Overview

The aim of the QUALIFI Level 3 Diploma in Data Science is to provide learners with an introduction and understanding of the field of data science.

The Level 3 Diploma provides a contemporary and holistic overview of data science, artificial intelligence, and machine learning, from the birth of artificial intelligence and machine learning in the late 1950s, to the dawn of the “big data” era in the early 2000s, to the current applications of AI and machine learning and the various challenges associated with them. In addition to the standard machine learning models of linear and logistic regression, decision trees and k-means clustering, the diploma introduces learners to two new exciting and emerging areas of data science: synthetic data and graph data science.

The Diploma also introduces learners to the data analytical landscape and associated analytical tools, teaching introductory Python so that Learners can analyse, explore, and visualise data, as well as implement a number of basic data science models.

Successful completion of the QUALIFI Level 3 Diploma in Data Science provides learners with the opportunity to progress to further study or employment.

Entry requirements

• Approved Centres are responsible for reviewing and making decisions as to the applicant’s ability to complete the learning programme successfully and meet the demands of the qualification. The centre's initial assessment will need to consider the support that is readily available or can be made available to meet individual learner needs, as appropriate.
• The qualification has been designed to be accessible without artificial barriers that restrict access. For this qualification, applicants must be aged 18 or over.
• Entry to the qualification will be through centre-led registration processes, which may include an interview or other appropriate processes.

• The Field of Data Science
• Python for Data Science
• Creating and Interpreting
• Visualisations in Data Science
• Data and Descriptive Statistics in Data Science
• Fundamentals of Data Analytics
• Data Analytics with Python
• Machine Learning Methods and Models in Data Science
• The Machine Learning Process
• Linear Regression in Data Science
• Logistic Regression in Data Science
• Decision Trees in Data Science
• K-means Clustering in Data Science
• Synthetic Data for Privacy and Security in Data Science
• Graphs and Graph Data Science

Learning Outcomes

• Gain the mathematical and statistical knowledge and understanding required to conduct basic data analysis.
• Develop analytical and machine learning skills with Python.
• Develop a strong understanding of data and data processes, including data cleaning, data structuring, and preparing data for analysis and visualisation.
• Understand the data science landscape and ecosystem, including relational databases, graph databases, programming languages such as Python, visualisation tools, and other analytical tools.
• Understand the machine learning processes, understanding which algorithms to apply to different problems, and the steps required build, test and verify a model.
• Develop an understanding of contemporary and emerging areas of data science, and how they can be applied to modern challenges.