Qualifi Level 3 Diploma in Data Science- Foundation

Qualification title: Qualifi Level 3 Diploma in Data Science

Qualification type: Vocational Related Qualification

Level: 3

Accreditation status: Accredited

TQT: 600

Guided learning hours: 455

Credit Equivalency: 60

Qualification number (RQF) : 610/1950/1

Availability: UK and International

Course Length: 6 Months or More

Study Mode: Online, Blended, On Campus.

Fees: On campus £6,500

Progression routes: Progress to QUALIFI Level 4 Diploma in Data Science; Apply for entry to a UK university for an undergraduate degree OR Progress to employment in an associated profession.

Progression FAQ’s: https://qualifi.net/progression-faqs/

Course 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.

Units

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

Awarding Classification/Grading

All unit grading is shown on the qualification transcript.

Fail – 0-39%

Pass – 40%-59%

Merit – 60% – 69%

Distinction 70%+

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.

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 initial assessment by the centre 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 interview or other appropriate processes.