Postgraduate Diploma in Data Science - 120 UK Credits (60ECTS )

The Data Science Institute's Postgraduate Diploma in Data Science is fully accredited under the European Qualifications Framework. This programme will enable graduates from any discipline to kickstart or enhance their careers in data science and analytics.

This diploma programme is divided down into 10 modules delivered via our online platform including scheduled live class groups, practice assignments based on real world examples.

Assessment: Assessment by technical exams and projects based on real world scenarios. You choose the projects under the guidance of your course mentors and draw on the full range of skills and knowledge gained on the programme. Through your project portfolio you will be able showcase your job readiness to employers.

Duration: 6 months to complete depending on either full time or part time basis. You will need to devote your time to studying the course content in depth, attend online group sessions, work with your mentors, carry out tasks and project work and take exams during the course.

Programme includes:

• Live online group sessions
• Industry experts
• One-to-one mentoring,
• Project support
• Mobile ready Learning platform
• 100+ video lessons
• Detailed lecture slides
• Case studies
• Practical activites
• Quizzes

Developed at a masters degree level, the Data Science Institute's online data science course enables you to gain skills in maths, statistics and programming in R, Python and SQL to organise, analyse and visualise data to uncover hidden solutions that challenge traditional business assumptions and produce entirely new operating and strategic models. The projects you do as part of the course will allow you to apply Data Science to a specialist field, including Fintech, the Pharmaceutical industry, IoT, Economics or Marketing.

COURSE MODULES:

Exploratory Data Analysis

Programming in Python, R and SQL

Data management

Measures of central tendency and variation

Bivariate relationships

Data visualisation

Statistical Inference

Principles of statistical inference

Parametric tests

Non-parametric tests

Analysis of variance (ANOVA)

Fundamentals of Predictive Modelling

Predictive modelling principles

LInear regression models

Model validation

Python and R packages for predictive modelling

Advanced Predictive Modelling

Logistic regression

models

Survival analysis

Cox regression

Poisson regression

Time Series Analysis

Time Series concepts

Assessing stationarity

ARIMA, ARCH, GARCH modelling

Panel data regression

Unsupervised Multivariate Methods

Principal Component Analysis

Factor Analysis

Multidimensional Scaling

Cluster Analysis

Machine Learning 1

Naive bayes

Support Vector Machines

K nearest neighbours

Machine Learning 2

Decision Tree

Random Forest

Association Rules

Neural Networks

Text Mining and Natural Language processing

Structured and unstructured data

Text mining in R and Python

Natural language processing

Data Science in Practice

Presentation and communication skills

Synthesis of data science knowledge

Application to real world data and scenarios

This course is for both experienced data professionals and graduates from other disciplines looking to develop a a career in data science, AI and analytics.

• An undergraduate degree or equivalent in any discipline.
• Other professional qualifications at level 6 in a numerate discipline such as accounting, finance, IT, computing.
• Candidates without a degree but with at least two years' relevant experience may also apply

Graduates of this programme will find opportunities in data science, data analytics and business analysis. Sectors include banking and finance, insurance, pharmaceuticals, consulting and education.