Postgraduate Diploma in Data Science

We are living in a data-driven world. Society needs personnel who can apply critical analytical tools and algorithms to model and explain patterns observed in data using mathematical and statistical techniques. The qualifications will enable the learner to work with data using Microsoft Excel and Python, manage and use data, and apply a variety of statistical learning algorithms such as Logistic Regression, Support Vector Machines, Decision Trees, Boosting Algorithms, Neural Networks, K-Means Clustering and Hierarchical Clustering. The student will furthermore gain knowledge in the domain area of either finance or marketing via the elective module, to enable them to function optimally as data scientists in these fields. Society will significantly benefit from these highly sought-after professionals who can effectively operate in the fourth industrial revolution with the availability of mega data and a continued exponential increase in computing power. These professionals will contribute to society in many ways, from simple and yet essential tasks such as traffic control, customer service, online buying habits, advertising, hospital waiting time, predicting the weather and exchange rate to more advanced tasks such as self-driving cars, fraud detection, energy exploration, robotics, and genetic manipulation. Subsequently, the economy will grow in many ways due to improved efficiency, effectiveness, decisions, productivity, and profitability in all sectors. 

The purpose of this postgraduate diploma is to enable working professionals to undertake advanced study in data science. Data-driven decisions are at the centre of a range of business functions. As such, the field of data science is at the intersection of business domains, coding, mathematics and statistics. The proposed programme has a strong interdisciplinary character and is relevant to graduates in applied mathematics, finance, computer science, economics and other related disciplines. The purpose of the Postgraduate Diploma in Data Science is to equip the student with the necessary skills to extract, clean, prepare and pre-process data for feeding into the best-suited algorithm(s), and finally for presenting the data in appropriate visual and written formats. The course provides the student with theoretical knowledge and a practical experience of the most widely used supervised and unsupervised learning algorithms. The student will be taught to present the final information in the format of a fit-for-purpose report, by depicting results in appropriate tables, supported by an interpretation of results. These skills, combined with domain knowledge, will provide the prospective data scientist with the necessary ability to provide practical solutions and future predictions to complex numerical problems in business and any field where data is used extensively.

What is Distance Learning?

THE ADMISSION CRITERIA FOR THE POSTGRADUATE DIPLOMA IN DATA SCIENCE ARE:

• A Bachelor’s degree in the fields of mathematics, computer science/IT, data science, engineering, commerce, statistics, or a related field.

• Students who have not reached proficiency in Python will be required to complete the formal bridging course.

To find out more about alternate access routes at STADIO, please click HERE

At the end of the programme, successful students will be able to:

1. Apply different data exploration methods to gain a critical understanding of data, variables, and observations.

2. Draft a data science project plan to apply decision making skills to solve complex problems.

3. Demonstrate understanding of data science methods and techniques.

4. Apply data management skills to code, prepare, pre-process, manipulate, and visualise data in preparation for the model building phase.

5. Build, fit, and tune models with respect to different data science methods.

6. Interpret and critically evaluate solutions to which a particular data science method has been applied in practice.

7. Apply the built models to unused data and use domain knowledge to provide decision makers with accurate predictive information to enable the formulation of important decisions.