You are provided with highly structured and detailed course content, broken down into ten distinct modules covering core skills and knowledge through our learning management system. While the course is self paced, tutors are on hand to provide support and feedback for your exam prep and project work.
* Holders of the Professional Certificate in Data Science can transfer up to 20 ECTS credits to the Postgraduate Diploma in Data Science
MODULE 1
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 pre- processing and prepare data for big data analytics. The module serves as an essential foundation for advanced analytics taught later in the course.
MODULE 2
Statistical inference is the process of drawing inferences or conclusions from data using statistical techniques. This is at the core of data science, and a strong understanding of statistics from the beginning is the prime ingredient for a competent data scientist. In this module, you will cover sampling, statistical distribution, hypothesis testing, and variance analysis and use R code to carry out various statistical tests and draw inferences from their output.
MODULE 3
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.
MODULE 4
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.
MODULE 5
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.
MODULE 6
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.
MODULE 7
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.
MODULE 8
The Machine Learning 2 module continues developing your machine learning knowledge and you will cover decision tree, random forest and neural network algorithms for regressionand 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.
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Decision Tree
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Random Forest
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Association Rules
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Neural Networks
MODULE 9
This module looks at analysing unstructured data such as that found 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.
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Structured vs unstructured data
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Text mining in R and Python
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Text mining using ggplot2
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Sentiment analysis using R and Python
MODULE 10
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.
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Presentation and communication skills
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Synthesis of data science knowledge
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Application to real world data and scenarios
