We propose a program consisting of two parts:
(1) an internship for students who graduate from Digital LevelUP in 2025, and
(2) mentor-led training and subsequent internships in three specialties — Data Science, Data Analytics, and DevOps.
1) Internship for 2025 graduates:
- 2 groups: Data Science and Data Analytics
- Number of students: 10 per group
- Structure: 2 months of intensive internship
- Outcome: accelerated upskilling and readiness for Strong Junior roles
- Language of instruction: English
- Modality: online (live sessions + LMS, repositories, progress tracking)
- Certification: internship certificate
(2) Trainings and subsequent internships — From Zero to Hero:
- 3 groups: Data Science, Data Analytics, DevOps (description of the trainings is provided in Annex 1)
- Number of students: 30 per group for trainings, 10 per group for internship (based on the results of the training, the top students will be admitted to the internship)
- Structure: 4 months of training + 2 months of internship
- Training Format: live on-line sessions with a professional, experienced trainer
- Training Delivery: 2 sessions per week, hands-on assignments, reviews, regular mentor sessions
- Outcome: portfolio and skills at Junior → Strong Junior level
- Language of instruction: English
- Modality: online (live sessions + LMS, repositories, progress tracking)
- Certification: separate course-completion certificate and internship certificate
Project work in small teams with an assigned mentor, regular stand-ups, and artifact reviews (code, reports, dashboards). Final demo day before a panel and partners. Outcomes include repositories, documentation, and a presentation ready for a professional portfolio.
Work Format:
- Teams of 5 participants
- Each team receives a dedicated project/task
- Mentor support: up to 4 hours per week ( live sessions + support in the chat)
- Weekly Q&A sessions: 2 hour per week
- Internship for 2025 graduates: shortlist from graduates 2025 with top performance; additional interview if needed.
- From Zero to Hero: online application → English test (B2 or higher) → basic technical screening by track → short motivation & soft-skills interview (or Motivational Letter).
Transparent progress metrics (attendance, homework completion, test results, artifact quality), midterm and final project gates, and monthly reports to IDDA on funnel, performance, risks, and corrective actions. IDDA receives access to the LMS/repositories and regular syncs with the program coordinator for full transparency.
A repeatable talent pipeline with applied skills in data and modern engineering. Graduates enter the market with validated competencies, a tangible portfolio, and project experience — accelerating employment and addressing the needs of public and private employers.
Annex 1
- Python for data analysis: preparation/cleaning, feature engineering
- Regression and classification (linear methods, SVM, tree-based ensembles)
- Clustering and association rules (K-Means, Hierarchical, Apriori/Eclat)
- NLP (bag-of-words, classic algorithms)
- Deep Learning (foundational neural nets, CNN)
- Dimensionality reduction (PCA, LDA, Kernel PCA)
- Model selection and boosting (k-fold CV, Grid Search, XGBoost)
- Deliverables: 1–2 complete case projects + code repository
- Linux, Git, fundamentals of virtualization
- AWS (EC2, IAM, S3, CloudFront, DR/HA, networking/security)
- Containerization: Docker (network, volumes, compose), foundational Kubernetes
- CI/CD: GitHub Actions, GitLab CI, Jenkins; pipelines and release cycles
- Monitoring & logging: Zabbix, Prometheus, Grafana, ELK overview
- IaC & configuration: Terraform, Ansible, Packer; Helm intro
- Secrets & cost: Vault/Parameter Store; cost-optimization basics
- Deliverables: working pipelines and infrastructure manifests
- SQL: from basics to advanced queries, aggregation, joins, optimization
- Excel: functions, pivot tables, visualization, reporting
- Power BI: data modeling, DAX (basic/advanced), dashboards, KPI, storytelling
- Python for analysts: Numpy, Pandas, visualization (Matplotlib), foundational ML (EDA, regression/classification/clustering)
- Deliverables: production-grade BI dashboards + analytical notebooks with case studies
