Alates 11.02.2026 on koolitus „Vali Andmetarkus!“ mikrokvalifikatsioon.*

„Vali Andmetarkus!“ koolitusprogrammi eesmärk on anda osalejatele vajalik lähtepositsioon – teadmised ja praktilised oskused alustamaks oma karjääri andmevaldkonnas, näiteks andmeanalüütiku, aruandluse spetsialisti või andmehalduri rollis. Samuti annab koolitus võimekuse kasutada andmeanalüüsi teiste rollide edukaks täitmiseks. 

Vajadus erinevate andmevaldkonna asjatundjate järele Eesti asutustes ja ettevõtetes on suur. Esmaseks vajaduseks, eriti väikese ja keskmise suurusega ettevõtetes, on üldise andmevõimekuse ja andmete kasutamise oskuse tõstmine. Koolitusprogramm annab tervikpildi ja vajalike töövahendite kasutusoskuse peamistes andmevaldkonna teemades alates – mida tähendab andmepõhine juhtimine ja otsustamine; kuidas andmeid koguda ja tagada nende kvaliteet, privaatsus ja turvalisus; kuidas toimub andmeanalüüs ja tulemuste ilmekas visualiseerimine, kuidas kasutada tehisaru andmeanalüüsi protsessi toetamiseks. Osalejad saavad kogemuse andmete elutsükli juhtimises ning omandavad  andmeanalüüsi baasmeetodite ja tööriistade kasutusoskuse. 

Koolitusprogrammi üheks osaks on karjäärimoodul, mille eesmärgiks on toetada osalejaid karjäärimuutusel andmete valdkonda. Moodul aitab mõista andmetega seotud töörolle, sõnastada karjäärieesmärgid, hinnata oma tugevusi ja arenguvajadusi ning omandada tööotsinguks vajalikke oskusi. Samuti toetatakse kontaktide loomist tööandjate ja praktikapakkujatega ning luuakse isiklik tegevusplaan edasisteks sammudeks.  

Programm annab väärtuslikke andmepädevusi ka teiste erialade spetsialistidele. Nagu teisteski tehnoloogia valdkondades, on andmevaldkonnas eriti nõutud spetsialistid, kes kompetentsed nii andmetöötluses kui ettevõtte tegevusvaldkonnas. 

Programmi läbinu:  

• Mõistab andmeanalüüsi protsessi, andmete elutsüklit ning rolli äriprobleemide lahendamisel; 

• Oskab lähtudes äriprobleemist leida vajalikud andmeallikad, andmed koondada, töödelda ja analüüsida, kasutades sobivaid meetodeid ja tööriistu (nt SQL, Power BI, Python); 

• Oskab andmeid tõlgendada ja visualiseerida ning arusaadavalt esitada tulemusi erinevatele sihtrühmadele; 

• Tunneb võimalusi andmeanalüüsi protsesside automatiseerimiseks ja tehisintellekti kasutamiseks analüüsi tõhustamiseks; 

• Tunneb andmehalduse ja andmekorralduse põhimõtteid ja töömeetodeid; 

• Tunneb andmete õigusraamistikku, valdkonna eetilisi ja õiguslikke aluseid, sh andmekaitse ja vastavusnõuded; 

• Oskab end tööotsingutel sihipäraselt esitleda ja teha teadlikke samme tööle kandideerimisel.  

Koolituse teemad:  

• Andmete väärtus ja roll ettevõttes 

• Andmekvaliteet ja turvalisus 

• Andmepõhiste otsuste tegemine 

• Andmete kogumine ja talletamine 

• Andmete analüüs 

• SQL andmete pärimiseks ja kokkuvõtete tegemiseks 

• Andmete visualiseerimine Power BI abil 

• Python andeanalüüsi tööriistana 

• Tehisaru võimalused andmetega töös 

• Edukas tööle kandideerimine

Koolitusprogrammi õppekava on leitav siit: Õppekava

Sihtgrupp: 

• Inimesed, kes soovivad asuda tööle andmeanalüütikaga seotud ametikohtadele. 

• Inimesed, kes soovivad parandada oma tööalast tulemuslikkust, rakendades andmepõhist otsustamist ja andmeanalüüsi tööriistu.  

Õppe maht: Koolituse kogumaht on 247 tundi ja koosneb 240 tunnist koolituskeskkonnas ja 7 tundi iseseisvast praktilisest tööst.

Õpitulemuste saavutamise hindamismeetod: Praktiliste ülesannete ja lõpuprojekti sooritamine 

Õppekavarühm: 0613 Tarkvara ja rakenduste arenduse ning analüüsi õppekavarühm 

Eeldused programmis osalemiseks: 

• Bakalaureusekraad või tehniline kutseharidus (tase 5) 

• Vähemalt kaheaastane töökogemus ametikohtadel, mis eeldavad analüütilist mõtlemist. 

• Tripod Analytical© testi sooritamine 

• Hea arvutikasutusoskus: oskus hallata faile, kasutada erinevat tarkvara ja orienteeruda digikeskkondades. 

• Kesktasemel Exceli oskus: valemite kasutamine, pivot-tabelite koostamine ja andmete esmase puhastamise võimekus. 

• Eesti- ja inglise keele oskus vähemalt tasemel B2 

Koolitusele kandideerimine koosneb 4 etapist: 

I etapp – Taotluste läbivaatus, esmane kandidaatide selekteerimine ankeetide alusel  

II etapp – Ankeetide põhjal väljavalitud kandidaatide kutsumine Tripod Analytical© vaimse üldvõimekuse testile. Testimine toimib veebis (test kestab ca 1 tunni). 

III etapp – Vestlus kandidaadiga 

IV etapp – Kandidaatide lõplik valik profiilide, testitulemuste ja vestluste alusel. Kandidaatide teavitamine e-posti teel. 

REGISTREERIMINE KOOLITUSELE SIIN!

*AS-le BCS Koolitus (registrikood 10723047) on Haridus- ja Teadusministeeriumi 11.02.2026 käskkirjaga nr 1.1-3/26/19 antud tegevusluba mikrokvalifikatsioonõppe läbiviimiseks õppekava „Vali Andmetarkus!“ (kood 261200) alusel.

Koolituse eesmärk: Osalejad saavad teadmised ja praktilise läbimängu kogemuse konteinervirtualiseerimisest, teenuskonteineritest ning konteinersüsteemide haldamisest Kubernetese abil.

Sihtrühm ja eelteadmised: Koolitusele on oodatud kõik teenuste ja serverisüsteemide administraatorid. Kasuks tuleb kergema Linuxi käsurea tundmine.

Õppevahendid: Linuxi operatsioonisüsteemil toimiv arvuti, millel on õigused rakenduste installeerimiseks ja seadistamiseks.

Programm:

Päev 1

Konteinervirtualiseerimine docker/podman baasil

• Virtualiseerimise ülesanded, konteinervirtualiseerimine, ajaloost ja hetkeseisust
• Konteinervirtualiseerimise ideoloogia, teenuste ühildamine
• Konteinerite repod, rakendamisinfo repodes
• Konteineri loomine ja rakendamine
• Dockeri/Podmani installeerimine
• Dockerfile – konteineri “kokaraamaturetsept”
• Docker compose – konteinerisüsteemi “kokaraamat”
• Podmani ja Dockeri erinevused ja sarnasused
• Konteinerite võrgud
• Konteinerite volüümid
• Dockeri/Podmani kasutusjuhud, eelised ja miinused

Päev 2

Kubernetes – Olemus ja kasutamine

• Mis on Kubernetes, milliseid eesmärke püstitab ja kuidas neid realiseerib
• Master, Node, Desired state, deklaratiivkeel ja selle eesmärgid
• Mõisted pod (konteiner), service (teenus), deployment (keskkond)
• Minikube Windows/Linux keskkondades
• Kubernetese käsitsi installeerimine
• Konteinerid ja nende replikad
• Teenused. Teenuse loomine iteratiivselt ja deklaratiivselt
• Salvestuspind Kuberneteses – virtuaalkettad, kleimid ja klassid

Päev 3 

Kubernetes – Administreerimine ja automatiseerimine

• Helm, Kubernetese paketihaldur
• Erinevad võrgunduse ja salvestuslahendused
• Kubernetese uuendamine
• Monitooring
• Kasutajate ja komponentide ligipääsude haldamine
• Teenuste automaatne paigaldamine Kubernetesesse

Õppemeetodid: Õppetöös saab osaleda klassiruumi tulles või liitudes koolitusega läbi veebikeskkonna Zoom.
Koolituse maht on 27 tundi sh 15 tundi praktilisi harjutusi koolituskeskkonnas.

Hindamismeetod: Praktiliste harjutuste sooritamine.

Hindamiskriteerium: Õpiväljundite saavutamist hinnatakse koolituse ajal läbi viidud tagasisidestatud praktiliste harjutuste põhjal.

Koolituse lõpetamine: Koolituse lõpetaja saab tunnistuse kui sooritab kõik koolituse jooksul antud praktilised harjutused.
Osalejatele, kes õpiväljundeid saavutanud ei ole väljastatakse soovi korral tõend koolitusel osalemise kohta.

Hind sisaldab: Kohvipause koolituskeskuses ja ühiseid lõunasööke.

Õppekavarühm: 0612 Andmebaaside ja võrgu disain ning haldus.

Lektori CV

Kliendi tagasisided:

„Koolitus oli sisukas ja hea ülesehitusega. Koolitaja oli pädev ja seletas nii, et aru sai ka inimene, kes polnud antud teemadega kursis, tõi palju näiteid. Koolituse praktilised ülesanded olid sisukad ja õpetlikud.“

„Väga mõnus õhkkond, hea lektor, sisukas koolitus, korralik tehnika, mugav asukoht- mida veel tahta?„

This 2-day hands-on course explores how to build intelligent workflows and assistants by combining Large Language Models with structured logic and automation platforms.

This workshop introduces participants to the tooling and design principles needed to build AI-powered workflows and systems. We’ll cover LangFlow, n8n, LLM prompt engineering, fine-tuning, RAG pipelines, agent orchestration, and the trade-offs between hosted and local models. By the end of the course, you will have created working AI bots and automation tailored to your context.

Target audience and prior knowledge (Prerequisites):

• Developers
• DevOps Engineers
• Solution Architects
• Automation Engineers
• Data/AI Engineers
• Technical Product Owners

Teaching aids: Windows operating system computer. If desired, the participant can use their personal computer, which we ask you to inform the training company about.

Program:

Day 1 — Foundations: From Prompts to Workflows

• Understanding the AI Workflow Landscape: what LLMs can and can’t do, common workflow types, and modern tooling overview (LangFlow, n8n, LangChain)
• Creating your first LLM-powered automation: using n8n and LangFlow to build workflows and connect logic
• Prompt Engineering Essentials: few-shot prompting, roles, output formatting, common mistakes and fixes
• RAG in Practice: building a retrieval-augmented assistant using LlamaIndex or LangChain and a vector database
• Modular AI workflows: breaking down tasks into steps using multi-turn logic and orchestration patterns

Day 2 — Agents, Autonomy, and Evaluation

• Multi-Agent Systems Overview: MCP, A2A patterns, AutoGen-style orchestration, role assignment and structured dialogue
• Tool-Augmented Agents: implementing agents that call external tools, APIs, and internal services dynamically
• Evaluation and Testing: LangSmith, DeepEval, Promptfoo, designing test cases and using LLM-as-a-judge
• Local vs Cloud Models: performance, privacy, and cost trade-offs, including Ollama and fallback strategies
• Final Lab: design and present your own AI workflow or assistant using techniques and tools covered in the course

Learning methods: You can participate in the study joining the training through the online environment Zoom. The volume of training is 16 hours incl. 8 hours of practical exercises in the training environment.

Assessment method: The achievement of learning outcomes is assessed on the basis of feedback-based practical exercises carried out during the training.

Graduation Criteria: A graduate of the training receives a certificate if he performs all the practical exercises given during the training. Participants who have not achieved the learning outcomes will be issued a certificate of participation in the training upon request.

The price includes:

• Unique content – no fluff, only real-world AI and automation.
• Certificate of attendance – includes digital proof of your participation.
• Optional exam – test your knowledge and include your score on the certificate.
• Online format – attend from anywhere with full access to instructors.
• Recordings available – replay sessions at your own pace.
• Live and interactive – ask questions, discuss problems, get feedback.
• Q&A session each day – dig deeper into your edge cases and ideas.
• Quizzes and polls – keep the energy high and check understanding.
• Hands-on labs – build working flows with LangFlow, agents, RAG.
• Homework and feedback – optional exercises to reinforce your learning.
• Lifetime support – follow-up help and project advice anytime.

Curriculum group: 0613 Software and Application Development and Analysis.

This 2-day hands-on course explores how to build intelligent workflows and assistants by combining Large Language Models with structured logic and automation platforms.

This workshop introduces participants to the tooling and design principles needed to build AI-powered workflows and systems. We’ll cover LangFlow, n8n, LLM prompt engineering, fine-tuning, RAG pipelines, agent orchestration, and the trade-offs between hosted and local models. By the end of the course, you will have created working AI bots and automation tailored to your context.

Target audience and prior knowledge (Prerequisites):

• Developers
• DevOps Engineers
• Solution Architects
• Automation Engineers
• Data/AI Engineers
• Technical Product Owners

Teaching aids: Windows operating system computer. If desired, the participant can use their personal computer, which we ask you to inform the training company about.

Program:

Day 1 — Foundations: From Prompts to Workflows

• Understanding the AI Workflow Landscape: what LLMs can and can’t do, common workflow types, and modern tooling overview (LangFlow, n8n, LangChain)
• Creating your first LLM-powered automation: using n8n and LangFlow to build workflows and connect logic
• Prompt Engineering Essentials: few-shot prompting, roles, output formatting, common mistakes and fixes
• RAG in Practice: building a retrieval-augmented assistant using LlamaIndex or LangChain and a vector database
• Modular AI workflows: breaking down tasks into steps using multi-turn logic and orchestration patterns

Day 2 — Agents, Autonomy, and Evaluation

• Multi-Agent Systems Overview: MCP, A2A patterns, AutoGen-style orchestration, role assignment and structured dialogue
• Tool-Augmented Agents: implementing agents that call external tools, APIs, and internal services dynamically
• Evaluation and Testing: LangSmith, DeepEval, Promptfoo, designing test cases and using LLM-as-a-judge
• Local vs Cloud Models: performance, privacy, and cost trade-offs, including Ollama and fallback strategies
• Final Lab: design and present your own AI workflow or assistant using techniques and tools covered in the course

Learning methods: You can participate in the study joining the training through the online environment Zoom. The volume of training is 16 hours incl. 8 hours of practical exercises in the training environment.

Assessment method: The achievement of learning outcomes is assessed on the basis of feedback-based practical exercises carried out during the training.

Graduation Criteria: A graduate of the training receives a certificate if he performs all the practical exercises given during the training. Participants who have not achieved the learning outcomes will be issued a certificate of participation in the training upon request.

The price includes:

• Unique content – no fluff, only real-world AI and automation.
• Certificate of attendance – includes digital proof of your participation.
• Optional exam – test your knowledge and include your score on the certificate.
• Online format – attend from anywhere with full access to instructors.
• Recordings available – replay sessions at your own pace.
• Live and interactive – ask questions, discuss problems, get feedback.
• Q&A session each day – dig deeper into your edge cases and ideas.
• Quizzes and polls – keep the energy high and check understanding.
• Hands-on labs – build working flows with LangFlow, agents, RAG.
• Homework and feedback – optional exercises to reinforce your learning.
• Lifetime support – follow-up help and project advice anytime.

Curriculum group: 0613 Software and Application Development and Analysis.

This workshop consists of two days spent on improving your skills of cloud infrastructure provisioning with the help of mighty Terraform. By using declarative language (HCL) to describe cloud resources, you will learn how to efficiently connect the dots of complex cloud configurations: servers, load balancers, keys, firewall rules, storage, base images, CDNs, lambda functions and many more. State management and sharing with the help of workspaces and external storage engines will allow you to create identical environments from the same code base and ensure their consistency over time. Organizing code base, orchestrating cluster deployments, implementing reusable modules are only few topics that are going to be touched during this extremely pragmatic and practical workshop based on trainer’s real-life experience managing complex cloud environments.

What’s Inside

DevSecOps is the integration of security practices and principles into the DevOps process, with the aim of creating a more secure software development lifecycle. In this 2-day workshop, we will cover tips and tricks on how to increase security of software delivery supply chains and existing infrastructure.

What’s Inside

Day 1:

• Introduction to DevSecOps
  • Definition of DevSecOps; the role of security in DevOps
  • Introduction into threat modeling, attack surface, vulnerability and risk management
  • Overview of DevSecOps tools and practices
• Software supply chain security
  • Definition and importance of supply chain security
  • Supply chain elements: software packages/updates, CI/CD pipelines, external vendors, SaaS vendors
  • Software vendor management, compliance and regulatory requirements, incident response and recovery
  • Threats and risk management to supply chain security
  • Practical exercise: Conduct a supply chain risk assessment for a sample software product and develop a risk mitigation plan
  • Practical exercise: Develop an incident response plan for a supply chain security incident
• Software Bill of Materials (SBOM)
  • Definition and purpose of SBOM in supply chain security
  • Overview of SBOM formats (e.g. SPDX, CycloneDX)
  • SBOM generation tools (e.g. OWASP Dependency-Track)
  • Practical exercise: Generate an SBOM for a sample software product using a SBOM generation tool and analyze it to identify potential security risks.
• SIEM and log management
  • Introduction to security information and event management (SIEM)
  • SIEM components and architecture
  • Types of logs and log management
  • Log analysis and correlation
  • Real-time monitoring and alerting
  • Overview of popular SIEM tools (e.g. Splunk, ELK, LogRhythm)
  • Practical exercise: Install and configure a SIEM tool (ELK) and perform log analysis and correlation to identify potential security incidents.
• Container and Orchestrator Security
  • Overview of containers and containerization
  • Container security risks
  • Secure container deployment
  • Container orchestration security
  • Popular container security tools (e.g. Aqua, Sysdig, Twistlock)
  • Practical exercise: Build and deploy a containerized application using a secure container platform (e.g. Docker , Kubernetes) and apply container security best practices.

Day 2:

• Secret Management
  • Definition of secrets and their importance in security
  • Types of secrets (e.g. passwords, API keys, certificates)
  • Best practices for secret management (e.g. encryption, rotation, access control)
  • Secret management tools (e.g. HashiCorp Vault, AWS Secrets Manager)
  • Integration of secret management in CI/CD pipelines
  • Practical exercise: Implement a simple secret management solution using a tool like HashiCorp Vault and integrate it into a CI/CD pipeline.
• Secure software development
  • Secure coding practices, secure software development lifecycle (SSDL) and threat modeling
  • Code scanners for security problems, integration of security scanners into CI/CD pipelines
  • Practical exercise: Develop a sample application and apply secure coding practices, perform threat modeling, and integrate security testing in a CI/CD pipeline.
• OWASP
  • Overview of the OWASP Top Ten security threats
  • A1: Injection flaws
  • A2: Broken authentication and session management
  • A3: Cross-site scripting (XSS)
  • A4: Security misconfigurations
  • A5: Insecure direct object references
  • A6: Cross-site request forgery (CSRF)
  • A7: Using components with known vulnerabilities
  • A8: Insufficient logging and monitoring
  • Other security risks
  • Practical exercise: Perform a hands-on assessment of a web application, identify and exploit at least one OWASP Top Ten vulnerability.

• Open-Source Security
  • Open-source software security risks
  • Vulnerability management in open-source software
  • Popular open-source security tools (e.g. OWASP Dependency-Check, SonarQube)
  • Practical exercise: Perform a hands-on assessment of an open-source software package using an open-source vulnerability scanner (e.g. OWASP Dependency-Check) and integrate static code analysis using an open-source tool (e.g. SonarQube).
• Version Control Security
  • Git commit signing and verification
  • Git permissions models
  • Practical exercise: Configure Git commit signing with GPG and sign and verify Git commits.

Õppekavarühm: 0613 Tarkvara ja rakenduste arendus ning analüüs.

Lecturer’s Linkedin

This course focuses on the most commonly used Kubernetes features as well as provides practical tutorials and real-life examples of deploying distributed applications, managing networking primitives (load balancers, proxies), setting up persistent data storage, dynamic configuration management, and many other exciting features built into the core of Kubernetes.

(veel …)

This 3-day workshop focuses on solving challenges that organizations face when implementing DevOps initiatives. It introduces principles of DevOps and tools that help reach full automation of infrastructure provisioning and software delivery. Theoretical background as well as practical hands-on examples of tools like Ansible, Docker, AWS, Terraform, Kubernetes, Serverless and many others are given during this workshop.

Overview of the following tools will be given during the workshop:

• Bash|Capistrano|Sshoogr|Fabric
• Puppet|Chef
• VirtualBox
• Vagrant
• AWS EC2
• Jenkins
• Graphite
• LogStash
• Kibana/Grafana.

Coverage:

• Module 01: introduction to DevOps, infrastructure as code, immutable infrastructure, idempotence principle, delivery pipelines, GitOps
• Module 02: managing virtual/cloud resources with IaC, tooling overview, building first infrastructure configuration pipeline with Terraform
• Module 03: managing multi-cloud/multi-data-center resources with Terraform and Terraform modules
• Module 04: integrating with classic server provisioning tools like Ansible
• Module 05: dynamic inventories, network management, building complex infrastructure delivery pipelines, organizing team work
• Module 06: managing base machine images with Packer
• Module 07: introduction to containers and container management
• Module 08: building/publishing images, running containers with Docker
• Module 09: implementing complex use cases with Docker Compose
• Module 10: using Kubernetes to run container workloads
• Module 11: using Helm charts and Kubernetes operators
• Module 12: leveraging service mesh features with Linkerd
• Module 13: introduction to observability: logs, metrics, traces
• Module 14: configuring Prometheus jobs and exporters, creating Grafana data sources and dashboards
• Module 15: DevSecOps: integrating linters, security policy checkers, vulnerability scanners
• Module 16: leveraging managed FaaS: OpenFaaS, Kubeless and Serverless
• Module 17: implementing a chat bot for Slack for effective ChatOps
• Module 18: introduction to chaos engineering

This course focuses on the most commonly used Kubernetes features as well as provides practical tutorials and real-life examples of deploying distributed applications, managing networking primitives (load balancers, proxies), setting up persistent data storage, dynamic configuration management, and many other exciting features built into the core of Kubernetes.

(veel …)

Platform Engineering is critical in today’s tech landscape for enabling developer productivity, system resilience, and operational efficiency. This 2-day practical workshop covers key topics in platform engineering, including infrastructure as code, CI/CD pipelines, GitOps, Kubernetes, Crossplane, and automated deployment strategies. It’s designed for developers, DevOps engineers, and system administrators looking to build and manage robust platforms. The course includes hands-on exercises based on real-world scenarios, ensuring participants gain practical skills they can apply immediately. All participants will receive comprehensive materials, including cheat sheets, access to online slides, and code examples.

Who’s This For: developers, devops engineers, system administrators, software architects.

What’s Inside

Õppekavarühm: 0613 Tarkvara ja rakenduste arendus ning analüüs.

Lecturer’s Linkedin