We assume basic programming in any language – knowledge of loops, conditions and basic data structures. Python is not required: the course starts by unifying the basics, so you pick it up during the first lessons.
The course is for anyone who wants to work with data using Python, SQL and modern tools used in practice – from economists and scientists to developers and future data analysts.
Preparing for junior positions in data analytics, business intelligence or data science and want to gain practical experience.
Developers, testers and other technical specialists who want to extend their skills with data work, reporting and the basics of machine learning.
Professionals from finance, controlling or marketing who want to move from Excel to Python, SQL and automated analyses.
Need to process research and experimental data efficiently, use statistics and create clear visualisations of results.
The course is taught online, so you can join from anywhere. Lessons combine explanation, demonstrations and practical exercises – explanation alternates with practice. Video recordings of all lessons are available.
Explanation of a new topic, live demonstrations in Python and short ongoing exercises – whether in an evening lesson or a weekend morning block.
Independent work on data tasks, practising procedures and room for questions – in the weekday format in the practical lesson, at weekends in the afternoon blocks.
Video recordings of the lessons are available for you to return to when revising or catching up on missed material.
Between lessons (or between weekends) you receive assignments for independent practice so you can try the material outside class as well.
During the course you have access to a shared messenger for questions, sharing solutions and communicating with the lecturer and other participants.
Every new topic has a practical part: first the principle and typical use, then practice on real data.
We offer the course in two formats – the specific days and times are always shown on the card of the chosen date.
The first block unifies the basics of Python and SQL and teaches you to work efficiently with tabular data using the Pandas library – from loading a dataset to aggregations and advanced SQL queries connected to Python.
The opening block unifies basic work with Python and SQL so that students share a common language for further work with data. They go through typical data structures, basic access to tables, reading and modifying data, and the most common differences between how the same tasks are solved in Python and in SQL.
Students master the pandas module as the main tool for manipulating tables in Python. The topic focuses on dataframes, series and other important data types, on filtering data and on basic statistics that help quickly understand the contents of a dataset.
The second pandas block builds on basic work with tables and extends it with aggregations and groupby operations. Students see how to summarise data clearly, create summary views and prepare tables for further analysis and visualisation.
The lesson introduces the basics of SQL and working with relational databases. Students learn to write basic SELECT queries, filter data with WHERE, sort and group results and use aggregate functions. They gain a solid foundation for the advanced SQL work in the next lesson.
The lesson deepens SQL work and shows how to connect it effectively with Python in analytical work. Students get to know window functions, JOINs and how to combine the power of the database and Python when processing larger datasets.
The second block guides you through data visualisation, the basics of statistics and exploratory analysis. You learn to read charts, detect anomalies, work with data quality and understand the role of ETL processes in the data ecosystem.
Students learn how to read data through charts and how to choose the right type of visualisation for the goal of the analysis. They go through Plotly and Seaborn, a basic overview of chart types and techniques for fitting more dimensions into a 2D view without losing readability.
This block introduces the basic statistical tools for data analysis. Students revise mean, standard deviation, variance and correlation and get to know the most important distributions, especially the normal and binomial distributions.
In this part students learn to use visualisation and statistics to understand data better. It includes finding anomalies, handling missing values, data validation and practical procedures for detecting problems in a dataset early.
A theory-oriented lesson explains the basic principles of ETL processes and their role in the data ecosystem. Students understand the concepts of OLTP and OLAP and get to know data warehouses and the typical data sources used in practice.
The final block connects hypothesis testing, predictive modelling and time-series work with the real practice of data teams. You get an introduction to machine learning and understand how data analysis works in operation.
The lesson explains the basics of hypothesis testing and the principles of A/B testing when evaluating changes to a product or service. Regression analysis follows, with a demonstration of how to get answers to practical questions from data based on measurable relationships.
Students get an introduction to predictive modelling and machine learning. They go through the difference between supervised and unsupervised learning, classification and regression, and basic methods for evaluating a model’s accuracy and its usability in practice.
The block focuses on analysing and modelling time series. Students get to know trend, seasonality, basic statistical models and how machine-learning methods can be used to predict developments over time.
The final lesson shows how data analysis works in real operation. It covers the differences between the data analyst, data scientist and data engineer roles, basic principles of documentation, task planning and monitoring, and communication with customers and stakeholders.
After completing the course you will be able to carry out a complete data analysis – from loading and cleaning data through visualisation and statistics to the basics of predictive modelling.
You learn to work with data efficiently using Python and the Pandas library, create dataframe structures and filter and aggregate data.
You master JOINs, window functions and connecting SQL databases with Python for efficient analytical work.
You learn to create clear charts and data visualisations with Plotly and Seaborn and to interpret analysis results correctly.
You understand basic statistical principles, working with distributions, correlations, regression analysis and A/B testing.
You get an introduction to machine learning, classification, regression and evaluating the performance of predictive models.
You learn to solve real data tasks, work with data quality, ETL processes and time series, and understand the roles in data teams.
In the course you will work with modern technologies used in data analytics, business intelligence and machine-learning projects in real business practice.
One of the most widely used languages for data analytics, AI and process automation.
The fundamental technology for working with databases and data warehouses, used across IT.
The standard Python library for processing and analysing tabular data.
A powerful library for numerical computation and efficient work with data arrays.
A modern tool for creating interactive charts and dashboards.
A visualisation library for statistical charts and exploratory data analysis.
One of the most widely used machine-learning libraries in Python.
The data analysts’ favourite environment for combining code, charts and documentation.
It doesn’t end with the course. Even after completing it, you keep plenty of benefits you can rely on:
An official Turing Institut certificate documenting your newly acquired skills.
Recordings of all lessons and study materials remain yours forever – come back to them whenever you need.
A Discord full of people like you, a newsletter with IT news and exclusive members-only events.
There is currently no scheduled date for this course. Write to us or give us a call — we will let you know as soon as a new date is announced, and we are happy to arrange a date that suits you.
We have submitted this course to the Czech Labour Office programme. Once it is approved, the state can cover up to 76.73 % of the course price for you. Join the waitlist and you will be among the first to know.
How the funding worksBasic programming in any language is enough – we expect knowledge of loops, conditions and basic data structures. Python is not required: the first lessons bring everyone to a common baseline.
No. The course starts by unifying the basics of Python and SQL and gradually guides you through the whole data-analysis process. It is suitable for beginners as well as developers who want to extend their skills with data work.
No. Practical exercises take place in Jupyter Notebook, and for most tasks an ordinary laptop with a browser is enough. More demanding computations are done in a cloud environment to which you get access.
It depends on the chosen format. In the weekday format classes take 2 × 2 hours a week; we recommend an additional 2–4 hours for assignments and self-study. The weekend format is 3 consecutive weekends (Saturday + Sunday, approx. 9:30–18:30), and between weekends we recommend 4–6 hours of home practice. You set your own pace within the course date – video recordings are also available.
The course is designed so that knowledge of advanced mathematics such as linear algebra or calculus is not necessary. Simple mathematics is all we need, and we also explain the statistics we use. Our lecturers are, however, well versed in mathematical and theoretical topics and are happy to answer questions.
These two courses sit side by side in the catalogue, but they teach different skills.
This course is about data analytics in practice. We guide you through the whole process: loading and cleaning data, writing SQL (including JOINs and window functions), working with Pandas, visualising in Plotly and Seaborn, and relying on statistics, A/B testing and exploratory analysis. We cover ETL, data quality, time series and what analysis looks like in a real team — the roles of analyst, data scientist and data engineer. Machine learning is included as an introduction: predictive modelling, classification, regression and model evaluation in Scikit-learn. The goal is to be able to extract an answer from data and communicate it clearly.
Machine Learning and Neural Networks goes deep into models. You build and train your own networks — from classic algorithms through convolutional networks for images, generative networks (GANs) and autoencoders to LSTMs and language processing. You work in TensorFlow / Keras. The goal is to understand how a model learns from data and to be able to evaluate and tune it — not to build a complete analytical workflow from SQL to chart.
The courses meet at predictions and the basics of ML. Here it is one lesson in the context of analytical practice; there it is the main topic for the whole course. Here you clean, aggregate and visualise data. In Machine Learning, data preparation is a means to training the network, not the goal.
A simple guide: if you want to work with business data, SQL, charts and statistics and understand what they mean — stay here. If you want to train neural networks yourself and go into deep learning — go for the Machine Learning and Neural Networks course. If you are interested in building applications on top of ready-made language models (agents, RAG, LangChain), that is a third path: AI Agent Developer. The courses build on each other, but none is a prerequisite for another.
A more detailed comparison of all AI courses by goal can be found in the article Which AI course should I choose?.