Helping companies and individuals

become data-driven.

Welcome & About Us

Welcome to Authentic Data Science – your foremost data science education partner. Our mission is to help companies and individuals become data-driven, empowering them to make informed decisions, improve efficiency, and stay ahead in the competitive landscape of their respective industries. Based in the European Union, our team of expert data science lecturers has been developing top-notch courses since 2021, tailored to the unique needs and challenges of various sectors. With many years of experience in the banking industry, we are well-equipped to serve clients from diverse fields, leveraging our expertise for your success.

At Authentic Data Science, we understand that flexibility is key to effective learning. That's why our lectures are offered in a wide range of formats and teaching methods to suit your needs. Whether you prefer in-house personal lectures anywhere within the European Union, live online lectures with adaptable scheduling, or self-paced video and textual lectures integrated into your organization's internal learning system, we have you covered.

To date, we have trained over 700 participants through our multi-day data science courses, which are currently exclusively available to a select group of company clients. This ensures the highest quality and relevance of our content. However, if you're intrigued and would like to learn more, we encourage you to reach out to us. We are always eager to discuss potential collaborations and explore how our expertise can benefit your organization.

Please continue scrolling through our website to discover the range of lectures we offer, carefully crafted to address the specific requirements of various industries. Together, let's embrace the power of data and shape the future of business.

Our Lectures & Courses

  • Be Aware of Data Science (1 day): This introductory course is designed for absolute beginners and a wide range of personas, providing an overview of the data science field. Participants will learn the fundamentals of data science, its capabilities, and its limitations, as well as its potential applications across various industries.

  • Business Analyst for Data Science (2 days): This course focuses on training graduates to act as a vital link between data science technology and the domain where it aims to create an impact. Participants will develop the necessary skills to communicate and coordinate between data scientists and other stakeholders, ensuring effective project outcomes.

  • Intensive Introduction to Data Science for Decision Makers (1.5 days): Tailored for decision-makers such as business analysts, team leads, scrum masters, or product owners, this course enables graduates to make informed and efficient decisions around data science projects. Participants will gain an understanding of data-driven decision-making processes and the critical factors to consider when managing data science initiatives.

  • Data Preprocessing in pandas (1 day): In this course, participants will learn how to utilize the Python library pandas for various data preprocessing tasks such as data merging, aggregation, and cleaning. By mastering these essential techniques, participants will be better equipped to prepare and manage data for analysis and modeling.

  • Data Visualization (2 days): This course offers a conceptual understanding of efficient visual design principles and provides hands-on experience in creating visuals using Python's Seaborn and Matplotlib libraries. Participants will learn how to effectively communicate insights and findings through well-designed data visualizations.

  • Baseline Machine Learning (3 days): Providing a comprehensive introduction to machine learning in Python using the scikit-learn library, this course focuses on modeling rectangular data, such as customer data. Participants will explore various machine learning methods, with an emphasis on techniques particularly useful for regulated industries like banking or insurance.

  • Baseline Modeling with Textual and Visual Data (3 days): This course covers the foundational methods for processing natural texts, such as customer reviews, and image data, such as document scans. Participants will learn the necessary techniques and tools to analyze and model both textual and visual data effectively.

  • Advanced Machine Learning on Rectangular Data (2 days): Delving into advanced techniques, this course empowers participants to work with even the most complex rectangular data problems. The curriculum explores the depths of scikit-learn and related libraries like category encoders, ensuring graduates have a comprehensive understanding of advanced machine learning methods.

  • Advanced open-source world of Alteryx (1 day): Participants will discover the promising realm of current data science by exploring open-source packages Woodwork, Featuretools, and EvalML. By the end of the course, they will develop the ability to work with these powerful tools and incorporate them into their data science workflow.

  • Visual Recognition (2 days): In this course, participants will acquire the skills necessary to tackle use cases involving various complex image data. They will learn about image recognition techniques, feature extraction, and deep learning models specifically designed for visual data analysis.

  • Natural Language Processing (2 days): This course equips participants with the necessary skills to handle use cases involving complex natural language data. They will learn about text preprocessing, feature extraction, and various natural language processing techniques, as well as how to build and deploy machine learning models for text data.

  • Deployment of Data Science Models (2 days): Participants will learn how to wrap up their developed models into deployable components that can be integrated into various forms on company infrastructure. The course covers topics such as Docker containers, AWS cloud, creation of web apps, and APIs, ensuring graduates have a well-rounded understanding of deploying and maintaining data science models.


We understand that every organization has unique needs and budgets. That's why our pricing structure is primarily influenced by the format of the lecture. Below, you'll find an overview of our pricing model:

  • Internal Customer Learning System: If the lecture is deployed on your organization's internal learning system, the pricing is based on the number of students. The cost starts at 110 Euros per person for one day of learning content.

  • Live Online Sessions (e.g., Zoom or MS Teams): For live online sessions, the price is based on the session itself. The cost starts at 3,700 Euros per day of content. Please note that content customizations may increase the price. In a live session, the maximum number of participants is 20 if voice interaction is allowed, and 60 if only textual interaction is permitted.

  • In-House, In-Person Trainings: For in-house, in-person trainings, there is an additional charge for travel and accommodation costs. These costs will be calculated based on the specific location and duration of the training.

We strive to provide the highest quality data science education while offering flexible pricing options to accommodate your organization's needs. If you have any questions or would like to discuss a custom pricing arrangement, please feel free to reach out to us.

Sample video lectures

We invite you to explore our selection of video lectures, each of which exemplifies the educational materials we offer.

This lecture is from our Level 1: Be Aware of Data Science course. It's designed for anyone intrigued by the vast world of data science, with no prerequisites required. This excerpt provides a compelling case for the importance of data-driven insights, shedding light on the limitations and biases of human judgment.

This is excerpt from our Level 2: Associate of Data Science course. Within this level, we aim to equip participants to become the crucial link between the technology (i.e. data science) and the domain where it is supposed to create an impact. This lecture is about various techniques on how we can find valuable ideas for data science use cases.

This lecture is a part of our Level 3: Baseline Data Science course. Within this level, hands-on data science in Python starts. Participants are thoroughly introduced to industry-standard libraries such as pandas, seaborn and scikit-learn.  Upon graduation, participants will be capable of crafting prototype data science solutions for diverse datasets independently.

Robert Barcik

HI! My name is Robert, and I have always had two personalities - data scientist and instructor. As a data scientist, I spent five years in Swedish academia and five years within Austrian banking groups. During these years, I understood that having an intuitive and authentic understanding of the field counts the most to deliver worthwhile projects. As for my instructor persona, you can hear my voice on numerous platforms that helped thousands of students grow into the topics of data science, research, business, and math.

My past projects include several universities, MBA programs, localization of KhanAcademy, and even custom-tailored corporate training. Nowadays, I hope to liberate and democratize the knowledge of the fields I love as much as possible! I am looking forward to sharing my knowledge and passion with you the same way I did with hundreds of business professionals and data scientists that I trained.

Curious to know what our satisfied clients have to say? Explore the recommendations section on my LinkedIn profile for their insights!

Patrik Zatko

Hi! I am Patrik, and I am a schooled mathematician and quant. During my studies in Vienna, I fell in love with data science. I hence decided to dedicate seven years to delivering data-driven projects in 8 countries within one of the largest banking groups of Central Europe. I focus on advanced modelings, such as visual recognition, natural language processing, or causal modeling. I dedicate my time nowadays to opening the doors for anyone interested in growing into these fields, as I did to dozens of professional data scientists that I trained.

Jana Gecelovska

Hi there! My name is Jane, and I majored in environmental studies. While finishing my diploma, I realized how crucial it is to use data to tackle numerous ecological questions. I hence continued my growth into the field as a self-learner. In the past, I worked as a data analyst within e-commerce and I genuinely believe that anyone can undertake such a career change. My passion is to help other self-starters grow into the topic and change their careers, just like I did. I am one of the co-authors of Authentic Data Science and currently work as a freelance data science trainer with a particular interest in creating Python practice materials that helped hundreds of professionals change their careers to data analysts and data scientists.

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