Data Science

What data science is and why it matters

Every day we leave a trail of numbers behind us: a purchase at the corner shop, a bus ride paid by card, a weather forecast checked on a phone, a mark entered into an electronic school register. Data science is the field that turns such numbers into understanding. A data specialist collects information, cleans it, looks for patterns and explains what those patterns mean — for example, which products sell better in winter, which neighbourhoods need an extra bus route, or which students might need extra support before an exam.

Today almost every organisation — a hospital, a farm cooperative, a city administration, a small online shop — stores data. What is often missing is people who can read it carefully and honestly. That is why data skills are useful not only for programmers but also for economists, engineers, teachers and anyone who makes decisions based on facts.

What you will learn and do in class

The program teaches data work from scratch. It runs for 6 months and includes 48 lessons organised into four modules. Every topic is reinforced with a practical exercise.

Python fundamentals. You start with the language itself: variables, conditions and loops, then functions and modules. Early exercises are small and concrete — a program that converts temperatures, counts the words in a text or checks whether a number is even. By the end of the module you read and write files, for instance opening a CSV table of daily temperatures and finding the warmest day.

Working with data. Here you meet Pandas and NumPy inside Jupyter notebooks. You load tables, filter rows, group records and calculate totals — say, the average number of visitors per weekday or results by region. A large part of the module is devoted to cleaning: real tables have empty cells, typos and duplicates, and you learn how to find and treat missing values. You also write your first SQL queries to pull the rows you need from a database.

Statistics and visualization. You calculate the mean, median and spread and learn when each one tells the truth and when it misleads. You explore relationships and correlation — and why correlation on its own does not prove cause. With Matplotlib you build line charts, bar charts and histograms, and practise turning every chart into a clear one-sentence conclusion.

Machine learning basics. In the final module you build simple regression and classification models — predicting a numeric value, such as electricity use from the outdoor temperature, or sorting items into categories. You learn to evaluate a model honestly on data it has not seen before. The module ends with a final project on a topic you choose yourself, which you complete independently and defend.

Classes run three times a week, two hours each. Homework is reviewed by the instructor with individual feedback.

A typical working day of a data specialist

The job is less about complex formulas and more about careful, patient work. A typical day may include:

• talking to a colleague to understand the real question behind a request;
• finding the right data in a database or a set of files and checking that it is complete;
• cleaning tables: fixing formats, removing duplicates, deciding what to do with gaps;
• exploring the data with summary statistics and quick charts;
• building or updating a model and checking how reliable its results are;
• writing a short explanation of the findings for people who are not specialists.

Much of the time goes into cleaning and checking, and good specialists document every step so that colleagues can repeat the analysis.

Where these skills are used

Data skills are applied in many sectors: trade and logistics, healthcare, agriculture, education, telecommunications, public services and research. After the course, people usually continue in one of several directions: junior data analyst or data science roles, analytical tasks within their current job, deeper study of machine learning, or applying data methods in their own field — economics, engineering or teaching. The course gives a solid foundation and a finished project you can show; further growth depends on continued practice and learning.

How to prepare before the course starts

You do not need prior programming experience, but a little preparation makes the first weeks easier:

• refresh school mathematics: percentages, fractions, averages and reading simple graphs;
• get comfortable with your computer: creating folders, saving and finding files, installing a program;
• try free introductory Python tutorials and type the examples yourself;
• open any table you have — household records, step counts, weather observations — and ask yourself three questions about it;
• set aside fixed time slots in your week now, so that study becomes a habit from day one.

Frequently asked questions

Is the program really free? Yes. All programs of the association are free.

Who can apply, and what documents are needed? Anyone aged 16 or older can apply. No documents are needed to apply — just your name and phone number. A passport copy may be requested after admission so that the certificate can be issued. Our specialists call you back within 1–2 working days.

Are classes held in person or online? Both formats are used: classes take place offline in the association’s training centres or online, depending on the group schedule. Online programs are open to participants across the country.

How much time will I need each week? Plan for at least about 6 hours of classes per week plus independent work — in this program, three two-hour classes a week. Regular practice between classes matters more than long sessions once in a while.

Will I receive a certificate? Yes, a certificate is given on successful completion. Missing more than 30% of classes may mean no certificate, so plan your attendance in advance.

If you are ready to start working with data, apply using the enrolment button on this page.