with exploratory data analysis and a thorough study of this will help you to perform data health checks and provide initial business insights. You will gain a sound understanding of Python and R programming, descriptive statistics, data management and data visualisation. You will also learn SQL for big data preprocessing and prepare data for big data analytics.
- Programming Basics in Python and R
- Data management
- Measures of central tendency and variation
- Bivariate relationships
- Data visualisation
Statistical inference is the process of drawing inferences or conclusions from data using statistical techniques. This is at the core of data analytics and data science, and a strong understanding of statistics from the beginning is the prime ingredient for a competent data analyst. In this unit, you will cover the fundamentals of sampling, statistical distribution, hypothesis testing, and variance analysis and use Python and R code to carry out various statistical tests and draw inferences from their output.
- Fundamental principles of statistical inference
- Standard parametric tests
- Non-parametric tests
- Analysis of Variance
Fundamentals of Predictive Modelling
Solutions to many business problems are related to
successfully predicting future outcomes. This module
introduces predictive modelling and provides a foundation
for more advanced methods and machine learning. You’ll
gain an understanding of the general approach to predictive modelling and then build simple and multiple linear regression models in Python and R and apply these in a range of contexts.
- Predictive modelling principles
- Linear regression models
- Model validation
- Python and R packages and functions for predictive modelling
Advanced Predictive Modelling
In this module, you are introduced to model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and this module covers detailed model building processes. Multinomial and ordinal logisitic regression are also covered.
- Logistic regression models
- Survival analysis
- Cox regression
- Poisson regression
Time Series Analysis
In this module, time series forecasting methods are introduced and explored. You will analyse and forecast macroeconomic variables such as GDP and inflation, as well as look at complex financial models using ARCH and GARCH, ARIMA, time series regression, exponential smoothing and other models.
- Time series concepts
- Assessing stationarity
- ARIMA, ARCH, GARCH modelling
- Panel Data Regression
Unsupervised Multivariate Methods
Data reduction is a key process in data science and you will learn to apply data reduction methods such as principal component analysis, factor analysis and multidimensional scaling. You will also learn to segment and analyse large data sets using clustering methods, another key analytical technique that brings out rich business insight if carried out skillfully.
- Principal Component Analysis
- Factor Analysis
- Multidimensional Scaling
- Cluster Analysis
Machine Learning 1
Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this Machine Learning 1 module, you will understand applications of the support vector machine, K nearest neighbours and naive bayes algorithms for classification and regression problems using case studies from a range of industries and sectors.
- Naive Bayes Method
- Support Vector Machine Algorithm
- K nearest neighbours
Machine Learning 2
The Machine Learning 2 module continues developing your machine learning knowledge and you will cover decision trees, random forest and neural network algorithms for regression and classification, again drawing on case studies from real-world data. You will have the opportunity to compare the performance of machine learning algorithms against classical statistical models and learn to assess which are most appropriate for specific scenarios.
- Decision Tree
- Random Forest
- Association Rules
- Neural Networks
Text Mining and Natural Language processing
This module looks at analysing unstructured data such as that found in social media, newspaper articles, videos and more. In particular, you will look at methods for text mining and natural language processing using R and Python code to produce graphical representations of unstructured data and carry out sentiment analysis.
- Structured vs unstructured data
- Text mining in R and Python
- Text mining using ggplot2
- Sentiment analysis using R and Python
Data Science in Practice
The Data Science in Practice module provides you with an opportunity to yor apply knowledge through project work. You will select a project from a specific domain and appropriately apply exploratory data analysis, statistical methods and select appropriate advanced modelling techniques. This module also develops your scientific communication skills through the preparation of project reports and presentations.
- Presentation and communication skills
- Synthesis of data science knowledge
- Application to real-world data and scenarios
Aim of the Program
The overall aims of the Postgraduate Diploma in Data Science will enable you to:
Gain the mathematical and statistical knowledge and understanding needed to carry out data analysis to an advanced level
Develop essential data science skills in the R, Python and SQL programming languages
Develop a strong understanding of data management, including evaluation, structuring and cleaning of data for analysis
Become familiar with and apply the tools and techniques used in data visualisation
Develop a comprehensive knowledge of classical data analytics, including statistical inference, predictive modelling and time series analysis
Use new generation algorithms for supervised and unsupervised machine learning
Apply a wide range of data science skills, knowledge and techniques within real-world contexts, including building a portfolio of work to demonstrate a practical understanding of the field.
Assessment and Projects
The course is assessed by assignments and exams for each module and a guided capstone project based on real-world data and scenarios provides the opportunity to bring together elements of the overall course.
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