
Data science is an interdisciplinary of computer science and statistics; hence you have to do both Diplomas in Data Analytics and Data Science Statistical Methods before embarking on Advanced Diploma.
Diploma in Data Analytics
Data Analytics is revolutionising the way businesses operate. Many companies, regardless of size, need data insight to make decisions and develop strategies in order to drive growth and maintain a competitive edge. On completion of any Diploma programme or higher, learners interested in pursuing Big Data technology, can register for Diploma in Data Analytics.
Why does the programme exist – Data is pervasive in business. Managing internal data is bad enough; now with big data, its even worse. If not well managed, organisations might end up wasting a lot of time and incurring a lot of cost. The purpose of this course is discussing tools and techniques in data analytics and management.
How it fits into the larger programme – Today’s data comes from many sources and has different forms. (i) Text, audio, video, hashtag, multimedia, links etc. There is a lot of variability in terms of: Dimensions, Data type, Frequency (ii) Social media (facebook, twitter, weibo) (iii) Internet of Things (IoT) data from devices (smartphone, laptop, fitbit, windmills /weather, vehicles, jet engines, Point of Sale (PoS), financial data). [remember; there are billions of devices generating data 24/7]
For whom it was designed – Anyone interested in using powerful data visualisation tools to help stakeholders understand the nature of the results, and the recommended actions to take. Since data science is changing the way we work, use data and the way organisations understand the world; job opportunities and future potentials are unlimited.
How it will benefit learners – Data analysis isn't new. What is new is the vast quantities of data available from massively varied sources: from log files, email, social media, sales data, patient information files, sports performance data, sensor data, security cameras. This course gives knowledge on how to analyse data to add organisations competitive edge by investigating data, exploring the best ways in order to create value to the business.
Diploma in Foundations of Data Science Statistical Methods using Excel
Data Science is an interdisciplinary field which is a combination of computer science and statistics. The purpose of this course is to focus on statistical methods aspects. Because statistics formulas can be daunting and also moving with technology, instead of using manual calculating devices (calculators), learners will use Excel formulas when solving all statical methods calculations.
Why does the programme exist – Data science is the study of data in order to extract meaningful business insights using two fields of statistics; Descriptive Statistics and Inferential Statistics.
How it fits into the larger programme – Uncertainty is the biggest source of difficulty both in business and in developing machine learning algorithm. At this level, the aim is for learners to understand probability as a measure of uncertainty and its role in data science.
For whom it was designed – Knowledge in data science core fundamentals is becoming a major influence in today's data driven decision world. To master the field of Artificial Intelligence (AI) and Machine Learning (ML), it is vital to know data science fundamental concepts.
How it will benefit learners – Without the knowledge of turning data into actional insights, big data is worthless. This course provides the core statistical methods and techniques (mean, standard deviation, hypothesis testing, sampling, analysis of variance and correlation regression which underpins data analysis framework.
Advanced Diploma in Data Science & Business Analytics
Both Diploma in Data Analytics and Diploma in Foundations of Data Science Statistical Methods using Excel are equally important in laying the ground knowledge that will eventually lead to developing Machine Learning (ML) and Artificial Intelligence (AI) models. Data Science is part computer science and part statistics. Diploma in Data Analytics covers major computing topics while the Diploma in Foundations of Data Science Statistical Methods using Excel covers the statistical methods, which provide the core algorithms for data science. Machine Learning (ML) contains a number of algorithms used to dissect and summarise data in order to make predictions. These algorithms are divided into supervised and unsupervised learning algorithms. With the knowledge attained from both Diploma programmes, learners can then embark on Advanced Diploma in Data Science & Business Analytics and build models using supervised and unsupervised learning algorithms.
Why does the programme exist – Machine Learning (ML) is key to unlocking value for business, using algorithms to study data and extract meaningful business insights. Due to the enormous variety of data and volumes, learners need not only learn how to develop models, but the different techniques and computation powers that store and process the data; hence the course includes Programming, SQL and Power BI Data Modelling techniques.
How it fits into the larger programme – there are algorithms and steps in creating machine learning models. Learners will be able to describe supervised and unsupervised learning algorithms, the end goals of each algorithm, the types of algorithms and the two major software programmes used in developing models; R and Python Programming.
For whom it was designed – anyone interested in Machine Learning (ML) and Artificial Intelligence (AI) technologies: (i) supervised learning (using Regression and Classification algorithms) (ii) unsupervised learning (clustering and association).
How it will benefit learners – machine learning concepts are used almost in every business setting; healthcare, cyber security, finance, social media, recommendation, marketing, research etc. Understanding uses and implementation of machine learning help learners decide specialisation fields of interest. This course provides skills and competencies in core disciplines: data analytics/science, statistics, computer science and business.