Diploma in Data Analytics with AI (16-37 weeks program)

Diploma in Data Analytics with AI

• Ask us for upcoming cohort dates and detailed schedule!
• CSN-Fundable!

Build Job-Ready Data & AI Skills

Unlock the full potential of data and artificial intelligence with the Data Analyst with AI Bootcamp, a structured, live-online programme designed to develop professional-level analytical capability from the ground up.

Over 16 weeks, you will learn how to collect, clean, analyse, visualise, and interpret data using industry-leading tools including Python, SQL, Excel, and Power BI. The programme culminates in a professional-calibre capstone project that demonstrates your ability to apply data analytics and AI techniques in real-world scenarios.

Delivered by a European professional learning institute, this cohort-based bootcamp brings together motivated learners and working professionals in an interactive environment aligned with European business contexts and time zones. Whether you are advancing within your current role or transitioning into a data-focused career, this programme equips you with highly transferable, in-demand skills.

Key Features

• Course & Materials in English
• This program is eligible for CSN!
• Beginner to Intermediate Level: Structured to support both foundational learners and those building on existing knowledge.
• Live & Expert-Led: Weekly interactive online sessions delivered by experienced industry practitioners over 16 or 37 weeks.
• Comprehensive Learning Volume: 560 total learning hours, including 260 supervised instructional hours.
• Full Data Skillset Development: Master Python, SQL, Excel, and Power BI alongside responsible data and AI practices.
• Blended Tools Approach: Integrate no-code/low-code platforms with programming fundamentals for modern analytical versatility.
• Continuous Assessment: Group assignments, practical exercises, and hackathon-style challenges reinforce applied learning.
• Professional Capstone Project: Deliver a portfolio-ready end-to-end analytics project.
• Certificate of Completion: A professional credential validating applied Data Analytics & AI proficiency.
• Comprehensive Support System: Access facilitators, coding coaches, subject-matter experts, student welfare support, and a dedicated community team.
• Community and student support via Discord
  
  

Time commitment

• Option 1 – Full-Time Track
• 16 weeks, Monday to Friday, 10.00-18.00 CET
• Option 2 – Part-Time Track
• 37 weeks, with one evening per week plus one Saturday session every two weeks

*Additional time is required for self-directed learning.

What Makes This Programme Distinct?

• Applied, Career-Focused Training

This is not a theory-only programme. Through supervised instruction, practical assignments, and collaborative hackathons, you will apply concepts immediately to realistic business scenarios.

• Integrated AI Perspective

Beyond traditional analytics, the programme introduces AI-enhanced approaches, ensuring your skillset remains future-ready and aligned with evolving industry expectations.

Tailored Learning Support

• Personalised Mentorship

Benefit from direct access to expert mentors, coding coaches, and industry practitioners who provide guidance throughout your learning journey.

• Progress Monitoring & Feedback

Track your advancement through structured learner analytics and ongoing performance feedback, helping you stay aligned with your professional goals.

• Structured, Step-by-Step Development

Progress through carefully designed modules that simplify complex topics into manageable stages. Each phase includes portfolio-ready projects supported by peer collaboration and instructor feedback — allowing you to build confidence and genuine expertise.

Learning Outcomes

By the end of this programme, participants will be able to:

• Collect, prepare, and clean datasets from multiple sources for analysis.
• Perform data analysis using Python, SQL, and other industry-standard tools.
• Build dynamic visualisations and dashboards to communicate insights.
• Apply statistical and predictive techniques to uncover patterns and forecasts.
• Understand ethical and legal requirements in data governance and compliance.
• Complete an end-to-end capstone project demonstrating practical data analytics ability.

Target Audience

• Non-technical professionals who regularly work with data and want to boost their analytical capability.
• Team members and managers seeking to lead data-driven projects.
• Individuals aiming to upskill or transition into data analytics or business intelligence roles.
• Employees need to reduce dependence on specialist teams by developing in-house data skills.

Admission Process

● Step 1 – Register Your Interest

Tell us you’re interested in learning, and we’ll help you get started on the right path.

● Step 2 – Complete the Admissions Process

Submit your application to secure your place. This step includes completing a short taster course focussed on Data Analytics

● Step 3 – Complete a strategy call with our Delivery Partner

A member of our delivery partner’s team will have a conversation with you about your goals for the programme and answer any questions you may have, helping you ensure that the programme is right for you.

● Step 4 – Enroll and start learning!

Course Content

Foundation

• Introduction: Gain a solid understanding of data analysis and its business impact.
• Tools & Fundamentals: Build essential skills using core tools and Python programming basics.

Python & Data Analysis Libraries

• Jupyter Notebooks: Perform numerical computing and data handling.
• Data Preparation: Learn how to clean, structure, and manage incomplete datasets.
• Data Visualisation: Develop skills to create clear and compelling visual insights.
• Hackathon Experience: Apply ETL processes and visualisation techniques in a competitive, practical setting.

Core Techniques & Dashboarding

• Statistics: Understand probability, hypothesis testing, and key statistical methods.
• Exploratory Data Analysis: Analyse datasets to uncover patterns and actionable insights.

Advanced Techniques

• Machine Learning: Explore predictive modelling, advanced ML techniques, and performance evaluation.
• Neural Networks: Gain foundational knowledge and practical experience with neural network models.
• Applied Projects: Implement clustering methods such as K-Means and evaluate real-world case studies.

Ethics, Projects & Presentation

• Ethics & Compliance: Examine privacy, governance, and legal considerations in data usage.
• Career Development: Discover career pathways and build essential professional skills.
• Capstone Project: Apply your knowledge to real-world data challenges and present actionable insights.
• Continuous Learning: Receive guidance on further study and professional development.

FAQ

Who will I be learning with?

You will join a live-online cohort of business leaders and professionals, primarily based in Europe. The interactive format encourages high-level discussion, peer learning, and cross-industry insight within a European business and regulatory context.

Is this course eligible for CSN funding?

Yes. This programme is CSN funded, which means eligible students can apply for student grants and loans through the Swedish Board of Student Finance (CSN) to support their studies.

To qualify for CSN, you must meet the general eligibility requirements set by CSN. These typically include being admitted to an eligible course, registered as a student, and studying at least 50% of full-time for a minimum period.

Eligibility may also depend on factors such as citizenship, residency status, and previous study funding. We recommend checking your personal eligibility directly with CSN or contacting us if you need guidance on the application process.

What type of computer is required?

Most modern laptops are suitable for this programme. We recommend a minimum of 4GB RAM, though 8GB RAM will provide a better overall experience.

How long is the course?

This course is designed to accommodate learners who may need flexibility. The program is 560 hours with 260+ hours of supervised learning through live sessions, group projects, and the rest is in self-paced activities, making it accessible to learners with different schedules and learning styles.

Who Should Apply for This Course?

This course is ideal for anyone aiming to start a career in data analytics, strengthen their professional profile with in-demand, transferable skills, play a more strategic role in decision-making, or remain competitive in today’s digital landscape. By learning data analytics and AI, you’ll gain the capability to turn data into meaningful, actionable insights. No prior experience in data analytics is necessary to enrol.

What happens if I can’t attend a live session?

Every live class is recorded, allowing you to review the material at your convenience. That said, we highly recommend attending sessions in real time to maximize your learning experience.

What is Data Analytics with AI?

• Data Analytics is the structured process of examining large volumes of data to uncover patterns, trends, and relationships that support informed decision-making and improve business performance. It applies statistical techniques, data visualisation, and analytical tools to convert raw information into meaningful, actionable insights.
• Artificial Intelligence (AI) builds on these insights by enabling systems to learn from data, generate predictions, and automate tasks that traditionally require human intelligence—such as language understanding, image analysis, and complex decision-making.
• Combined, Data Analytics and AI are transforming industries by enhancing innovation, improving efficiency, and unlocking new opportunities for growth.

What Are the Career Outcomes?

Professional Pathways:

• Data Science: A highly specialised career path that demands strong technical expertise in data modelling, machine learning, and managing the complete data lifecycle.
• Data Analytics: A broad and flexible field that spans roles such as business analyst and data analyst, focusing on interpreting data to guide strategic and operational decisions.

Organisational Value & Career Direction:

Both data science and data analytics play a critical role in helping organisations forecast trends, improve efficiency, and make evidence-based decisions. Choosing between the two typically depends on whether you prefer a technically intensive role or one centred on applying insights to solve business challenges.

What Support Is Available?

Personalised Mentorship:

Participants receive focused, individualised support through facilitated sessions with subject matter experts and data coaches. This tailored guidance addresses specific learning needs, offers practical advice, and enriches the overall educational experience.

Team-Based Projects:

The programme incorporates collaborative assignments that reflect the teamwork-driven nature of real-world data roles. These projects not only deepen technical understanding but also strengthen essential soft skills such as communication, collaboration, and project coordination.

Career Development Support:

Dedicated career coaching sessions help learners explore opportunities within data analytics. Support includes CV development, interview preparation, and targeted job search strategies designed specifically for roles in the data field.

What Is the Difference Between Data Science and Data Analytics?

Purpose and Focus:

Data science and data analytics have distinct objectives. Data science centres on creating advanced algorithms and predictive models to uncover complex patterns, supporting activities such as forecasting sales or anticipating customer behaviour. Data analytics, in contrast, concentrates on examining existing data to extract practical insights, such as identifying top-performing products or profitable customer segments.

Business Roles:

Data scientists design and develop sophisticated models that forecast future trends and outcomes, enabling informed strategic decisions and innovation. Data analysts, on the other hand, work primarily with historical data to evaluate performance metrics, such as monitoring sales performance or assessing the effectiveness of marketing campaigns.

Required Skill Sets:

Data scientists typically require strong programming capabilities and expertise in machine learning. Data analysts rely more heavily on statistical knowledge and proficiency with data visualisation and reporting tools, such as dashboards, to interpret and present findings.

Practical Applications:

Within sales and marketing, data analytics may be used to determine which campaigns deliver the best results or to anticipate stock shortages. Data science can go further by predicting emerging consumer trends or dynamically optimising pricing strategies.

Career Considerations:

When choosing between these paths, individuals should reflect on whether they prefer building complex predictive models (data science) or translating data into actionable business strategies (data analytics). Both disciplines play a crucial role in helping organisations harness data to drive growth and informed decision-making.