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Киреев Василий Сергеевич
Обработка и анализ больших данных представляет собой новую практическую задачу, требующую навыков работы с современным инструментарием. В настоящее время данные называют «нефтью 21 века», они накапливаются в корпоративных и государственных информационных системах, социальных сетях, веб-блогах и сайтах и потенциально являются ценным ресурсом для извлечения новых знаний, инсайтов для научных исследований, повышения эффективности и конкурентоспособности предприятий. Методы интеллектуального анализа больших данных, таким образом, представляют собой тот необходимый инструмент для высвобождения этого потенциала. Курс «Машинное обучение...
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Обработка и анализ больших данных представляет собой новую практическую задачу, требующую навыков работы с современным инструментарием. В настоящее время данные называют «нефтью 21 века», они накапливаются в корпоративных и государственных информационных системах, социальных сетях, веб-блогах и сайтах и потенциально являются ценным ресурсом для извлечения новых знаний, инсайтов для научных исследований, повышения эффективности и конкурентоспособности предприятий. Методы интеллектуального анализа больших данных, таким образом, представляют собой тот необходимый инструмент для высвобождения этого потенциала. Курс «Машинное обучение и большие данные» входит в число базовых при подготовке современных экономистов-математиков на уровне магистров. Изучение дисциплины позволит студентам получить и развивать навыки анализа и диагностики проблем экономики, современных методов их решения, а также ознакомиться с современной спецификой исследования операций в зарубежных и отечественных организациях. Целями и задачами курса являются: формирование фундаментальных общеэкономических и естественнонаучных знаний; освоение математических и инструментальных методов машинного обучения; использование современных информационно-коммуникационных технологий в профессиональной деятельности; закрепление профессиональных навыков в области прогнозирования основных социально-экономических показателей деятельности предприятия, отрасли, региона и экономики в целом. Компетенции по решению задач в анализе данных с помощью методов машинного обучения, будут получены студентами после прохождения курса «Машинное обучение и большие данные». Изучение дисциплины позволит выработать навыки постановки и решения проблем развития организации, развить творческое мышление специалистов в области системного анализа и бизнес-моделирования, выработать умение решать управленческие проблемы в конкретной экономической ситуации.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines big data processing and analysis, a topic highly relevant to modern business and scientific research
Builds a solid foundation for economics students specializing in mathematical modeling and analysis
Helps students forecast key economic indicators using machine learning and big data analysis
Taught by instructors who are experienced economics and mathematics professors
May require prior knowledge in mathematics and data analysis

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Reviews summary

Well-grounded theoretical foundation

This course provides a strong foundation in the theoretical underpinnings of machine learning and big data analysis. While students have mentioned that practical exercises could be improved, they universally agree that the theoretical content is comprehensive and well-presented.
Strong theoretical grounding in machine learning.
"Мало практики. Но теоретическая база очень основательна"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Машинное обучение и большие данные with these activities:
Read "Machine Learning for Dummies"
Provides a gentle introduction to machine learning
Show steps
  • Read chapters on fundamental concepts of machine learning.
  • Work through practice exercises and examples.
Find a mentor
Provides guidance and support
Show steps
  • Identify potential mentors in your field.
  • Reach out and request a mentorship.
  • Set up regular meetings to discuss progress and challenges.
Review basics of computer science
Solidifies foundation in computer science
Browse courses on Artificial Intelligence
Show steps
  • Go over fundamentals of data structures.
  • Review algorithms and their implementation.
  • Brush up on basic programming concepts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Code daily
Develops coding skills and fluency
Show steps
  • Start with simple coding problems.
  • Practice writing clean and efficient code.
  • Build small projects to apply learnings.
Solve LeetCode problems
Develops problem-solving skills and algorithmic thinking
Show steps
  • Start with easy problems and gradually increase difficulty.
  • Analyze problem statements and design efficient solutions.
  • Submit solutions and review feedback to improve.
Attend workshops
Provides mentorship and practical application
Show steps
  • Find workshops that are relevant to the course.
  • Attend workshops and actively participate.
  • Implement learnings from workshops in your own projects.
Build a portfolio
Provides hands-on practice and showcases skills
Show steps
  • Work on projects that demonstrate your abilities.
  • Write blog posts or articles on your learnings.
  • Maintain a personal website to showcase your work.
Become a teaching assistant
Reinforces understanding by teaching others
Show steps
  • Apply for a position as a teaching assistant.
  • Prepare and deliver lectures or assist students in labs.
  • Answer questions and provide guidance to students.

Career center

Learners who complete Машинное обучение и большие данные will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models. The course, "Machine Learning and Big Data," can be particularly valuable as it provides a comprehensive understanding of machine learning algorithms, model evaluation techniques, and big data technologies. This knowledge is essential for Machine Learning Engineers to build and deploy effective machine learning systems.
AI Engineer
AI Engineers develop and maintain artificial intelligence systems. The course, "Machine Learning and Big Data," can be particularly valuable as it helps build a foundation in machine learning algorithms, data analysis techniques, and big data technologies. This knowledge is essential for AI Engineers to design, implement, and evaluate AI systems.
Data Architect
Data Architects design and manage data systems. The course, "Machine Learning and Big Data," can be particularly valuable as it provides a comprehensive understanding of big data technologies and data management principles. This knowledge is essential for Data Architects to design and implement scalable and efficient data systems.
Data Analyst
Data Analysts collect, clean, and analyze data to extract insights. The course, "Machine Learning and Big Data," can be very useful as it teaches methods for data analysis, data visualization, and statistical modeling. This knowledge is essential for Data Analysts to effectively interpret and communicate data-driven insights.
Data Scientist
Data Scientists use data to solve business problems. The course, "Machine Learning and Big Data," can be very useful as it provides a foundation in data analysis, machine learning, and big data technologies. This knowledge is essential for Data Scientists to effectively extract insights from data and develop data-driven solutions.
Software Developer
Software Developers design, develop, and maintain software applications. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Software Developers in developing data-driven applications.
Statistician
Statisticians collect, analyze, and interpret data. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Statisticians in analyzing data and developing data-driven solutions.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Operations Research Analysts in analyzing data and developing data-driven solutions.
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Business Analysts in identifying data-driven opportunities for process improvement.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Quantitative Analysts in analyzing financial data and making data-driven decisions.
Financial Analyst
Financial Analysts evaluate financial data to make investment recommendations. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Financial Analysts in analyzing financial data and making data-driven投资 decisions.
Management Consultant
Management Consultants advise businesses on how to improve their operations. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Management Consultants in analyzing business data and making data-driven recommendations.
Project Manager
Project Managers plan and execute projects. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Project Managers in analyzing project data and making data-driven decisions.
Product Manager
Product Managers develop and manage products. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Product Managers in analyzing product data and making data-driven decisions.
Researcher
Researchers conduct research in a variety of fields. The course, "Machine Learning and Big Data," may be useful as it provides an understanding of data analysis techniques and big data technologies. This knowledge can be helpful for Researchers in analyzing data and developing data-driven solutions.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Машинное обучение и большие данные.
An in-depth reference for deep learning techniques, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. Suitable for advanced learners and practitioners.
A classic textbook that provides a comprehensive and in-depth treatment of statistical learning methods. Covers various aspects of machine learning, including supervised and unsupervised learning, regularization techniques, and model evaluation.
Provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning, regularization techniques, and model evaluation. Ideal for students with a strong statistical background.
A rigorous and in-depth treatment of machine learning from a probabilistic perspective. Provides a solid foundation for researchers and advanced learners seeking a deep understanding of the mathematical underpinnings of machine learning.
Delves into the mathematical foundations of machine learning, providing a rigorous treatment of pattern recognition, statistical modeling, and Bayesian inference. A valuable resource for students with a strong mathematical background.
A comprehensive guide to machine learning in Python, covering topics such as data preprocessing, feature engineering, and model evaluation. Includes hands-on exercises and code examples.
Focuses on the statistical foundations of machine learning, providing a comprehensive understanding of data analysis and modeling techniques. Suitable for students with a strong statistical background.
Provides a comprehensive overview of machine learning algorithms and techniques. Emphasizes practical applications and real-world examples, making it accessible to students with diverse backgrounds.

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