The Professional Development Award (PDA) in Data Science is designed to help learners develop the practical knowledge and analytical skills needed to work confidently with data in a modern digital environment. As organizations increasingly rely on data to support decision-making, there is growing demand for people who can collect, manage, interpret and communicate information effectively.

PDA in Data Science introduces learners to the complete data journey, from acquiring and preparing data to analyzing patterns and presenting meaningful insights. Along the way, learners develop skills in programming, statistical thinking, data visualization and problem solving while applying recognized techniques to realistic scenarios and projects.

Rather than focusing solely on technical knowledge, PDA in Data Science encourages learners to think critically about the quality, reliability and ethical use of data. They learn how to transform raw information into evidence that can support informed decisions in business, public services, research and many other sectors.

Course Contents

• Experience Analytics
• Data Collection and Storage
• Artificial Intelligence and Machine Learning
• Introduction to Spreadsheets
• Introduction to Tableau
• Introduction to SQL
• Introduction to Python
• Embarking on a Career in Data Analytics

Target Audience

The PDA in Data Science is intended for learners who want to develop practical data analysis and data science skills for employment, further study, or professional development.

This qualification is suitable for:

• School leavers preparing for careers in data, digital technologies, or business.
• College students wishing to develop practical data science skills alongside other qualifications.
• Employees seeking to up-skill or re-skill in data analysis, reporting, or business intelligence.
• Career changers interested in moving into data-related roles.
• Individuals working in sectors such as business, finance, healthcare, engineering, manufacturing, retail, or the public sector who use data to support decision-making.
• Anyone with an interest in analyzing, interpreting, and communicating data using modern digital tools.

Learners may progress to further specialized training, education, or employment opportunities in Data Science. These qualifications are designed to support advancement along various pathways within Data Science or related fields. However, individuals completing lower levels may require additional training before being adequately prepared for employment in data science-related roles. Options include:

• Entry-level roles involving data analysis, reporting, business intelligence, or digital support.
• Professional development within existing employment where data skills are increasingly important.
• A broad range of vendor certifications and training, such as Microsoft PL-300, AI – 900 and Google Data Analytics Beginner professional certificate etc. that learners may pursue.
• Learners who complete the SCQF Level 7, can move into 8 within the PDAs may also progress to higher qualifications, such as the HNC and HND in Data Science (Next Generation Qualifications) or Computing.
• Further or higher education programmes in data analytics, computing, artificial intelligence, or related disciplines.

Career Opportunities

Successful completion of the course can support progression into roles such as:

• Data Analyst (Junior)
• Junior Data Technician
• Business Intelligence (BI) Assistant
• Data Administrator
• Reporting Analyst
• Management Information (MI) Assistant
• Business Support Analyst
• Data Quality Officer
• Research Assistant
• Digital Analyst
• Operations Analyst
• Marketing Data Assistant
• Customer Insights Assistant
• Database Support Assistant
• Information Officer

Entry Requirements

Entry to level 7; has no pre-requisites and is the recommended entry point for most learners.

Learners are expected to have:

• Basic digital literacy and confidence using computers.
• Fundamental numeracy skills.
• Experience with spreadsheets (such as Microsoft Excel or similar software) is beneficial but not essential.
• An interest in problem-solving and working with data.

Learning Modes:

• Blended Learning – Students attend in-person classes combined with online learning activities. This approach provides face-to-face support from instructors while allowing students flexibility to complete some coursework remotely.
• Distance Learning – Students complete their studies remotely without attending regular in-person classes. Learning materials, lessons, and assessments are delivered online, with support provided through digital platforms and communication with instructors.
• Self-Paced Learning – Students work through course materials independently and progress at their own speed. This option offers maximum flexibility, allowing students to choose when and how quickly they complete their learning, while meeting the required course outcomes and deadlines.

Application Process

If there are any concerns regarding your eligibility, you may be required to attend an interview with a member of the course team. This may be in person, over the phone, or through Microsoft Teams.

SAAS Funding:

Course funding is available for this course through SAAS, including the QS exam fee.

Self-Funding:

If any student does not qualify for SAAS funding, they can self-finance this course.