One bundle, two skills that belong together. You go through two courses that complement each other perfectly — you master complete data analysis with Python and the basics of artificial intelligence and at the same time master relational databases and SQL in depth.
Part 1
26 lessons · approx. 52 hours · online
The complete data-analysis process — from data cleaning through Pandas, SQL queries, visualisation and statistics to A/B testing and the basics of machine learning.
Part 2
6 lessons · approx. 12 hours · online
Table design, queries from basic to advanced, views, procedures and triggers, and performance tuning — everything you need to work with data in real applications.
Bundle advantage: you get both courses together for CZK 41,900 incl. VAT — cheaper than buying them separately — and you develop your data-analysis and SQL skills in parallel, so you can connect them right away when working with real data.
We assume basic programming in any language — knowledge of loops, conditions and basic data structures. Python is not required: the data-analysis part starts by unifying the basics, so you pick it up during the first lessons. For the relational databases and SQL part you need no previous experience.
The bundle is for anyone who wants to work with data to the full — from database design and SQL queries through analyses in Python to the basics of machine learning. Basic programming in any language is enough.
Preparing for junior positions in data analytics, business intelligence or data science and want to gain practical experience and a solid database foundation.
Developers, testers and other technical specialists who want to extend their skills with data work and reporting and master relational databases in depth.
Professionals from finance, controlling or marketing who want to move from Excel to Python, SQL and automated analyses — and get to the data themselves, without waiting for developers.
Need to process research and experimental data efficiently, use statistics and create clear visualisations of results.
Both courses are taught online with live lecturers, so you can join from anywhere. Lessons combine explanation, demonstrations and practical exercises, and video recordings of all lessons are available for you to return to at any time.
All lessons of both courses are available in the e-learning platform for unlimited playback.
You try out new skills right away on real data and specific real-world tasks.
Optional assignments and tests in the e-learning platform with feedback from lecturers.
A space for questions and sharing solutions plus individual consultations with lecturers.
We go through interview preparation with you and lend you specialist literature on the topic.
Every lesson is focused on immediate use in a real project.
Data analysis and SQL are taught in parallel — both parts of the bundle run side by side in an evening rhythm that fits well with work and your own preparation between lessons.
The first block guides you through complete data analysis — from Python, Pandas and SQL queries through visualisation, statistics and exploratory analysis to A/B testing, predictive modelling and the practice of data teams.
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.
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 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.
The second block guides you through relational databases and SQL — from table design through advanced queries and procedural SQL to performance and transactions. At the end, optional interview preparation awaits you.
Classification of database systems and their most common representatives, tables, data types, primary keys and basic data operations.
Linking tables and foreign keys, data normalisation, database structure design and queries over multiple tables.
Aggregating and grouping data, nested queries, views and materialised views.
Introduction to procedural programming — conditions, variables and procedures.
Functions, triggers and procedural SQL in practice.
Indexes, transactions, partitioning and database clusters.
Career counselling, a mock interview and a review of your CV and LinkedIn profile.
After completing the bundle you will master complete data analysis as well as working with relational databases – from table design and SQL queries through visualisations 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 design table structures with relationships, master queries from basic to advanced including JOINs and window functions, and learn to work with views, procedures and triggers.
You understand transactions and learn to tune database performance with indexes and partitioning — skills every team appreciates.
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 worksBecause in practice they meet every day. The data you analyse almost always lives in a relational database — and an analyst who can find, join and prepare it in SQL on their own is much more independent and faster. The data-analysis part teaches you to use SQL for analytical work (JOINs, window functions, connecting to Python); the relational-databases part goes into depth — from table design through procedures and triggers to performance optimisation. On top of that, the bundle is cheaper than buying both courses separately.
Basic programming in any language is enough — we expect knowledge of loops, conditions and basic data structures. Python is not required: the first data-analysis lessons bring everyone to a common baseline. For the relational databases and SQL part you need no previous experience.
No. The data-analysis part starts by unifying the basics of Python and SQL and gradually guides you through the whole data-analysis process. The relational-databases part is designed for complete beginners — from your first tables to advanced techniques.
Both courses are taught online with live lecturers, complemented by optional assignments and tests in the e-learning platform and individual consultations with lecturers. Teaching runs in parallel — both parts of the bundle run side by side, so you develop data analysis and database skills at the same time. You find the exact lesson schedule with the specific course date. All materials and lesson recordings are available in our e-learning platform.
Yes, the payment can be split. If you have such a request, please write it in the note on the order form.
Both courses of the bundle including video recordings of all lessons, access to the e-learning system with examples and tests, individual online consultations, loan of specialist literature, interview preparation, free access to our lectures and webinars, and certificates of completion for both courses.