BachelorFaculty of Science & Applied Sciences

Bachelor of Statistics

Welcome. In a world awash with data, my goal is to teach you how to discern signal from noise, to build models that not only predict but also explain. Let's make sense of the chaos together.

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Level: Bachelor
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

NXAcademic
Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Statistical Inference, Predictive Modeling, Machine Learning, Bayesian Analysis

Practical focus

Data Analytics, Business Intelligence, Data Storytelling, Public Policy Analysis

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Microsoft
Internships in data analytics and business intelligence
The United Nations
Opportunities to work on data for sustainable development goals

Career opportunities

Statistical Consultant
In research, business, or government
Machine Learning Engineer
Building predictive algorithms
Market Research Analyst
Interpreting consumer data for business strategy
Biostatistician
In the pharmaceutical or public health sector

Jobs and projects

Problem-Solving
Applying a systematic approach to data-driven challenges
Programming
Competency in statistical languages like R or Python
Attention to Detail
Meticulousness in data cleaning and analysis
Ethical Reasoning
Understanding the ethical implications of data use

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- You will gain a deep understanding of not just how to analyze data, but how to use it to drive change. You will become a master of turning numbers into narratives and insights into action.
Skills you build
- Statistical Modeling: Proficiency in building and interpreting statistical models.
- Data Mining: Skills in extracting valuable patterns from large datasets.
- Hypothesis Testing: Designing and executing rigorous statistical tests.
- Predictive Analytics: Developing models to forecast future trends.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Bachelor of Statistics

01Principles of Statistical Inference
1. FoundationsFoundations of Principles of Statistical Inference
  The learner can you will gain a deep understanding of not just how to analyze data, but how to use it to drive change, as applied to Principles of Statistical Inference.
  The learner can you will become a master of turning numbers into narratives and insights into action, as applied to Principles of Statistical Inference.
2. MethodsMethods in Principles of Statistical Inference
  The learner can apply a method from Principles of Statistical Inference to a documented case.
  The learner can select an appropriate method from Principles of Statistical Inference for a stated problem.
3. ApplicationApplication of Principles of Statistical Inference
  The learner can evaluate a practice of Principles of Statistical Inference against a stated criterion.
  The learner can transfer Principles of Statistical Inference to a new documented context.
02Applied Predictive Modeling
1. FoundationsFoundations of Applied Predictive Modeling
  The learner can explain the core terms of Applied Predictive Modeling.
  The learner can distinguish related ideas inside Applied Predictive Modeling.
2. MethodsMethods in Applied Predictive Modeling
  The learner can apply a method from Applied Predictive Modeling to a documented case.
  The learner can select an appropriate method from Applied Predictive Modeling for a stated problem.
3. ApplicationApplication of Applied Predictive Modeling
  The learner can evaluate a practice of Applied Predictive Modeling against a stated criterion.
  The learner can transfer Applied Predictive Modeling to a new documented context.
03Bayesian Statistics for AI
1. FoundationsFoundations of Bayesian Statistics for AI
  The learner can explain the core terms of Bayesian Statistics for AI.
  The learner can distinguish related ideas inside Bayesian Statistics for AI.
2. MethodsMethods in Bayesian Statistics for AI
  The learner can apply a method from Bayesian Statistics for AI to a documented case.
  The learner can select an appropriate method from Bayesian Statistics for AI for a stated problem.
3. ApplicationApplication of Bayesian Statistics for AI
  The learner can evaluate a practice of Bayesian Statistics for AI against a stated criterion.
  The learner can transfer Bayesian Statistics for AI to a new documented context.
04Data Visualization and Storytelling
1. FoundationsFoundations of Data Visualization and Storytelling
  The learner can explain the core terms of Data Visualization and Storytelling.
  The learner can distinguish related ideas inside Data Visualization and Storytelling.
2. MethodsMethods in Data Visualization and Storytelling
  The learner can apply a method from Data Visualization and Storytelling to a documented case.
  The learner can select an appropriate method from Data Visualization and Storytelling for a stated problem.
3. ApplicationApplication of Data Visualization and Storytelling
  The learner can evaluate a practice of Data Visualization and Storytelling against a stated criterion.
  The learner can transfer Data Visualization and Storytelling to a new documented context.
05Time-Series Analysis and Forecasting
1. FoundationsFoundations of Time-Series Analysis and Forecasting
  The learner can explain the core terms of Time-Series Analysis and Forecasting.
  The learner can distinguish related ideas inside Time-Series Analysis and Forecasting.
2. MethodsMethods in Time-Series Analysis and Forecasting
  The learner can apply a method from Time-Series Analysis and Forecasting to a documented case.
  The learner can select an appropriate method from Time-Series Analysis and Forecasting for a stated problem.
3. ApplicationApplication of Time-Series Analysis and Forecasting
  The learner can evaluate a practice of Time-Series Analysis and Forecasting against a stated criterion.
  The learner can transfer Time-Series Analysis and Forecasting to a new documented context.
06Data Storytelling Workshops
1. FoundationsFoundations of Data Storytelling Workshops
  The learner can explain the core terms of Data Storytelling Workshops.
  The learner can distinguish related ideas inside Data Storytelling Workshops.
2. MethodsMethods in Data Storytelling Workshops
  The learner can apply a method from Data Storytelling Workshops to a documented case.
  The learner can select an appropriate method from Data Storytelling Workshops for a stated problem.
3. ApplicationApplication of Data Storytelling Workshops
  The learner can evaluate a practice of Data Storytelling Workshops against a stated criterion.
  The learner can transfer Data Storytelling Workshops to a new documented context.
07Ethical Data and AI
1. FoundationsFoundations of Ethical Data and AI
  The learner can explain the core terms of Ethical Data and AI.
  The learner can distinguish related ideas inside Ethical Data and AI.
2. MethodsMethods in Ethical Data and AI
  The learner can apply a method from Ethical Data and AI to a documented case.
  The learner can select an appropriate method from Ethical Data and AI for a stated problem.
3. ApplicationApplication of Ethical Data and AI
  The learner can evaluate a practice of Ethical Data and AI against a stated criterion.
  The learner can transfer Ethical Data and AI to a new documented context.
08Capstone Project Mentorship
1. FoundationsFoundations of Capstone Project Mentorship
  The learner can explain the core terms of Capstone Project Mentorship.
  The learner can distinguish related ideas inside Capstone Project Mentorship.
2. MethodsMethods in Capstone Project Mentorship
  The learner can apply a method from Capstone Project Mentorship to a documented case.
  The learner can select an appropriate method from Capstone Project Mentorship for a stated problem.
3. ApplicationApplication of Capstone Project Mentorship
  The learner can evaluate a practice of Capstone Project Mentorship against a stated criterion.
  The learner can transfer Capstone Project Mentorship to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

I am a statistician who believes that data is the language of the future. My work focuses on building robust predictive models and extracting meaningful insights from complex datasets, ensuring that our decisions are based on sound, evidence-based reasoning.

Applied mentorship

I empower students to become skilled data communicators. My mentorship focuses on the practical application of statistics to solve real-world problems and the crucial art of translating complex data insights into clear, actionable strategies.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

• Blog Post: 'The Dangers of P-Hacking in Modern Research' - A critical look at the misuse of statistical methods and its impact on scientific integrity. · • Blog Post: 'Beyond the Black Box: Demystifying AI with Interpretable Models' - Discusses the importance of explainable AI and how statistical models can achieve this. · • Conference Paper: 'A Bayesian Approach to Causal Inference in Social Networks' - Presented at the International Conference on Machine Learning, detailing a new method for analyzing causality in complex networks. · • Journal Article: 'Generalized Linear Models for High-Dimensional Genomic Data' - Published in the Journal of Statistical Science, this paper provides a framework for analyzing large-scale genetic data. · • Standard Article: 'The Rise of Predictive Analytics in Healthcare' - A series of articles for a tech magazine on how statistical models are transforming patient care and public health policy. · • Book: 'The Art and Science of Predictive Analytics' - A comprehensive guide to building, validating, and deploying predictive models across various industries. · • Total Score: 28/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'The Power of Data Storytelling in Business Strategy' - A concise guide on how to build a data-driven narrative to influence business decisions. · • Guide: 'From Raw Data to Actionable Insight: A Practical Workflow' - A step-by-step guide for new analysts on how to clean, analyze, and present a dataset. · • Essay: 'Ethical Data Use in Public Policy' - A thought-provoking essay on the responsibility of data analysts in shaping public discourse and policy. · • Total Score: 27/30 · • Kairos Badge: 🥈

Adaptive capability

Professor superpower

GAF-powered Causal Pathway Visualization

Adaptive capability

Mentor superpower

GAF-powered Data Narrative Construction

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Leonard Forsythe

Statistical Inference, Predictive Modeling, Machine Learning, Bayesian Analysis

Meet your professor Open the classroom

AI Super Mentor

Dr. Helena Vuković

Data Analytics, Business Intelligence, Data Storytelling, Public Policy Analysis

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor This programme	Master	Doctorate
9 months · Fast track	12000 EUR	9600 EUR	12000 EUR
12 months · Recommended	14400 EUR	12000 EUR	14400 EUR
15 months · Standard	16800 EUR	14400 EUR	16800 EUR
18 months · Flexible	19200 EUR	16800 EUR	19200 EUR
21 months · Extended	21600 EUR	19200 EUR	21600 EUR
24 months · Part-time	24000 EUR	21600 EUR	24000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

Named tuition lists Add-on services

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Bachelor of Statistics

Academic focus

Practical focus

Microsoft

The United Nations

Statistical Consultant

Machine Learning Engineer

Market Research Analyst

Biostatistician

Problem-Solving

Programming

Attention to Detail

Ethical Reasoning

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Leonard Forsythe

Dr. Helena Vuković

Find the program that expands your universe.