Software Engineering

A broad, practical group of programs for developing production-ready software engineers across multiple specialization paths.

• Software Engineering (general track; language options include Java, Python, .NET)
• iOS Development (Swift, Xcode, iOS ecosystem)
• Frontend Development (HTML/CSS/JS, modern frameworks)
• Full-Stack Development
• Automated & Manual Software Testing

Tracks for preparing technical specialists who support modern distributed systems.

• DevOps Engineering
• Cloud Engineering
• Cybersecurity (fundamentals to applied security)

Non-engineering, but highly sought-after areas with a focus on technology management.

• Project Management
• Agile Delivery Fundamentals
• Digital Transformation Essentials

In addition to the tracks listed above, we can launch any training program requested by the client. StrategEast can:

• adapt existing tracks to your specific goals
• adjust program length (shorten or extend modules)
  design training aligned with selected professional certifications (Oracle, AWS, Microsoft, etc.)
• tailor content to government needs, university requirements or industry-specific tasks
• create fully customized learning paths for different audience groups
• integrate practical cases, projects and tools used in your organization

Our mentors are senior engineers, architects and managers from leading technology companies and major IT services & consulting firms. Their practical experience enables us to develop highly applied programs that match the standards and expectations of real industry environments.

Trainings and subsequent internships:

• Group: Data Science (as an example). Description of the training 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:

• Structure: 2 months of intensive internship
• Number of students: teams of 5 participants. Each team receives a dedicated project/task
• Language of instruction: English
• Modality: mentor support online (live sessions + LMS, repositories, progress tracking+weekly Q&A sessions:)
• Certification:  all participants who successfully complete the internship receive an official Internship Certificate issued by Sigma Software Group, and students following cloud-related tracks may also obtain professional certifications from Oracle, AWS or Microsoft, depending on the chosen specialization.
• Outcome: accelerated upskilling and readiness for Strong Junior roles

1. Online application
2. English test (B2+)
3. Technical screening
4. Motivation/soft-skills interview

We ensure full transparency and consistent quality across all stages of the program.

Our reporting framework includes:

• detailed progress metrics for each participant (attendance, assignment completion, test results and quality of delivered artifacts)
• structured midterm and final project evaluation checkpoints
• monthly program reports covering student performance, completion rates, risks and recommended corrective actions
  access to the LMS, repositories and project materials for full visibility
  regular sync meetings with the program coordinator

The program creates a scalable and repeatable talent pipeline equipped with practical skills in data, software engineering and modern technologies. Graduates complete the program with validated competencies, real project experience and a tangible portfolio.

As a result, they enter the job market significantly faster and are better prepared for roles in both the public and private sectors, supporting national digital transformation and workforce development initiatives.

Data Science

The goal of this program is to give participants a strong, practical foundation in Data Science, supported by confident proficiency in Python. Approximately 20–30% of the curriculum focuses on Python tools and techniques, while the remaining 70% covers a wide range of Data Science methods, including regression, classification, clustering, reinforcement learning, natural language processing, deep learning and model selection.

The program provides a complete overview of both Python and applied Data Science, ensuring a well-rounded and highly practical learning experience.

Target Audience

The course is intended for specialists who want to strengthen their Data Science skills, transition into analytics roles or gain practical experience in machine learning.

Trainer Profile

• Over 15 years of experience in IT, with a strong background in data analysis, processing and architecture
• 7+ years of hands-on work in Data Science
• Expertise across multiple industries: corporate finance, stock markets, pharmaceuticals and medical technology
• 10+ years of mentoring in Data Science
• Degrees in “Finance of Enterprises” and “State Regulation of the Economy”
• Certified Data Science and Data Analysis specialist

Course Format and Duration

• Total duration: 4 months
• Frequency: 2 sessions per week
• Session duration: 1 hour 15 minutes
• Format: online
• Language: English

Learning Outcomes

By the end of the program, participants will be able to:

• Understand the fundamentals of Python and Data Science
• Clean, preprocess and transform datasets using Python
• Apply regression methods to model continuous outcomes (linear, polynomial, etc.)
• Use classification algorithms for categorical predictions
• Build and train deep learning models, including fully connected and convolutional networks
• Apply dimensionality reduction techniques like PCA and LDA
• Evaluate models using k-fold cross-validation, parameter tuning and boosting (XGBoost)
• Prepare complete, high-quality projects suitable for a professional portfolio

Course Content

Module 1: Data Preprocessing with Python

Module 2: Regression (Simple Linear, Multiple Linear, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression)

Module 3: Classification (Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification)

Module 4: Clustering (K-Means, Hierarchical Clustering)

Module 5: Association Rule Learning (Apriori, Eclat)

Module 6: Reinforcement Learning (Upper Confidence Bound, Thompson Sampling)

Module 7: Natural Language Processing (Bag-of-words model and algorithms for NLP)

Module 8: Deep Learning (Artificial Neural Networks, Convolutional Neural Networks)

Module 9: Dimensionality Reduction (PCA, LDA, Kernel PCA)

Module 10: Model Selection & Boosting (k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost)

Additional Features

• Home assignments for every module
• Discussion of homework questions at the beginning of each session
• Certificate of completion issued to all successful participants

Requirements

Participants must have at least an intermediate (B1–B2) level of English.

Expected Results

Upon completing the program, participants will:

• Improve their data analysis and machine learning skills
• Confidently clean and preprocess complex datasets
• Apply multiple regression and classification algorithms to real problems
• Use clustering and association techniques to detect patterns in data
• Develop deep learning models for images and text
• Evaluate and optimize ML models using industry best practices
• Create end-to-end Data Science projects suitable for a professional portfolio

Online Data Science Internship for Graduates

• Format: Project-based teamwork + Mentorship + Hard Skills development
• Duration: 2 months
• Participants: 2 teams, 5 members in each team

Goal:

To strengthen and systematize skills gained during the training phase by solving realistic Data Science tasks in small teams, supported by experienced mentors.

Work Format:

• Structure: 2 months of intensive internship
• Number of students: teams of 5 participants. Each team receives a dedicated project/task
• Language of instruction: English
• Modality: mentor support online (live sessions + LMS, repositories, progress tracking+weekly Q&A sessions:)
• Certification:  all participants who successfully complete the internship receive an official Internship Certificate issued by Sigma Software Group, and students following cloud-related tracks may also obtain professional certifications from Oracle, AWS or Microsoft, depending on the chosen specialization.
• Outcome: accelerated upskilling and readiness for Strong Junior roles

Sprint 1 (Week 1–2): Set Up

Activities

• Distribution of self-learning materials
• Understanding the project task
• Defining solution strategy and required tools
• Team role distribution
• Lecture: Instruments and toolchain
• Lecture: Team building and team management
• Friday workshop

Outcomes

Participants establish a productive team environment, define roles, set up tools and align on workflow, ensuring a solid foundation for further technical work.

Sprint 2 (Week 3–4): Exploration

Activities

• Daily meetings
• Exploratory Data Analysis (EDA)
• Feature engineering
• Data augmentation
• Evaluation metric selection
• Presentation skills lecture
• Friday workshop

Outcomes

Participants conduct independent EDA, apply feature engineering and data augmentation, choose relevant evaluation metrics and communicate analytical findings clearly and confidently.

Sprint 3 (Week 5–6): Prototyping

Activities

• Daily sync meetings
• Model selection
• Prototyping and model comparison
• Preparing the first version of the presentation
• Dry-run session
• Friday workshop

Outcomes

Participants design and prototype ML models, justify their decisions, refine results through iteration and prepare a well-structured preliminary project presentation.

Sprint 4 (Week 7–8): Finalization

Activities

• Daily sync meetings
• Finalization and refinement of the project
• Preparing the final presentation
• Dry-run session
• Project Demo

Outcomes

Participants finalize the solution, polish project artifacts, prepare a clear and engaging final presentation and deliver a confident technical demo demonstrating practical value and methodological rigor.

Goal:

To develop communication, collaboration and self-organization skills in distributed teams — essential competencies for real engineering environments.

Lecture Topics (3 hours):

• Effective communication in IT teams
• Time management and self-organization in remote environments
• Agile fundamentals: roles, sprints, daily meetings

Workshops (3 hours):

• Agile: Scrum processes
• Time management and self-organization
• Project presentations

Final Project Presentation

Format: Online team presentations to an expert panel (company representatives, mentors, partners)

Duration: up to 1.5 hours

Goal: Demonstrate technical results, methodological understanding and teamwork effectiveness.

Outcomes Upon Completion

Participants will gain:

• A complete, production-style Data Science project for their portfolio
• Improved teamwork, communication and presentation skills
• Practical experience with Data Science workflows and tools
• Certificate of completion (after the final presentation)