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outpeer.kz

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Data Science

950 000 ₸
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Allocated 8 Quotas

The Data Science course is designed for students who want to gain systematic knowledge and practical skills in data science and machine learning. The program covers the fundamentals of Python programming, working with key libraries for data analysis and visualization, mathematical foundations, statistics, and probability theory, as well as an introduction to machine learning and deep learning. The course is intended for participants with basic knowledge of programming and mathematics who wish to learn how to work effectively with data in practice — from processing to building and evaluating models. The combination of theoretical lessons and practical case studies provides a solid foundation for further development in the field of Data Science.

Special condition

Additional Payment. Upon successful admission to the course, participants are required to pay the remaining portion of the tuition fee before the start of training. The total cost of the 6-month course is 950,000 KZT. This amount is covered as follows: — 400,000 KZT is covered by the TechOrda grant, — 200,000 KZT is covered by the Outpeer school, — the remaining 350,000 KZT is paid by the student.

Course details

level

For beginner

Study format

Online

Start

June

Entrance exams

No

Duration, in weeks

26

Duration in academic hours

156

Education language

Russian

Classes days_of_week

Monday, Wednesday, Friday

Teaching methodology

There are more practices than theories

Qualifications

Machine Learning Engineer, Data Scientist, Data Analyst

Classes format

Lessons are conducted online 3 times a week for 2 hours each.

Skills


▸ Python programming: basic syntax, functions, and working with files ▸ Using data analysis libraries: NumPy and Pandas for data processing and transformation ▸ Data visualization with Matplotlib and Seaborn ▸ Mathematical foundations: linear algebra, probability theory, statistics, and calculus ▸ Data preparation and transformation: handling missing values, encoding categorical features, scaling ▸ Building and evaluating machine learning models: linear and logistic regression, classification and clustering methods ▸ Creating simple ML pipelines, understanding model training principles and avoiding overfitting ▸ Implementing data analysis and machine learning projects using real-world datasets

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