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Космачев Алексей Дмитриевич,
Бардуков Анатолий Андреевич,
and
Зимовнов Андрей Вадимович
Ключ к успешному развитию любой компании в современном мире — это умение делать быстрые и правильные решения, основываясь на данных. С каждым годом этих данных становится всё больше, при этом старые методы анализа перестают адекватно работать на таких...
Read more
Ключ к успешному развитию любой компании в современном мире — это умение делать быстрые и правильные решения, основываясь на данных. С каждым годом этих данных становится всё больше, при этом старые методы анализа перестают адекватно работать на таких...
Read more
Ключ к успешному развитию любой компании в современном мире — это умение делать быстрые и правильные решения, основываясь на данных. С каждым годом этих данных становится всё больше, при этом старые методы анализа перестают адекватно работать на таких объемах.
На этом онлайн-курсе НИУ ВШЭ мы изучим основные технологии и приемы, позволяющие эффективно хранить и обрабатывать огромные массивы информации. Мы также обсудим, откуда данные вообще возникают, как их собирать и какая инженерная подготовка требуется для безболезненной работы с этими технологиями.
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Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Learn advanced technologies for working with large datasets
Taught by renowned instructors Zimovnov, Kosmachev, and Bardukov
Covers methods for storing, processing, and analyzing big data
Strengthens understanding of data sources and collection techniques
Provides a solid foundation for working with big data
Some prerequisite knowledge in data analysis may be beneficial
Save this course
Save Обработка больших данных to your list so you can find it easily later:
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Reviews summary
Effective big data handling
Students say this course effectively teaches the basics of big data handling with a lot of practical application. One student did note that some of the practice assignments had broken data sets.
Many practical applications to enhance learning
"Интересный курс, много практики."
"Very liked the section about bash: discovered something new, although he himself is familiar with it and has been using it for a long time."
Be prepared to encounter broken data sets in some practice assignments
"В некоторых практических заданиях битые выборки, нет возможности потыкать данные."
Expect challenging homework assignments
"Хороший курс, домашки сложные"
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 Обработка больших данных with these
activities:
Form a study group with classmates
Show steps
Fosters collaboration, improves understanding through peer-to-peer discussions.
Browse courses on
Collaboration
Show steps
-
Find a group of classmates with complementary skills and interests.
-
Set regular meeting times and stick to them.
-
Discuss course material, work on assignments, and prepare for exams together.
-
Support and encourage each other.
Review linear algebra
Show steps
Refreshes foundational knowledge of linear algebra necessary for understanding data analysis topics.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of vectors, matrices, and linear transformations.
-
Practice solving systems of linear equations.
-
Review the basics of matrix operations, such as addition, subtraction, multiplication, and inverse.
-
Practice finding eigenvalues and eigenvectors of matrices.
Read 'Data Science for Business'
Show steps
Provides a comprehensive overview of data science concepts and techniques.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Summarize the key concepts of each chapter.
-
Answer the review questions at the end of each chapter.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow tutorials on data visualization techniques
Show steps
Provides hands-on experience with data visualization tools and techniques covered in the course.
Browse courses on
Data Visualization
Show steps
-
Identify a dataset to visualize
-
Choose a data visualization tool like Tableau or Power BI
-
Follow a tutorial on how to create a visualization using the chosen tool
-
Interpret the results of the visualization
Complete the 'Python for Data Science' tutorial
Show steps
Develops proficiency in Python programming, a key skill for data analysis.
Browse courses on
Python Programming
Show steps
-
Follow the steps in the tutorial.
-
Complete all the exercises in the tutorial.
-
Create a small data analysis project using Python.
Practice SQL queries
Show steps
Reinforces basic SQL syntax and query techniques covered in the course.
Browse courses on
SQL
Show steps
-
Install a database management system like PostgreSQL or MySQL
-
Create a database and populate it with sample data
-
Execute a variety of SQL queries to retrieve and manipulate data
-
Review the results of the queries and identify any errors
Solve practice problems on data analysis
Show steps
Strengthens problem-solving skills and improves understanding of data analysis concepts.
Browse courses on
Data Analysis
Show steps
-
Find practice problems online or in textbooks.
-
Solve the problems using the techniques learned in the course.
-
Review your solutions and identify areas for improvement.
Write a blog post on a data analysis topic
Show steps
Summarizes and reinforces key concepts of data analysis.
Browse courses on
Data Analysis
Show steps
-
Choose a topic that you are familiar with and passionate about.
-
Research the topic thoroughly.
-
Write a clear and concise blog post that explains the topic to a non-technical audience.
-
Promote your blog post on social media and other channels.
Design and implement a data analysis project
Show steps
Applies the knowledge and skills gained in the course to a practical project.
Browse courses on
Data Analysis
Show steps
-
Identify a real-world dataset
-
Develop a research question or hypothesis
-
Clean and prepare the data
-
Analyze the data using appropriate machine learning algorithms
-
Present the results of the analysis
Develop a data visualization dashboard
Show steps
Applies data analysis skills to create effective data visualizations.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and identify the key insights you want to communicate.
-
Select appropriate data visualization techniques.
-
Use a data visualization tool to create the dashboard.
-
Present your dashboard to a small group and gather feedback.
Participate in a data science competition
Show steps
Provides practical experience in applying data analysis skills to real-world problems.
Browse courses on
Kaggle
Show steps
-
Choose a competition that aligns with your interests and skill level.
-
Download the dataset and familiarize yourself with the problem statement.
-
Develop and implement a data analysis solution.
-
Submit your solution and track your progress on the leaderboard.
Form a study group with classmates
Show steps
Fosters collaboration, improves understanding through peer-to-peer discussions.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find a group of classmates with complementary skills and interests.
- Set regular meeting times and stick to them.
- Discuss course material, work on assignments, and prepare for exams together.
- Support and encourage each other.
Review linear algebra
Show steps
Refreshes foundational knowledge of linear algebra necessary for understanding data analysis topics.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of vectors, matrices, and linear transformations.
- Practice solving systems of linear equations.
- Review the basics of matrix operations, such as addition, subtraction, multiplication, and inverse.
- Practice finding eigenvalues and eigenvectors of matrices.
Read 'Data Science for Business'
Show steps
Provides a comprehensive overview of data science concepts and techniques.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Summarize the key concepts of each chapter.
- Answer the review questions at the end of each chapter.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow tutorials on data visualization techniques
Show steps
Provides hands-on experience with data visualization tools and techniques covered in the course.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a dataset to visualize
- Choose a data visualization tool like Tableau or Power BI
- Follow a tutorial on how to create a visualization using the chosen tool
- Interpret the results of the visualization
Complete the 'Python for Data Science' tutorial
Show steps
Develops proficiency in Python programming, a key skill for data analysis.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Follow the steps in the tutorial.
- Complete all the exercises in the tutorial.
- Create a small data analysis project using Python.
Practice SQL queries
Show steps
Reinforces basic SQL syntax and query techniques covered in the course.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Install a database management system like PostgreSQL or MySQL
- Create a database and populate it with sample data
- Execute a variety of SQL queries to retrieve and manipulate data
- Review the results of the queries and identify any errors
Solve practice problems on data analysis
Show steps
Strengthens problem-solving skills and improves understanding of data analysis concepts.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Solve the problems using the techniques learned in the course.
- Review your solutions and identify areas for improvement.
Write a blog post on a data analysis topic
Show steps
Summarizes and reinforces key concepts of data analysis.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a topic that you are familiar with and passionate about.
- Research the topic thoroughly.
- Write a clear and concise blog post that explains the topic to a non-technical audience.
- Promote your blog post on social media and other channels.
Design and implement a data analysis project
Show steps
Applies the knowledge and skills gained in the course to a practical project.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a real-world dataset
- Develop a research question or hypothesis
- Clean and prepare the data
- Analyze the data using appropriate machine learning algorithms
- Present the results of the analysis
Develop a data visualization dashboard
Show steps
Applies data analysis skills to create effective data visualizations.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and identify the key insights you want to communicate.
- Select appropriate data visualization techniques.
- Use a data visualization tool to create the dashboard.
- Present your dashboard to a small group and gather feedback.
Participate in a data science competition
Show steps
Provides practical experience in applying data analysis skills to real-world problems.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Choose a competition that aligns with your interests and skill level.
- Download the dataset and familiarize yourself with the problem statement.
- Develop and implement a data analysis solution.
- Submit your solution and track your progress on the leaderboard.
Career center
Learners who complete Обработка больших данных will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts play a key role in developing strategies for collecting, cleaning, and analyzing large datasets. Taking this course will help you build a foundation in the technologies used to store and process these datasets. The course will also discuss current analytical best practices, which Data Analysts can leverage to produce quality insights.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to build models that can predict future outcomes. The technologies and tools covered in this course are essential for Data Scientists to efficiently handle large datasets and build models at scale. Taking this course will help you gain hands-on experience with these technologies and build a portfolio of projects that you can showcase to potential employers.
Big Data Architect
Big Data Architect
Big Data Architects design and implement systems for storing and processing large datasets. This course will provide you with knowledge of the different technologies and techniques used in Big Data Architecture. Taking this course will help you stay up-to-date on the latest Big Data technologies and trends.
Business Intelligence Manager
Business Intelligence Manager
Business Intelligence (BI) Managers oversee the collection, analysis, and reporting of data within an organization. The insights generated by BIs are used to support decision-making and improve business outcomes. Taking this course will give you the skills needed to manage and interpret large datasets and produce data-driven insights.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure used to store and process large datasets. The technologies covered in this course are essential for Data Engineers to build and maintain reliable and scalable data pipelines. Taking this course will help you gain the skills and knowledge needed to succeed as a Data Engineer.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning (ML) Engineers use their knowledge of ML algorithms and techniques to build and deploy ML models. The technologies and tools covered in this course are essential for ML Engineers to efficiently handle large datasets and build ML models at scale. Taking this course will help you gain hands-on experience with these technologies and tools and build a portfolio of projects to showcase to potential employers.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to help stakeholders understand complex datasets. The insights generated through data visualization are used to support decision-making, improve customer experience, and identify trends and patterns. Taking this course will help you gain the skills needed to create effective data visualizations.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts develop and implement policies and procedures to ensure the accuracy, consistency, and security of data within an organization. Taking this course will help you gain the skills and knowledge needed to manage data effectively and ensure compliance with industry regulations.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course will give you the knowledge and skills needed to manage large datasets and protect the integrity of an organization's data. Taking this course may help you stand out in your role by providing expertise in big data technologies.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. The technologies and tools covered in this course are becoming increasingly important for Quantitative Analysts as financial institutions look to leverage big data to gain a competitive advantage. Taking this course may help you stand out in your role by providing expertise in big data technologies.
Software Developer
Software Developer
Software Developers design and build software applications. This course will give you the skills and knowledge needed to develop software applications that can handle large datasets. Taking this course may help you stand out in your role by providing expertise in big data technologies.
Enterprise Architect
Enterprise Architect
Enterprise Architects design and manage the technology infrastructure of an organization. This course will give you the knowledge and skills needed to design and manage large-scale data systems. Taking this course may help you stand out in your role by providing expertise in big data technologies.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products and services. This course will give you the skills and knowledge needed to analyze customer data and develop products that meet their needs. Taking this course may help you stand out in your role by providing expertise in big data technologies.
Marketer
Marketer
Marketers develop and execute marketing campaigns. This course will give you the skills and knowledge needed to analyze marketing data and develop effective campaigns. Taking this course may help you stand out in your role by providing expertise in big data technologies.
Sales Manager
Sales Manager
Sales Managers lead and oversee sales teams. This course will give you the skills and knowledge needed to analyze sales data and develop effective sales strategies. Taking this course may help you stand out in your role by providing expertise in big data technologies.
For more career information including salaries, visit:
OpenCourser.com/course/0ncrel/obrabotka
Reading list
We've selected nine 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
Обработка больших данных.
Provides a practical introduction to data science for business professionals. It covers topics such as data collection, analysis, and visualization.
Provides a comprehensive overview of the Hadoop ecosystem, including topics such as HDFS, MapReduce, and Hive.
Provides a comprehensive overview of the Spark framework, including topics such as Spark SQL, Spark Streaming, and Spark MLlib.
Provides a practical introduction to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and deep learning.
Save
Provides a comprehensive overview of deep learning, including topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Save
Provides a comprehensive overview of data mining, including topics such as data preprocessing, clustering, and classification.
Provides a comprehensive overview of data warehousing, including topics such as data modeling, ETL, and data quality.
Provides a comprehensive overview of dimensional modeling, including topics such as star schemas, snowflake schemas, and bridge tables.
Provides a practical introduction to big data analytics for beginners. It covers topics such as data collection, data storage, and data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0ncrel/obrabotka
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Time
24 hours
Via
Coursera
Institution
HSE University
Instructors
Космачев Алексей Дмитриевич
Бардуков Анатолий Андреевич
Зимовнов Андрей Вадимович
This course belongs to a
collection
called
Промышленное машинное обучение. It's comprised of three courses:
- Обработка больших данных (viewing)
- Проектирование и реализация систем машинного обучения
- Машинное обучение на больших данных
Good to know
Learn advanced technologies for working with large datasets
Taught by renowned instructors Zimovnov, Kosmachev, and Bardukov
Covers methods for storing, processing, and analyzing big data
Strengthens understanding of data sources and collection techniques
Provides a solid foundation for working with big data
Some prerequisite knowledge in data analysis may be beneficial
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