Once you graduate from the postgraduate diploma in data science, you’ll be in a position to take advantage of the rapidly growing number of jobs in a wide range of data science and advanced analytics roles throughout all kinds of organization. In addition, this internationally recognized certification in data science will enable you to progress to advanced entry to a data science online masters, data analytics or business.
With the emergence of cloud computing, big data and artificial intelligence, data science has become a key fourth generation profession. The convergence of these technologies has given rise to new and powerful approaches to developing business insights and decision making based on the analysis of vast amounts of data using advanced statistical techniques and complex machine learning algorithms.
Developed at a masters degree level, Digita’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.
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 R and Python programming, as well as the fundamentals of statistics. This includes writing R and Python commands for data management, basic statistical analysis, performing descriptive statistics and presenting data using appropriate graphs and diagrams. This unit serves as a foundation for advanced analytics.
In the statistical inference unit you will gain an in-depth understanding of statistical distribution and hypothesis testing. This includes Binomial, Poisson, Normal, Log Normal, Exponential, t, F and Chi Square distributions, as well as parametric tests and non-parametric tests used in research problems. The unit will help you to formulate research hypotheses, select appropriate tests for them, write R and Python programs to perform hypothesis testing and draw inferences using the outputs generated.
A good understanding of predictive modelling is an essential part of being an effective data scientist as many business problems are related to successfully predicting future outcomes. This fundamentals of predictive modelling unit provides a strong foundation for predictive modelling and covers the entire modelling process in the context of real life case studies. Many concepts in predictive modelling methods are commonly used in business and therefore these concepts will be discussed in detail.
In this advanced predictive modelling unit you will learn model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and detailed model building processes for binary dependent variables are covered. In addition, multinomial models and ordinal scaled variables will also be discussed.
In this time series analysis unit, 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.
In this unsupervised multivariate methods unit you will learn that data reduction is a key process in business analytics projects and you will learn to apply data reduction methods such as principal component analysis, factor analysis and multi-dimensional 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.
Machine learning algorithms are new generation algorithms and used in conjunction with classical predictive modelling methods. In the machine learning unit you will understand applications of various machine learning techniques including the Naïve Bayes Method, Support Vector Machine Algorithm, Decision Tree, Random Forest, Association Rules and Neural Networks.
In this unit, you are introduced to further key knowledge areas associated with data science. This includes analysis of unstructured data using Text Mining, handling data with SQL and building interactive web apps straight from R using the Shiny package. You will also explore the Hadoop framework and further concepts in Big Data Analytics and Artificial Intelligence.
This is a business focussed unit that introduces you to key business concepts at a postgraduate level to complement your data science skills and knowledge. It covers topics including leadership skills, entrepreneurship, innovation, ethics and sustainability, globalisation and organisational culture, encouraging you to evaluate data science within wider contexts.
Course applicants should have one of the following:
A degree or equivalent in any discipline
Other professional relevant qualifications
Candidates without a degree but with at least three years relevant experience may also apply
Assessment and Student project
The Postgraduate Diploma in Data Science is assessed through a combination of technical exams and a major student project.
Exams account for 40% of your overall assessment and you will take three online exams to test your technical knowledge. Exams consist of multiple choice questions, Short answers, and code samples.
A student project accounts for the remaining 60% of your assessment and you will have the opportunity to apply a wide range of data science skills and knowledge to real world problems and scenarios. It will also provide an opportunity for you to build a portfolio for future employment
The Postgraduate Diploma in Data is accredited by Qualifi, a recognised UK awarding body. It carries 120 (60 ECTS) on the regulated qualifications framework.
The qualification is at level 7 on the European Qualifications Framework, with equivalents in the following countries:
Republic of Ireland (NFQ) – Level 9
England and Northern Ireland (RQF) – Level 7
Scotland (SQF) – Level 11
Qualifications at this level recognise highly developed and complex levels of knowledge that enable the development of in-depth and original responses to complicated and unpredictable problems and situations and are at a levelequivalent to master’s degrees.
Graduates of the Postgraduate Diploma in Data Science will be able to apply for entry to university master’s programmes in data science, analytics and other related degrees and many cases transfer credits from the diploma to these.
The usual fee for this programme is £3000, however it is sometimes offered with a discount. Please check our payment options for current fee offers and payment plans
Learners who successfully complete the Postgraduate Diploma Data Science will find opportunities in a wide range of roles and organisations including:
Banking and Finance
To apply for the Postgraduate Diploma in Data Science, in the first instance fill in the apply now form. One of our team will get back to you within a day to discuss any questions you have about the programme and entry requirements before completing your course purchase.