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OzenVision

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Data Analytics with AI

500 000 ₸
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Allocated 17 Quotas

The "Data Analytics with AI" course is a hands-on program designed for those who want to master modern data analysis tools and develop the ability to make data-driven decisions. Throughout the course, students learn Python, SQL, Power BI, statistics, data visualization techniques and the fundamentals of machine learning. Special emphasis is placed on leveraging cutting-edge AI tools that help automate tasks and enhance the efficiency of analytical work. The program is suitable both for beginners with no prior programming experience and for those looking to systematize and expand their existing skills in data analytics. The curriculum combines theoretical training with practical assignments and real-world case studies. By the end of the course, students complete an individual portfolio project that demonstrates their ability to work with data at every stage of the analytical process. Upon graduation, students are able to collect, process, and analyze data, apply statistical methods, and build basic machine learning models.

Special condition

The student receives an educational grant in the amount of 400 000 KZT, provided that they pay the remaining course fee difference of 100 000 KZT. The student is required to complete the full course, successfully finish the final project, and pass the final assessment with a score of no less than 50%. In case of failure to meet these requirements, the student must reimburse the full cost of the course. If the student misses more than 30% of classes within a calendar month without a valid reason, they may be expelled and will also be required to repay the full tuition cost.

Course details

level

For all

Study format

Online

Entrance exams

No

Duration, in weeks

27

Education language

Russian

Qualifications

junior

Skills


• Proficiency in Python for data analysis and automation of analytical tasks • Collecting, cleaning, and transforming data using pandas and NumPy • Practical skills in working with SQL and relational databases • Performing exploratory data analysis (EDA) and identifying patterns • Applying statistical analysis methods and hypothesis testing • Building interactive dashboards and business reports in Power BI • Using AI tools to accelerate analytics and data processing • Training, comparing, and evaluating machine learning models • Working with classification, regression, and ensemble algorithms

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