The Level 3 Certificate in Data Science and Analytics introduces learners to the foundational principles of data handling, analysis, and interpretation. This 60-credit qualification is designed for individuals looking to start a career in data science or enhance their data literacy skills. The programme covers essential tools such as Python, data visualization, machine learning, and statistical techniques, enabling students to extract insights and make data-driven decisions.
This course bridges academic theory with practical applications, preparing learners for further study in data science or entry-level analytical roles in diverse industries.
- Level 3 Certificate in Data Science and Analytics
- Certificate Level Exit Award
06 Months
Certificate Level 3
| Study Mode | Delivery Mode | Language | Credits |
|---|---|---|---|
| Full Time / Part Time | Live (Online) Delivery | English | 60 |
Modules: |
|---|
| 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 |
| Level 3 in Data Science |
The course is composed of core modules focusing on essential data science skills and concepts, from foundational programming to advanced analytics techniques.
Core Modules:
The Field of Data Science
Explore the scope, roles, and impact of data science in modern industries.
Python for Data Science
Learn programming essentials using Python for data handling and computation.
Creating and Interpreting Visualisations in Data Science
Master the art of visual storytelling using data dashboards and plots.
Data and Descriptive Statistics in Data Science
Gain foundational statistical knowledge to summarize and interpret datasets.
Fundamentals of Data Analytics
Understand data analysis processes, data types, and analytical approaches.
Data Analytics with Python
Apply Python to perform data wrangling, cleaning, and exploratory data analysis.
Machine Learning Methods and Models in Data Science
Learn about key machine learning algorithms and when to use them.
The Machine Learning Process
Understand the life cycle of a machine learning project—from data to deployment.
Linear Regression in Data Science
Use regression techniques to model relationships between variables.
Logistic Regression in Data Science
Apply binary classification methods to solve practical problems.
Decision Trees in Data Science
Explore tree-based algorithms for classification and regression tasks.
K-means Clustering in Data Science
Understand unsupervised learning through clustering and pattern detection.
Synthetic Data for Privacy and Security in Data Science
Learn how synthetic data is used to protect privacy and enhance security.
Graphs and Graph Data Science
Analyse relationships and networks using graph-based data science methods
By the end of the course, learners will be able to:
- Understand the core concepts and applications of data science and analytics.
- Use Python to manipulate, analyse, and visualise data.
- Apply statistical techniques to interpret datasets.
- Build and evaluate simple machine learning models.
- Communicate insights using professional visualisation tools.
- Recognise the importance of data privacy, ethics, and secure handling.
Applicants should meet at least one of the following:
- A high school diploma or equivalent qualification.
- Basic computer or IT knowledge; prior experience in data, mathematics, or programming is advantageous but not required.
Upon completion, learners can progress to:
- Level 4 Diploma in Data Science, Data Analytics, or Information Technology
- Level 5 Extended Diploma in Data Science
- Year 1 of a Bachelor’s Degree in Data Science or Computer Science
- Certifications in data-related fields (e.g., Python, SQL, Power BI, or Tableau)
Graduates may qualify for roles such as:
Data Analyst (Junior Level)
Business Intelligence Assistant
Research Data Technician
Data Science Support Analyst
Entry-Level Python Programmer
Reporting and Visualisation Assistant
Level 4 Diploma in IT-related rectors
Level 4 Diploma in DS
Level 5 Extended Diploma in DS
Year 1 of Bachelor's Degree
Directly into employment in an associated profession
The aim of the 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 Level 3 Diploma in Data Science provides learners with the opportunity to progress to further study or employment.
- 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
- K-means Clustering in Data Science
- Decision Trees in Data Science
- Synthetic Data for Privacy and Security in Data Science
- Graphs and Graph Data Science
60
06 Months