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For over 10 years, I ́ve been deeply involved in DATA SCIENCE and EDUCATION.

 

My experience ranges from Swedish universities to Austrian banks, always emphasizing real, practical knowledge.

 

I ́ve taught thousands through university courses, MBA programs and projects like the KhanAcademy localization.

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Data Science Trainings

Transform your company by turning data into actionable insights, unlocking enhanced efficiency and competitiveness. Since our EU launch in 2021, our focus has been on shaping outcomes that elevate your performance. Our hands-on, practical approach to learning ensures that you can tackle real-world challenges head-on and drive measurable results for your organization.

 

We understand that every company is unique, which is why our experienced data scientists work closely with you to tailor our programs to your specific needs. We offer a range of options designed to seamlessly integrate with your team's workflow. For those in the banking sector, our expertise ensures you have solutions tailored for your specific hurdles. Join the ranks of the 800+ professionals who've harnessed the power of our courses.

 

Discover the potential our diverse course offerings can unveil for you.

Overview of courses, available formats and pricing (prices excl. VAT).

With self-paced format, availability of the course is presumed on premises of the customer (e.g. own LMS system). Customization and content mixing is available. Volume discounts are available. In case of onsite events, trainer travels from Bratislava, economy travel expenses shall be covered by customer, travel time billed at 40€/hour.

1. Level 1: Be Aware of Data Science 

  • Self-paced: 6 hours of video content and exercises, 30€/per student

  • Live online/onsite: 1 full training day, 1400€/per occasion, max 50 participants

2. Level 2: Associate of Data Science

  • Self-paced: 6 hours of video content and exercises, 30€/per student

  • Live online/onsite: 2 full training days, 2800€/per occasion, max 50 participants

3. Level 3: Baseline Data Science

  • Self-paced:  87 hours of video content and Python exercises, 450€/per student

  • Live online/onsite: 9 full training days, starting at 25 200€/per intake, max 30 participants

4. Level 4: Modeler of Data Science

  • Self-paced:  67 hours of video content and Python exercises, 450€/per student

  • Live online/onsite: 10 shorter training days, starting at 19 400€/per intake, max 20 participants

5. Data Science for Product Owners & Leaders

  • Live online/onsite: 1 full training day, 1400€/per occasion

  • Live online/onsite (longer version): 2 full training days, 2800€/per occasion, max 30 participants

1. Level 1: Be Aware of Data Science

What is this course about?

In today's data-driven world, understanding how to extract valuable information is essential. This introductory course demystifies data science, making it accessible to everyone. You'll gain an intuitive understanding of how data science models create value, even if you're a complete beginner.

For whom is this course?

Anyone interested in data science.

 

Are there some prerequisites to this course?

Just a motivation to learn new concepts. This course is really meant for anyone, even if you are an absolute beginner to the topic of data science.

 

What will I gain from this course?

  • Learn how data science turns data into valuable information.

  • Understand what cognitive biases are and how data science helps us fight them.

  • Get to know what spurious correlation is and how we can avoid it.

  • Learn how to conduct a data-driven business experiment that verifies whether a change creates a positive impact.

  • Realize how big data brings unpurposed data collections and how we need to address these. 

  • Discover who data scientists are and what it would take for you to become one.

  • Recognize how data science creates scientific models through experimentation and observation.

  • Learn about basic methods of data science such as descriptive statistics of correlation measures.

  • Obtain a strong intuition behind what a machine learning model does.

  • Discover why a data science model simplifies human decision-making.

 

Course Structure:

  • Defining Data Science: This course begins by exploring the multifaceted nature of data science. We will examine the reasons behind data's immense value, the goals of data science, and the potential biases that can influence data science models.

  • Disciplines of Data Science: We will explore the diverse disciplines that converge to form the field of data science. We'll differentiate between artificial intelligence and machine learning,  explore the role and skills of a data scientist, and shed light on the factors contributing to the complexity of data science applications.

  • Describing and Exploring Data: We will learn about descriptive and exploratory data analysis techniques and discover how they generate valuable insights. We will explore the concept of correlation, distinguish between true and spurious correlations, and understand how outliers and measures of spread can impact our understanding of data.

  • Inference and Predictive Models: We will examine the realm of inferential and predictive data science. We also explore alternative approaches to machine learning for creating predictive models and demonstrate how statistical inference can be used to assess the effectiveness of efforts like a new sales campaign.

2. Level 2: Associate of Data Science

What is this course about?

This course provides a comprehensive, non-technical overview of data science projects, from initial ideas to deploying models into the real-world settings.

For whom is this course?

  • Business analysts

  • Technical experts from related fields (databases, cloud engineering)

  • Initiators of data science projects

  • Anyone new to data science seeking a solid foundation

Are there some prerequisites to this course?

Basic familiarity with data science concepts (e.g., Level 1 course).

What will I gain from this course?

You'll gain the skills to become a valuable contributor to data science projects. You'll be able to:

  • Identify and prioritize promising project ideas

  • Formulate actionable hypotheses and solution plans

  • Help integrate projects into the organization's broader goals

Course Structure:

  • Project Fundamentals: Gain a solid understanding of the key distinctions between data science models, products and projects.

  • Project Lifecycle: Learn to brainstorm, evaluate and refine data science project ideas, transforming them into concrete, actionable hypotheses and comprehensive solution plans.

  • Teams, Workflows, and Organizations: Master the art of assembling high-performing data science teams, establishing efficient workflows and aligning projects with broader organizational goals.

  • Data Essentials: Explore how real-world phenomena are represented in data, understand primary data formats and discover the hardware and software tools that empower data science teams.

  • Exploratory Data Analysis (EDA): Master the fundamentals of data exploration and visualization, learning best practices for identifying patterns, avoiding pitfalls and creating impactful visualizations.

  • Feature Engineering and Predictive Modeling: Learn how to transform raw data into meaningful features for machine learning models, grasp the essential principles of predictive modeling and explore common model families.

  • Supervised and Unsupervised Learning: Dive into regression and classification, two common supervised learning tasks and discover the power of unsupervised learning techniques for uncovering hidden patterns in data.

  • Model Deployment: Learn the best practices for deploying your predictive models into real-world environments, ensuring their reliability, scalability and effectiveness.

3. Level 3: Baseline Data Science

What is this course about?

This hands-on course empowers you to apply essential data science techniques to structured data using Python. Whether you're new to programming or looking to expand your existing skills, you'll gain the ability to preprocess, explore, visualize and build basic machine learning models.

For whom is this course?

  • Aspiring junior data analysts or data scientists

  • Professionals working with data (e.g., databases, reports) who want to expand their skill set

  • Anyone looking to learn Python for data science applications

  • Managers of data science teams who want a deeper understanding of the work

Are there some prerequisites to this course?

  • Conceptual knowledge of data science (e.g., completion of Level 2)

  • Basic hands-on experience working with data (e.g., creating reports in Excel or SQL)

  • High-school level mathematics

What will I gain from this course?

You'll be able to:

  • Perform advanced data analysis in Python, going beyond basic Excel capabilities.

  • Contribute to real-world data science projects as a junior analyst or scientist.

  • Build and evaluate basic machine learning models from structured data.

Course Structure:

  • Program kick-off: This session includes an hour-long introductory call designed to inform participants about the program structure and expectations. During this call, participants will have the opportunity to ask questions, clarify any doubts, and gain a clear understanding of the course objectives and content.

  • Intro to Python for Data Science: Three weeks of self-paced study with prepared video lectures and programming exercises. 

  • Wrangling and Visualising Data: Students will learn essential data preprocessing techniques using pandas, including data merging, aggregation, transformation, string operations and handling missing values. They will also explore various data visualization techniques, covering univariate visualization in pandas, storytelling and identifying misleading visuals, as well as bivariate and multivariate visualization using Matplotlib and Seaborn.

  • Preparation for Predictive Modelling: Students will be introduced to the basics of feature engineering, including numerical and categorical features, handling missing values and basics of datetime features. They will learn about feature selection techniques, filter and wrapper methods, and how to create and evaluate baseline models for both classification and regression tasks.

  • Supervised & Unsupervised Learning: This part of the course covers a range of machine learning techniques, beginning with linear methods like linear regression and regularized models, then moving to non-linear methods such as decision trees and random forests. Students will also learn about logistic regression and various unsupervised learning techniques, including clustering, dimensionality reduction and anomaly detection. Additionally, students will learn about neural networks, visual recognition and natural language processing, focusing on multilayer perceptrons, computer vision and text embeddings.

  • Capstone project: Participants will utilize all the skills and knowledge acquired throughout the course to analyze a provided dataset. Feedback is provided to participants individually on their submitted solution.

4. Level 4: Modeler of Data Science

What is this course about?

This advanced course takes you beyond the basics of machine learning. You'll delve into specialized techniques to handle complex scenarios, unusual datasets, and the nuances of model selection and evaluation.

For whom is this course?

Junior and professional data scientists looking to solidify their modeling expertise.

Are there some prerequisites to this course?

  • Junior-level knowledge of data science in Python (e.g., completion of Level 3)

  • Proficiency in Python, Pandas, Seaborn/Matplotlib, scikit-learn

  • Understanding of model validation, hyperparameter tuning, linear models, decision trees, unsupervised learning, neural networks, and basic image/text processing.

What will I gain from this course?

You'll be able to:

  • Take full ownership of predictive modeling projects.

  • Confidently select and apply the most suitable machine learning techniques for diverse challenges.

  • Address the data science problems that fall outside of standard approaches.

  • Expand your knowledge of advanced machine learning areas like visual recognition and natural language processing.

Course Structure:

This course blends self-paced learning with interactive live online sessions to provide a comprehensive and flexible learning experience.

  • Program Kick-Off: This session includes an hour-long introductory call designed to inform participants about the program structure and expectations. During this call, participants will have the opportunity to ask questions, clarify any doubts, and gain a clear understanding of the course objectives and content.

  • Advanced Processing of Rectangular Data (Self-Paced): In this self-paced segment of the course, you will explore advanced techniques for working with structured data. This part covers distance-based models, advanced feature engineering methods and effective strategies for handling imbalanced datasets.

  • Advanced Machine Learning on Rectangular Data: Gain in-depth knowledge of powerful ensemble methods like boosting and stacking, as well as advanced techniques for automating machine learning pipelines using scikit-learn.

  • Uplift Modeling & Model Interpretation: Explore uplift modeling, a specialized technique for estimating treatment effects using machine learning. Discover the growing importance of model interpretation, particularly in light of emerging regulations and learn essential techniques like partial dependency plots and Shapley values.

  • Woodwork, Featuretools & EvalML Libraries (Self-Paced): In this self-paced segment of the course, you will learn how to utilize specialized libraries designed for efficient handling of complex datasets.

  • Deep Learning in Keras (Self-Paced): In this self-paced segment of the course, you will build upon your foundational knowledge of neural networks and learn to create and train them using the Keras library.

  • Advanced Unsupervised Learning & Computer Vision (Self-Paced): In this self-paced segment of the course, you will explore the versatile world of autoencoders, a type of neural network used for unsupervised learning tasks. Additionally, you will learn about computer vision and how to train convolutional neural networks for image-based applications.

  • Natural Language Processing: Master techniques for working with textual data, including text embeddings and transfer learning and learn how to leverage pre-trained models for natural language processing tasks.

  • Time Series Problems: Gain expertise in addressing time-dependent data challenges with specialized machine learning libraries designed for time series analysis and forecasting.

  • Machine Learning Model Deployment (Concepts & Theory): Understand the strategies and considerations involved in deploying machine learning models, as well as the foundational concepts of Cloud infrastructure that will be used in the Hands-On practice session.

  • Machine Learning Model Deployment (Hands-On Practice): Get practical experience deploying models through a guided, end-to-end demo. You'll work with the command line, version control, model serialization, web app creation, Docker and scalable deployment on cloud infrastructure.

  • Exam: Showcase your understanding of the course material through a three-part exam comprising a theoretical quiz, a practical coding assignment related to model deployment, and a problem-solving task. You will have several opportunities to pass the exam successfully.

5. Data Science for Product Owners & Leaders

What is this course about?

This dynamic crash course equips product owners and leaders with the practical knowledge and skills needed to effectively navigate and lead data science initiatives. It provides a comprehensive overview of data science concepts, methodologies, and applications, specifically tailored to the needs of those overseeing and participating in data-driven projects.

For whom is this course?

  • Product owners responsible for data-driven products or features.

  • Leaders seeking to integrate data science into their organizations or teams.

  • Anyone involved in overseeing or participating in data science projects.

Are there some prerequisites to this course?

Just a motivation to learn new concepts. This course is really meant for anyone, even if you are an absolute beginner to the topic of data science.

What will I gain from this course?

  • A solid understanding of data science fundamentals, terminology, and key approaches.

  • Insights into fostering a data-driven organizational culture and the strategies for successful adoption of data science initiatives.

  • The ability to critically evaluate data, interpret visualizations, and identify patterns and potential biases.

  • Knowledge of both basic (descriptive & exploratory) and advanced (inferential & predictive) data science methods.

  • Practical techniques for generating valuable data science project ideas, formulating actionable hypotheses, and framing viable solutions.

  • Strategies for calculating the business value of data science projects, setting KPIs, and ensuring cost-effective implementation.

  • An understanding of the challenges and considerations involved in deploying and maintaining machine learning models in production.

  • An introduction to Natural Language Processing (NLP) and Visual Recognition (VR), their applications, and how they can be integrated into projects.

  • Examples of successful data science projects within the banking sector, relevant to your specific area of interest.

Course Structure:

  • Introduction to Data Science: Gain a broad overview of data science, covering essential concepts, terminology, the value of data and the roles involved in data science projects.

  • Data-Driven Organization & Culture: Learn how to cultivate a data-driven culture within your organization, develop adoption strategies, measure the impact of data science and prioritize data-driven decision-making.

  • Descriptive & Exploratory Approaches: Understand common pitfalls in data interpretation, master data storytelling and visualization techniques and learn to identify patterns in data while avoiding graphical distortions.

  • Inferential & Predictive Approaches: Explore supervised and unsupervised learning, different machine learning model families, model evaluation methods and the importance of focusing on business metrics in addition to technical ones.

  • Getting a Valuable Idea for a Data Science Project: Learn practical techniques for generating high-value project ideas, including utilizing exhaust data, formulating concrete hypotheses and decomposing complex problems.

  • Framing a Solution to be Implemented: Discover how to calculate the business value of data science projects, set effective KPIs, minimize costs, choose appropriate approaches and manage projects using methodologies like CRISP-DM.

  • Machine Learning Models in Production: Gain insights into the challenges and considerations involved in deploying machine learning models, including feedback loops, data validation and long-term maintenance of data science ecosystems.

  • Natural Language Processing and Visual Recognition: Explore common NLP and VR problems, transfer learning techniques, model intuition and the possibilities of combining both approaches in a project.

  • Real-Time Mindset: Learn to adopt a real-time mindset for data processing, moving beyond traditional batch processing approaches.

Let's get in touch

You may send your inquiries directly to my email or we can discuss them over a phone call.

+421 951 477 080

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