You will learn to:
- Analyze data, identify patterns, and build forecasts
- Create ML models (including neural networks and deep learning)
- Visualize results for business and reporting purposes
- Automate data processing: cleaning, transformation, classification
- Apply your knowledge in real cases—from startups to corporations
Technologies and Tools:
- Programming language: Python
- Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras, Matplotlib, OpenCV, Regex, and others
- Practice based on real R&D solutions and proprietary methodologies
Who is this course for?
- Those who want to transition into Data Science from analytics, development, or related fields
- Specialists who want to gain a deeper understanding of working with data
- Students of technical specialties
Result
After the course, you will confidently work with data, build ML solutions, and qualify for positions such as Data Scientist, ML Engineer, or Data Analyst.
Data Science Course Program
How the course works:
- Live classes twice a week
- 5 practical workshops: writing code together and analyzing mistakes
- Mini-projects: cases reflecting real company tasks
Live chat with mentors: support, Q&A, and feedback
Lesson 1. Lesson 1. Introduction to statistical learning
Homework 0. Introduction
Lesson 2. Data preparation for statistical learning
Homework 1. Data preparation and analysis
Lesson 3. Training regression models on Big Data
Homework 2. Polynomial regression
Lesson 4. Kalman filter
Homework 3. Recursive smoothing
Lesson 5. Nonlinear smoothing – R&D results
Lesson 6. Workshop 1. Regression
Homework 4. Regression
Lesson 7. Theory and practice of decision support
Lesson 8. Multi-criteria decision-making methods – R&D results
Homework 5. ERP system mock-up for multi-criteria decision-making
Lesson 9. Methodological foundations and technologies of data intelligence
Homework 6. Implementation of data intelligence processes: mini-projects in OLAP, Data Mining, Text Mining
Lesson 10. Clustering methods and technologies
Homework 7. Implementing clustering methods
Lesson 11. Workshop 2. Classification (part 1)
Lesson 11. Workshop 3. Classification (part 2)
Homework 8. Applied clustering
Lesson 12. Classification and identification methods
Homework 9. Implementing classification/identification methods
Lesson 13. Workshop 4. Clustering
Homework 10. Applied classification
Lesson 14. Basics of artificial neural networks
Homework 11. Designing neural networks
Lesson 15. Main types and technologies of neural networks
Lesson 16. Workshop 5. Neural Networks
Homework 12. Applying neural networks
Topic 2.1. Algorithms and Technologies for Forecasting the Dynamics of Performance Indicators in Trading Companies
Lesson 17. Algorithms and technologies for forecasting the dynamics of performance indicators in trading companies
Homework 13. Development of a software module for forecasting the dynamics of performance indicators in trading companies (mini-projects in the field of data analysis for e-commerce tasks)
Lesson 18. Methodological foundations of SCORING analysis
Lesson 19. Practice of SCORING analysis
Homework 14. Prototype of a CRM system for SCORING analysis (mini-projects in the banking sector of data analysis)
Lesson 20. Fundamentals of geoinformation technologies
Homework 15. Prototype of a GIS system
Lesson 21. Practice of geospatial information analysis
After completing the online Data Science course
Students will gain competencies in:
- Statistical learning
- Decision support technologies
- Machine learning methods
- Data intelligence (AI)
- Geospatial data analysis (GIS)
Each topic includes homework with the option to create a mini-project for your personal portfolio.
Where Data Science skills can be applied:
- Data analysis for e-commerce solutions
- Data analysis for industrial and infrastructure CRM and ERP systems
- Data processing and creation of virtual environments using Digital Twins, Automotive, and IoT technologies
Career opportunities after the Data Scientist course:
After completing the course, you can apply for roles related to data analysis, processing, and modeling, including:
- Data Scientist – works with large datasets, modeling, and forecasting
- Data Analyst – identifies trends, market needs, and solves business problems using data
- Business Intelligence Analyst – provides insights that support data-driven business decisions
- Data Engineer – responsible for data collection, processing, storage, and data pipeline development
- Database Architect – designs and manages databases, develops schemas, selects technologies, and defines optimal storage and access strategies
Want to dive deeper into the topic?
Read articles by our trainer Andrii Salata 👇
Who is a Data Scientist and how to know if this career is right for you? A professional overview
FAQ
Do you need prior experience to take the online Data Science courses?
Our course is designed with the understanding that students may have no prior experience in Data Science. However, to work effectively during the course, it is recommended to have:
- Basic knowledge of programming
- Basic knowledge of Python
- Basic knowledge of mathematics: elements of probability theory, discrete mathematics, matrix theory, function analysis, analytic geometry, trigonometry
Are there any technical requirements for a computer to take the Data Science course?
To make your DevOps learning as effective as possible, it is recommended to have a computer with the following technical specifications:
Operating system:
- Windows 10 (64-bit)
- macOS 10.13 or later
- Linux: Ubuntu 20.04–24.04
Processor:
- Minimum: Intel Core i3 (4th generation)
- Recommended: Intel Core i5 (7th generation)
AMD equivalents are acceptable
RAM:
- Minimum: 8 GB
- Recommended: 12 GB
Storage:
- Minimum: 200 GB HDD or more
- Recommended: 200 GB SSD or more
Is it possible to pay for the Data Science course in installments?
Yes, we provide the option to pay for the course in several installments. However, promotional offers do not apply in this case — installment payments are only available for the full course price. For detailed information about payment terms and installment options, please contact the course manager or indicate your preferences during registration.
What are the advantages of studying at Sigma Software University?
- Expert instructors. Training from leading specialists in the field of Data Science
- Modern methodologies. Use of up-to-date tools and techniques for data analysis
- Career opportunities. Access to a variety of professional roles after completing the course
- Certificate/diploma. Receive an official certificate upon completion
- Flexible schedule. Convenient timetable and online learning format
How relevant is the knowledge students gain worldwide?
Our course takes into account the latest trends, and the program is based on international standards. This ensures you will be competitive in the global market. The knowledge we teach is actively applied in leading companies around the world.
Payment in installments without commission and interest is available from PrivatBank
