Level 3 Diploma

IN DATA SCIENCE

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.

Regulated by the Office of Qualifications and Examinations Regulation

Approved by World Education Services (WES)

Member of the International Association of Universities
(IAU)

Awarding Body

Awarding Body

Regulated by the Office of Qualifications and Examinations Regulation

Approved by World Education Services (WES)

Member of the International Association of Universities
(IAU)

Awarding Body
Qualifi

Awarding Body
CQHE

Registration Documents

School : Computing

Location : Mount Lavinia

Learning Mode : Online | Physical

Duration : 06 - 08 months

Entry Requirements

Learning Outcomes

  • 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

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