DACA · Data and AI · career accelerator
Data Analyst Career Accelerator
Eleven weeks in the role, not in the audience. You step into a data analyst's job for a fictional Estonian start-up called UrbanStyle and solve its real business problems, with mentors who do this work for a living. You leave with a portfolio you can show, and with a certificate.
- 11 weeks
- Online, with mentors
- Registration open
The list of groups below comes straight from our learning platform, so what you see is what is actually open. Pick the dates that fit, and we will come back to you.
Volume312 academic hours
Duration11 weeks
RhythmWeekly, alongside a job
FormatOnline, with mentors
Language of instructionEnglish
AssessmentPortfolio-based
Registration is open. The dates of every group currently taking registrations are in the form below.
WHO IT IS FOR
Who this programme is built for
i
You already have a profession
You are good at what you do. This is not a first course after school; it is built for someone changing direction with working experience behind them.
ii
You want the work, not the lecture
You spend eleven weeks in the role of a data analyst rather than watching someone describe it. If you want a video library, this is the wrong programme.
iii
You can hold a weekly rhythm
One piece of real work every week, reviewed by a mentor. The rhythm is designed to run alongside a job, but it is a real commitment, not a background tab.
Language
The whole programme runs in English
This is not an Estonian programme with English subtitles. It is a group taught in English from the first session to the final defence, and you can go through the entire path, from this page to your certificate, without needing Estonian.
Live sessions and mentor feedbackEvery session with your mentor and your group runs in English, and the comments you get back on your work are in English.
Materials and the work you submitCourse materials, the datasets you work on and the portfolio you build are all in English. You write and present your analysis in English.
Registering and talking to usThe registration form on this page is in English, and you can reach our team in English by mail or by phone. Estonian and Russian are also available if you prefer one of them. Available dates are in the registration form. Picking one is not a commitment yet, it is how you tell us what works for you.
Evidence
What the finished Estonian-taught group reported
This course runs in English, but the group whose results are below studied it in Estonian - same curriculum, same mentors, same UrbanStyle project. We show you their outcomes as our commitment: you get the same programme, just taught in English. The numbers are their own exit-survey answers; the stories are written in English as third-person summaries of what they told us in Estonian, not direct quotes.
9 of 9rated their own AI skill in the top band at the end: they delegate to AI and use it creatively
4 of 9finished having achieved more than they came in expecting
4 of 9named their first working dashboard, in week five, as the turning point
Ele Sepp
Ele Sepp came to the programme with a background in sociology and only light previous contact with data work. She found analysis far less solitary than she expected: spotting a pattern in the team's data, after all the cleaning and processing, became one of her proudest moments in the programme. Her advice to anyone weighing the same move: the fear is normal, this is not rocket science, and the skills pay off in almost any role, even if you never change career.
Doris Kaarus
Doris Kaarus had been looking for work that truly engaged her, and found it in the fast-changing world of IT and data. The programme gave her practical skills, real connections and the confidence to make different decisions about her career. Her advice: you find out quickly whether the field is for you, and if it is, an interesting path follows.
Sille
Sille worked in the environmental field, where daily contact with data sparked her interest in learning the tools properly. She can now analyse data end to end - judging its quality, cleaning it, visualising the results and making decisions from it - and found a direction to build on. Her advice: it feels hard at first, then the pieces click, and the skill is useful in almost any role, even outside a dedicated analytics job.
Eike
Eike came from an HR and administrative background and had assumed data analysis was a field for technical specialists only. She now uses data to build clearer overviews, spot trends and support better decisions in her own HR work: proof, in her account, that this is a skill any motivated person can learn.
Kristjan
Kristjan joined as a software developer with only a rough sense of what data analysis involved. He left with a concrete grasp of the whole pipeline: querying, cleaning and aggregating data in SQL, visualising it in Python with recognised dashboard-layout patterns, and automating the process end to end. He also picked up a new AI tool along the way (Aider) that he can already picture using in his own software work.
Triin
Triin worked in communications and marketing and wanted more structure and skill in deciding things from numbers. The programme gave her exactly that, and her advice to anyone curious is simple: just start, since the tools, mentors and skills are there to build on.
Rita
Rita managed a team that relied mainly on Excel and a great deal of manual work. With what she learned, she can now make that work more efficient and automated, and present it clearly to others. She calls the programme a solid base to keep building on.
DACA graduate
One graduate, who asked to stay anonymous, put it simply: if you think analytically, this can be a great field. The programme gives a solid base to build on, even though some gaps remain to fill in afterwards.
Learning outcomes
What you will be able to do
- Apply data cleaning and organisation procedures to make raw data suitable for analysis.
- Create analytical queries that support business decisions, using SQL and the Python pandas library.
- Visualise data and build interactive dashboards (Microsoft Power BI Desktop or Python Plotly/Streamlit) to communicate results clearly.
- Present your analysis projects and your portfolio understandably to both technical and business audiences.
- Apply AI tools (chat assistants and coding assistants) responsibly throughout the data analysis workflow: formulate prompts, validate and check the output, and document your decisions.
The work
Eleven weeks inside one company's data
You do not build toy examples. From week one you hold a job at a fictional Estonian online retailer, and everything you learn arrives because that business needs an answer.
- 01
You are handed a business, not an exercise
UrbanStyle is a fast-growing online retailer whose data is a mess. Its managers have questions they cannot answer, and from week one those questions are yours.
- 02
You find what is actually wrong with the data
Duplicated sales rows, three date formats, missing customers. You learn to see it, and then to fix it, on data that behaves like real data because it was built to.
- 03
You turn the cleaned data into a decision
Analysis, then a dashboard a manager can open and read. Not a screenshot of one: a working view, built by you, that answers the question you were given.
- 04
You defend what you built
You present your work and your reasoning to people who do this for a living, and you take their comments back into the next piece. That loop repeats every week.
Tools
What you actually work on
- 01
Databases: SQL and PostgreSQL
Query and clean data. This is where a shaky dataset becomes one you can defend.
- 02
Analysis: Python and pandas
Find the patterns and relationships that answer a business question, not a textbook exercise.
- 03
Visualisation: Power BI or Streamlit
Build dashboards a manager can read. Windows users work in Power BI, Mac users in Python Streamlit; the programme supports both fully.
- 04
Portfolio and the job market
Eight to ten practical projects, then preparation for entering the job market with a recruitment specialist who works on your profile and interview readiness.
By the end you have eight to ten practical projects, including SQL scripts for data quality, customer segmentation analyses in Python and interactive dashboards.
Time investment
How a study week works
Every week has the same shape. It is a pattern, not a calendar entry: the exact days and times are confirmed when your group opens and reach you as an invitation.
Time per week
Booked time3 × 1.5 hSessions with your group. These are the only hours in the week that have to be open.
Time and placeBy calendar invitationThe exact time and place of every session reach you by calendar invitation once your group opens, straight into your own calendar.
One week
- Start of the weekSession with your groupLive, with your mentor and your group. The week gets its direction: what you will have finished by the end of it, and what that work is assessed against.
- First half of the weekIndependent workYou choose when. This is where the piece you bring to the mid-week session gets made.
- Mid-weekGroup workYou work on a real task together. This is where you find out fastest whether you are on the right track, because you see several solutions at once.
- Second half of the weekIndependent workYou take the work far enough that it can be shown to someone else.
- End of the weekSession and feedbackYou show what you finished and get feedback on it. The week closes, and the next one starts from a clean sheet.
How it runs
- Weeks 1–3Foundations and the business questionYou can turn a vague business problem into a question that data can answer.
- Weeks 4–5SQL, and data you can defendA dataset you cleaned yourself, and queries that stand up when someone checks them.
- Weeks 6–9Dashboards, Python and the database underneathA working dashboard a manager can read, and the code and pipeline behind it.
- Weeks 10–11Portfolio, defence and the job marketA defended final project and a profile ready for interviews.
What goes in your calendar
Duration11 weeks
Total volume312 academic hours
Guided sessions66 academic hours
Your own work246 academic hours
The dates of the next group are confirmed before it starts. What changes is the dates, not the pattern: the number of sessions in a week and their order are the same in every group.
Assessment
How your work is judged
You are assessed on what you produceThere is no written exam. Each learning outcome is assessed against stated criteria on the work in your portfolio, and the result is a pass rather than a grade on a scale.
Every week leaves an artefactEach week ends with a piece of work that is reviewed: a query, an analysis, a dashboard. That is what makes the portfolio at the end a record rather than a claim.
Three things are required to finishTaking part in at least 70% of the mentor sessions, every required weekly artefact assessed as a pass, and the final project defended successfully.
Your mentors
Who you work with every week
Alek Kozlov, MA
Programme architect and agile product management expert
Ex-Skype and Microsoft · orchestrates AI workflows
More than 20 years in IT and agile product development, including leading Skype's global agile transformation. Brings the view of how data analytics creates business value, and teaches how to use AI to speed up your own everyday work.
Silver Toompalu, MSc
SQL and data query expert
27+ years of teaching experience
A practitioner who has run SQL courses at every level and lectures at the Estonian University of Life Sciences and Tallinn College of Engineering. Connects technical know-how to practical examples and makes the structured world of databases make sense.
Renee Kooli, BSc
Visual storytelling expert
16+ years in data analysis · ex-Microsoft/Skype, Omniva
A data-analysis practitioner who turns complex data into visual stories people understand. Strengths: data visualisation, designing management views, and the strategic adoption of analytical solutions in organisations.
Ülari Ainjärv
Software architect, Python and API expert
20+ years in development and architecture
Has built business-critical systems for start-ups (Cleveron, Hepta Airborne) and public institutions (Riigi Kinnisvara). Joins the programme in its most technical phase and teaches how to connect Python to databases, automate data workflows and write reliable code.
They act as your team leads for the eleven weeks: you send work, they send it back with comments, and you do the next piece.
Before you start
What is expected of you
Previous experience with data is not required and no programming background is assumed. What the programme does need is enough English to follow live sessions and technical materials, a computer you can install tools on (Windows and Mac are both fully supported), and the weekly hours the rhythm above describes. If you want to check any of that against your own week before you commit, call us and we will go through it.
Talk it through firstState support is available to eligible learners; employer support and payment in instalments are also possible.
Outcome
What you finish with
A portfolioEight to ten practical projects: your tangible proof for an employer
A certificateA certificate issued by Ettevõtluskeskus OÜ, a registered Estonian adult-education provider, once you have achieved the learning outcomes
A prepared profilePreparation for the job market with a recruitment specialist: your profile and your interview readiness
Who is behind it
The provider
EHIS 22972Our curricula are entered in the Estonian national education register
AssessmentPortfolio-based throughout: you are assessed on the work you produce
MentoringGroup mentoring runs through the programme; one-on-one mentoring is available if needed
SupportState support is available to eligible learners – ask us
Next step
The list of groups below comes straight from our learning platform, so what you see is what is actually open. Pick the dates that fit, and we will come back to you.
Not ready to register yet?
Leave your email and we will send you DACA news and an invitation when the next English-taught group opens.
Common questions
Questions people ask before they register
Do I need previous experience with data?
No. The programme is built for a career change and starts from the basics, step by step with a mentor. No programming background is assumed either. What it does ask for is the weekly practice.
Do I need to speak Estonian?
No. Sessions, materials, mentor feedback and the registration form on this page are all in English, and you can deal with our team in English throughout. Our study organisation page is written in Estonian; ask us and we will go through it with you in English.
Can I take part from outside Tallinn, or outside Estonia?
The programme runs online, so you take part from wherever you are. The sessions are live at a set time rather than recorded, and the exact times reach you by calendar invitation once your group opens. If you are outside Estonia, tell us before you register and we will go through what applies in your situation.
How long is it and how much time does it take?
Eleven weeks, 312 academic hours in total. Of those, 66 are guided sessions with your mentor and your group, and 246 are your own work, which you place in your week yourself. The rhythm is built to run alongside a job; the exact split across weeks is confirmed when a group opens.
What computer and tools do I need?
A computer you can install software on. Windows and Mac are both fully supported: Windows users work in Power BI, Mac users in Python Streamlit, and the programme carries both tracks in full. A practical tip from earlier groups is to work on two screens, with materials on one and your working environment on the other.
What happens if I have to miss a session?
Missing an occasional session is normal and it is planned for. Finishing the programme requires taking part in at least 70% of the mentor sessions, so there is room in the schedule for a week where work or family takes over. If you can see a longer gap coming, tell us early and we will look at it with you.
Does AI do the analyst's work for me?
AI takes the routine: cleaning, drafts, repetition. The conclusion, the context and the responsibility stay with the analyst. That is exactly why AI runs through the whole programme as a working tool rather than sitting in one module.
How is my work assessed?
On your portfolio, throughout. Each learning outcome is assessed against stated criteria on the work you produce, and the result is a pass rather than a mark on a scale. There is no written exam; the final step is defending your final project.
What do I hold at the end?
A portfolio of eight to ten practical projects you can show at an interview, and a certificate issued by Ettevõtluskeskus OÜ once you have achieved the learning outcomes. If you take part but do not achieve all of them, you receive a record of your participation instead.
What does it cost and can I get support?
The price and the payment options are shown in the registration form above and confirmed before you commit. State support is available to eligible learners, and employer support or payment in instalments are both common; ask us and we will go through your situation.
Our terms of study and cancellation rules are on the study organisation page (in Estonian); ask us and we will go through them with you in English. The rest of our English-taught programmes are listed on the English overview, and this same programme in Estonian is at Andmeanalüütiku karjäärikiirendi.
For teams
Also available as in-house team training
DACA also works well as in-house training: your whole team goes through the same path together, on your own real situations.
Ask for a quote →See more options for companies →