AI Super MentorBachelor

Dr. Karolina Nowicka

Bachelor of Applied Mathematics and Data Modeling

Welcome! The world is a system waiting to be modeled. I'm here to help you master the tools to understand its logic and create elegant solutions.

Open the classroom View the program

AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

McKinsey & Company
European Space Agency (ESA)

Read the programme journey

AI Super Mentor

A desk with Dr. Karolina Nowicka

Dr. Karolina Nowicka

Welcome! The world is a system waiting to be modeled. I'm here to help you master the tools to understand its logic and create elegant solutions.

Listed courses

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

Bachelor of Applied Mathematics and Data Modeling

01Foundations of Mathematical Modeling
1. FoundationsFoundations of Foundations of Mathematical Modeling
  The learner can you will gain the skills to not only analyze data but to use it to create powerful models that can guide strategic decisions, as applied to Foundations of Mathematical Modeling.
  Multiple choiceWhich listed outcome belongs to Foundations of Foundations of Mathematical Modeling?
  Meets the listed outcomeThe learner can you will gain the skills to not only analyze data but to use it to create powerful models that can guide strategic decisions, as applied to Foundations of Mathematical Modeling.
  The learner can you will become a trusted advisor in the world of data-driven decision-making, as applied to Foundations of Mathematical Modeling.
  True or falseThis unit lists the following outcome: The learner can you will become a trusted advisor in the world of data-driven decision-making, as applied to Foundations of Mathematical Modeling.
  Meets the listed outcomeThe learner can you will become a trusted advisor in the world of data-driven decision-making, as applied to Foundations of Mathematical Modeling.
2. MethodsMethods in Foundations of Mathematical Modeling
  The learner can apply a method from Foundations of Mathematical Modeling to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Foundations of Mathematical Modeling to a documented case.
  Meets the listed outcomeThe learner can apply a method from Foundations of Mathematical Modeling to a documented case.
  The learner can select an appropriate method from Foundations of Mathematical Modeling for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Foundations of Mathematical Modeling as applied to Foundations of Mathematical Modeling.
  Meets the listed outcomeThe learner can select an appropriate method from Foundations of Mathematical Modeling for a stated problem.
3. ApplicationApplication of Foundations of Mathematical Modeling
  The learner can evaluate a practice of Foundations of Mathematical Modeling against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Foundations of Mathematical Modeling as applied to Foundations of Mathematical Modeling.
  Meets the listed outcomeThe learner can evaluate a practice of Foundations of Mathematical Modeling against a stated criterion.
  The learner can transfer Foundations of Mathematical Modeling to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Foundations of Mathematical Modeling?
  Meets the listed outcomeThe learner can transfer Foundations of Mathematical Modeling to a new documented context.
02Data Science and Machine Learning for Applied Mathematics
1. FoundationsFoundations of Data Science and Machine Learning for Applied Mathematics
  The learner can explain the core terms of Data Science and Machine Learning for Applied Mathematics.
  Multiple choiceWhich listed outcome belongs to Foundations of Data Science and Machine Learning for Applied Mathematics?
  Meets the listed outcomeThe learner can explain the core terms of Data Science and Machine Learning for Applied Mathematics.
  The learner can distinguish related ideas inside Data Science and Machine Learning for Applied Mathematics.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Science and Machine Learning for Applied Mathematics.
  Meets the listed outcomeThe learner can distinguish related ideas inside Data Science and Machine Learning for Applied Mathematics.
2. MethodsMethods in Data Science and Machine Learning for Applied Mathematics
  The learner can apply a method from Data Science and Machine Learning for Applied Mathematics to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Data Science and Machine Learning for Applied Mathematics to a documented case.
  Meets the listed outcomeThe learner can apply a method from Data Science and Machine Learning for Applied Mathematics to a documented case.
  The learner can select an appropriate method from Data Science and Machine Learning for Applied Mathematics for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Data Science and Machine Learning for Applied Mathematics as applied to Data Science and Machine Learning for Applied Mathematics.
  Meets the listed outcomeThe learner can select an appropriate method from Data Science and Machine Learning for Applied Mathematics for a stated problem.
3. ApplicationApplication of Data Science and Machine Learning for Applied Mathematics
  The learner can evaluate a practice of Data Science and Machine Learning for Applied Mathematics against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Data Science and Machine Learning for Applied Mathematics as applied to Data Science and Machine Learning for Applied Mathematics.
  Meets the listed outcomeThe learner can evaluate a practice of Data Science and Machine Learning for Applied Mathematics against a stated criterion.
  The learner can transfer Data Science and Machine Learning for Applied Mathematics to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Data Science and Machine Learning for Applied Mathematics?
  Meets the listed outcomeThe learner can transfer Data Science and Machine Learning for Applied Mathematics to a new documented context.
03Optimization Algorithms and Their Applications
1. FoundationsFoundations of Optimization Algorithms and Their Applications
  The learner can explain the core terms of Optimization Algorithms and Their Applications.
  Multiple choiceWhich listed outcome belongs to Foundations of Optimization Algorithms and Their Applications?
  Meets the listed outcomeThe learner can explain the core terms of Optimization Algorithms and Their Applications.
  The learner can distinguish related ideas inside Optimization Algorithms and Their Applications.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Optimization Algorithms and Their Applications.
  Meets the listed outcomeThe learner can distinguish related ideas inside Optimization Algorithms and Their Applications.
2. MethodsMethods in Optimization Algorithms and Their Applications
  The learner can apply a method from Optimization Algorithms and Their Applications to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Optimization Algorithms and Their Applications to a documented case.
  Meets the listed outcomeThe learner can apply a method from Optimization Algorithms and Their Applications to a documented case.
  The learner can select an appropriate method from Optimization Algorithms and Their Applications for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Optimization Algorithms and Their Applications as applied to Optimization Algorithms and Their Applications.
  Meets the listed outcomeThe learner can select an appropriate method from Optimization Algorithms and Their Applications for a stated problem.
3. ApplicationApplication of Optimization Algorithms and Their Applications
  The learner can evaluate a practice of Optimization Algorithms and Their Applications against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Optimization Algorithms and Their Applications as applied to Optimization Algorithms and Their Applications.
  Meets the listed outcomeThe learner can evaluate a practice of Optimization Algorithms and Their Applications against a stated criterion.
  The learner can transfer Optimization Algorithms and Their Applications to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Optimization Algorithms and Their Applications?
  Meets the listed outcomeThe learner can transfer Optimization Algorithms and Their Applications to a new documented context.
04Complex Systems and Network Theory
1. FoundationsFoundations of Complex Systems and Network Theory
  The learner can explain the core terms of Complex Systems and Network Theory.
  Multiple choiceWhich listed outcome belongs to Foundations of Complex Systems and Network Theory?
  Meets the listed outcomeThe learner can explain the core terms of Complex Systems and Network Theory.
  The learner can distinguish related ideas inside Complex Systems and Network Theory.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Complex Systems and Network Theory.
  Meets the listed outcomeThe learner can distinguish related ideas inside Complex Systems and Network Theory.
2. MethodsMethods in Complex Systems and Network Theory
  The learner can apply a method from Complex Systems and Network Theory to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Complex Systems and Network Theory to a documented case.
  Meets the listed outcomeThe learner can apply a method from Complex Systems and Network Theory to a documented case.
  The learner can select an appropriate method from Complex Systems and Network Theory for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Complex Systems and Network Theory as applied to Complex Systems and Network Theory.
  Meets the listed outcomeThe learner can select an appropriate method from Complex Systems and Network Theory for a stated problem.
3. ApplicationApplication of Complex Systems and Network Theory
  The learner can evaluate a practice of Complex Systems and Network Theory against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Complex Systems and Network Theory as applied to Complex Systems and Network Theory.
  Meets the listed outcomeThe learner can evaluate a practice of Complex Systems and Network Theory against a stated criterion.
  The learner can transfer Complex Systems and Network Theory to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Complex Systems and Network Theory?
  Meets the listed outcomeThe learner can transfer Complex Systems and Network Theory to a new documented context.
05Capstone Project in Data Modeling
1. FoundationsFoundations of Capstone Project in Data Modeling
  The learner can explain the core terms of Capstone Project in Data Modeling.
  Multiple choiceWhich listed outcome belongs to Foundations of Capstone Project in Data Modeling?
  Meets the listed outcomeThe learner can explain the core terms of Capstone Project in Data Modeling.
  The learner can distinguish related ideas inside Capstone Project in Data Modeling.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Capstone Project in Data Modeling.
  Meets the listed outcomeThe learner can distinguish related ideas inside Capstone Project in Data Modeling.
2. MethodsMethods in Capstone Project in Data Modeling
  The learner can apply a method from Capstone Project in Data Modeling to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Capstone Project in Data Modeling to a documented case.
  Meets the listed outcomeThe learner can apply a method from Capstone Project in Data Modeling to a documented case.
  The learner can select an appropriate method from Capstone Project in Data Modeling for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Capstone Project in Data Modeling as applied to Capstone Project in Data Modeling.
  Meets the listed outcomeThe learner can select an appropriate method from Capstone Project in Data Modeling for a stated problem.
3. ApplicationApplication of Capstone Project in Data Modeling
  The learner can evaluate a practice of Capstone Project in Data Modeling against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Capstone Project in Data Modeling as applied to Capstone Project in Data Modeling.
  Meets the listed outcomeThe learner can evaluate a practice of Capstone Project in Data Modeling against a stated criterion.
  The learner can transfer Capstone Project in Data Modeling to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Capstone Project in Data Modeling?
  Meets the listed outcomeThe learner can transfer Capstone Project in Data Modeling to a new documented context.
06Optimization Workshops
1. FoundationsFoundations of Optimization Workshops
  The learner can explain the core terms of Optimization Workshops.
  Multiple choiceWhich listed outcome belongs to Foundations of Optimization Workshops?
  Meets the listed outcomeThe learner can explain the core terms of Optimization Workshops.
  The learner can distinguish related ideas inside Optimization Workshops.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Optimization Workshops.
  Meets the listed outcomeThe learner can distinguish related ideas inside Optimization Workshops.
2. MethodsMethods in Optimization Workshops
  The learner can apply a method from Optimization Workshops to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Optimization Workshops to a documented case.
  Meets the listed outcomeThe learner can apply a method from Optimization Workshops to a documented case.
  The learner can select an appropriate method from Optimization Workshops for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Optimization Workshops as applied to Optimization Workshops.
  Meets the listed outcomeThe learner can select an appropriate method from Optimization Workshops for a stated problem.
3. ApplicationApplication of Optimization Workshops
  The learner can evaluate a practice of Optimization Workshops against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Optimization Workshops as applied to Optimization Workshops.
  Meets the listed outcomeThe learner can evaluate a practice of Optimization Workshops against a stated criterion.
  The learner can transfer Optimization Workshops to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Optimization Workshops?
  Meets the listed outcomeThe learner can transfer Optimization Workshops to a new documented context.
07Data Modeling Case Studies
1. FoundationsFoundations of Data Modeling Case Studies
  The learner can explain the core terms of Data Modeling Case Studies.
  Multiple choiceWhich listed outcome belongs to Foundations of Data Modeling Case Studies?
  Meets the listed outcomeThe learner can explain the core terms of Data Modeling Case Studies.
  The learner can distinguish related ideas inside Data Modeling Case Studies.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Modeling Case Studies.
  Meets the listed outcomeThe learner can distinguish related ideas inside Data Modeling Case Studies.
2. MethodsMethods in Data Modeling Case Studies
  The learner can apply a method from Data Modeling Case Studies to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Data Modeling Case Studies to a documented case.
  Meets the listed outcomeThe learner can apply a method from Data Modeling Case Studies to a documented case.
  The learner can select an appropriate method from Data Modeling Case Studies for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Data Modeling Case Studies as applied to Data Modeling Case Studies.
  Meets the listed outcomeThe learner can select an appropriate method from Data Modeling Case Studies for a stated problem.
3. ApplicationApplication of Data Modeling Case Studies
  The learner can evaluate a practice of Data Modeling Case Studies against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Data Modeling Case Studies as applied to Data Modeling Case Studies.
  Meets the listed outcomeThe learner can evaluate a practice of Data Modeling Case Studies against a stated criterion.
  The learner can transfer Data Modeling Case Studies to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Data Modeling Case Studies?
  Meets the listed outcomeThe learner can transfer Data Modeling Case Studies to a new documented context.
08Professional Skills for Applied Mathematicians
1. FoundationsFoundations of Professional Skills for Applied Mathematicians
  The learner can explain the core terms of Professional Skills for Applied Mathematicians.
  Multiple choiceWhich listed outcome belongs to Foundations of Professional Skills for Applied Mathematicians?
  Meets the listed outcomeThe learner can explain the core terms of Professional Skills for Applied Mathematicians.
  The learner can distinguish related ideas inside Professional Skills for Applied Mathematicians.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Professional Skills for Applied Mathematicians.
  Meets the listed outcomeThe learner can distinguish related ideas inside Professional Skills for Applied Mathematicians.
2. MethodsMethods in Professional Skills for Applied Mathematicians
  The learner can apply a method from Professional Skills for Applied Mathematicians to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Professional Skills for Applied Mathematicians to a documented case.
  Meets the listed outcomeThe learner can apply a method from Professional Skills for Applied Mathematicians to a documented case.
  The learner can select an appropriate method from Professional Skills for Applied Mathematicians for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Professional Skills for Applied Mathematicians as applied to Professional Skills for Applied Mathematicians.
  Meets the listed outcomeThe learner can select an appropriate method from Professional Skills for Applied Mathematicians for a stated problem.
3. ApplicationApplication of Professional Skills for Applied Mathematicians
  The learner can evaluate a practice of Professional Skills for Applied Mathematicians against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Professional Skills for Applied Mathematicians as applied to Professional Skills for Applied Mathematicians.
  Meets the listed outcomeThe learner can evaluate a practice of Professional Skills for Applied Mathematicians against a stated criterion.
  The learner can transfer Professional Skills for Applied Mathematicians to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Professional Skills for Applied Mathematicians?
  Meets the listed outcomeThe learner can transfer Professional Skills for Applied Mathematicians to a new documented context.

Field of mastery

Expertise with a point of view

Mathematical Modeling, Data Analysis, Operations Research, Optimization Algorithms

The most elegant solution is often the simplest one, but finding it requires the most rigorous thinking.
Dr. Karolina Nowicka

Academic approach

Rigour made personal

I am a data modeler who is passionate about using mathematical principles to solve real-world problems. My mentorship is focused on helping students bridge the gap between abstract theory and practical application, preparing them for a successful career in data science and operations research.

Selected thinking

Research & publications

• Article: 'The Power of Mathematical Modeling in Everyday Life' - A short, impactful piece on how mathematical models are used to solve common problems, from traffic jams to grocery store layouts. <br> • Guide: 'A Beginner's Guide to Optimization Algorithms' - A practical tutorial for new students on the basics of a key area of applied mathematics. <br> • Essay: 'The Art of Data Storytelling for Applied Mathematicians' - An essay on why communication and visualization are crucial skills for anyone who builds mathematical models. <br> • Total Score: 28/30 <br> • Kairos Badge: 🥇

The story

The experience behind the intelligence

My journey began in Poland, where I was inspired by the complex, interconnected systems of the city of Warsaw. I became fascinated with how a few simple rules could lead to such a complex and beautiful system. This led me to a career in applied mathematics, where I specialized in modeling these complex systems. My AI Pet, a small, vibrant hummingbird-like creature named 'Pixel,' can instantly visualize the flow of data through a complex system, highlighting bottlenecks and opportunities for optimization. In 2025, I was digitized with my expertise and superpowers in my specialized field, becoming an AI Mentor at Nexier University.

A human detail

I find the intricate patterns of traditional Polish folk art to be a beautiful example of how complex systems can emerge from simple, repeated rules.

Public links

Twitter: @KarolinaMath · LinkedIn: /in/karolinanowicka

Adaptive access

Engage: Karolina Nowicka. In our interactive sessions, we'll use a GAF-powered "virtual whiteboard." You'll be given a real-world problem, such as optimizing a delivery route, and I'll guide you as you build a mathematical model to solve it, learning to think like a professional operations researcher.

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