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Alex Aklson and Polong Lin
Несмотря на то, что в последние десятилетия существенно выросли вычислительные возможности и доступ к данным, наша способность использовать эти данные для принятия решений либо снижается, либо не используется с максимальной эффективностью. Чаще всего у нас отсутствует глубокие знания о задаваемых вопросах и том, как правильно применять данные для решения определенной проблемы. У этого курса всего одна задача, и она заключается в том, чтобы познакомить вас с методами, которые можно использовать при обработке и анализе данных, чтобы убедиться в том, что используемые для решения проблемы данные правильно подобраны и обработаны для...
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Несмотря на то, что в последние десятилетия существенно выросли вычислительные возможности и доступ к данным, наша способность использовать эти данные для принятия решений либо снижается, либо не используется с максимальной эффективностью. Чаще всего у нас отсутствует глубокие знания о задаваемых вопросах и том, как правильно применять данные для решения определенной проблемы. У этого курса всего одна задача, и она заключается в том, чтобы познакомить вас с методами, которые можно использовать при обработке и анализе данных, чтобы убедиться в том, что используемые для решения проблемы данные правильно подобраны и обработаны для поиска ответа на конкретный вопрос. Соответственно, из этого курса вы узнаете о: - основных этапах решения проблемы, связанной с обработкой и анализом данных; - основных этапах обработки и анализа данных: от формулирования проблемы конкретной компании или исследования до сбора и анализа данных, построения модели и обработки обратной связи после внедрения модели; - том, как размышляют аналитики данных. ВРЕМЕННАЯ АКЦИЯ: приобретите подписку всего за 39 долл. США в месяц и получите доступ к отсортированным материалам и сертификат.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines how data can be applied to business problems
Introduces data modeling and analytics
Teaches critical thinking and analytical reasoning in context of data analysis
Could be useful for beginners and intermediate learners alike
Taught by seasoned instructors
Includes hands-on labs to reinforce learning

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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 Методология обработки и анализа данных with these activities:
Review statistical concepts
Refresh your knowledge of statistical concepts to prepare for this course's emphasis on data analysis.
Browse courses on Statistical Concepts
Show steps
  • Review notes or textbooks on basic statistical concepts, such as mean, median, and standard deviation.
  • Take practice quizzes or online tests to assess your understanding.
Learn Python for data analysis
Solidify your understanding of Python programming, a foundational tool for data analysis in this course.
Browse courses on Python
Show steps
  • Complete online tutorials or courses on Python basics.
  • Practice writing Python code for data manipulation and analysis.
Practice data cleaning and manipulation
Develop proficiency in data cleaning and manipulation, essential skills for working with real-world datasets.
Browse courses on Data Cleaning
Show steps
  • Find practice datasets online or create your own.
  • Use Python or other tools to clean and manipulate the data.
Two other activities
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Build a data visualization project
Apply your knowledge of data analysis by creating a data visualization project that communicates insights from a dataset.
Browse courses on Data Visualization
Show steps
  • Choose a dataset and identify the key insights you want to convey.
  • Select appropriate visualization techniques and tools.
  • Create the visualization and refine it based on feedback.
Tutor peers in data analysis concepts
Reinforce your understanding of data analysis by helping others learn these concepts.
Browse courses on Mentoring
Show steps
  • Identify opportunities to tutor peers or participate in study groups.
  • Prepare materials and practice explaining data analysis concepts clearly.

Career center

Learners who complete Методология обработки и анализа данных will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts use their knowledge of data analysis tools and techniques to help businesses make better decisions. They collect, clean, and analyze data from a variety of sources to identify trends and patterns. This course will help you develop the skills you need to be a successful Data Analyst, including data collection, analysis, and visualization. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Data Scientist
Data Scientists use their knowledge of data science techniques to solve complex problems. They develop and implement machine learning models to predict outcomes and identify opportunities. This course will help you develop the skills you need to be a successful Data Scientist, including data science techniques, machine learning, and statistical modeling. You will also learn how to apply your skills to real-world problems.
Business Analyst
Business Analysts use their knowledge of business and data analysis to help businesses improve their operations. They identify opportunities for improvement and develop plans to implement those improvements. This course will help you develop the skills you need to be a successful Business Analyst, including data analysis, process improvement, and project management. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. They work with researchers, businesses, and other organizations to provide insights into data. This course will help you develop the skills you need to be a successful Statistician, including data analysis, statistical modeling, and data visualization. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Data Architect
Data Architects use their knowledge of data architecture techniques to design and implement data systems. They work with data engineers, analysts, and other stakeholders to ensure that data systems are scalable, reliable, and secure. This course will help you develop the skills you need to be a successful Data Architect, including data architecture, data modeling, and data security. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Risk Analyst
Risk Analysts use their knowledge of risk management techniques to identify and assess risks. They develop and implement plans to mitigate those risks. This course will help you develop the skills you need to be a successful Risk Analyst, including data analysis, risk assessment, and project management. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Product Manager
Product Managers use their knowledge of product management techniques to develop and launch new products. They work with engineers, designers, and marketers to create products that meet customer needs. This course will help you develop the skills you need to be a successful Product Manager, including data analysis, product development, and project management. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Data Engineer
Data Engineers use their knowledge of data engineering techniques to build and maintain data systems. They work with data scientists, analysts, and other stakeholders to ensure that data is available and accessible. This course will help you develop the skills you need to be a successful Data Engineer, including data engineering, data warehousing, and data management. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Data Visualization Specialist
Data Visualization Specialists use their knowledge of data visualization techniques to create visual representations of data. They work with data analysts, scientists, and other stakeholders to communicate insights from data. This course will help you develop the skills you need to be a successful Data Visualization Specialist, including data visualization, data analysis, and design. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics and statistics to analyze financial data. They develop and implement models to predict risk and return. This course will help you develop the skills you need to be a successful Quantitative Analyst, including data analysis, statistical modeling, and financial modeling. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Market Researcher
Market Researchers use their knowledge of market research techniques to understand customer needs and preferences. They conduct surveys, focus groups, and other research methods to collect data about customer behavior. This course will help you develop the skills you need to be a successful Market Researcher, including data analysis, survey design, and focus group facilitation. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Chief Data Officer
Chief Data Officers use their knowledge of data management techniques to oversee the use of data within an organization. They work with senior executives and other stakeholders to ensure that data is used to drive decision-making. This course will help you develop the skills you need to be a successful Chief Data Officer, including data management, data governance, and data strategy. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Data Privacy Officer
Data Privacy Officers use their knowledge of data privacy regulations to ensure that an organization complies with data privacy laws. They work with legal counsel and other stakeholders to develop and implement policies and procedures to protect data privacy. This course will help you develop the skills you need to be a successful Data Privacy Officer, including data privacy law, data security, and data ethics. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Data Scientist Manager
Data Scientist Managers use their knowledge of data science management techniques to lead teams of data scientists. They work with senior executives and other stakeholders to ensure that data science projects are aligned with business goals. This course will help you develop the skills you need to be a successful Data Scientist Manager, including data science management, team leadership, and project management. You will also learn how to communicate your findings to stakeholders in a clear and concise way.
Machine Learning Engineer
Machine Learning Engineers use their knowledge of machine learning techniques to develop and implement machine learning models. They work with data scientists and other stakeholders to ensure that machine learning models are accurate and reliable. This course will help you develop the skills you need to be a successful Machine Learning Engineer, including data science, machine learning, and software engineering. You will also learn how to communicate your findings to stakeholders in a clear and concise way.

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 Методология обработки и анализа данных.
This widely used textbook provides a rigorous foundation in statistical learning, covering topics such as regression, classification, and clustering. It offers a theoretical perspective and practical examples, making it a valuable reference for those seeking a deeper understanding of data analysis concepts.
Practical guide to using Python for data analysis, covering topics such as data loading, cleaning, and manipulation. It provides a comprehensive overview of Python libraries commonly used in data science, making it a valuable resource for beginners.
This hands-on guide focuses on implementing machine learning algorithms using popular Python libraries. Covering supervised and unsupervised learning, it provides a practical approach to applying data science techniques to real-world problems.
Provides a comprehensive overview of predictive modeling, covering various techniques and algorithms used in data science. It offers practical guidance on model selection, evaluation, and deployment, making it a valuable resource for those interested in building predictive models.
Serves as an easy-to-read and practical guide to data analysis, making it suitable for beginners with little or no technical background. It covers essential concepts such as data collection, preparation, analysis, and visualization, providing a solid foundation for further learning.
Provides a practical guide to data visualization, covering various techniques and best practices for presenting data effectively. It offers insights into how to choose appropriate visualizations, design clear and compelling graphics, and effectively communicate data insights.
Focuses on interpretable machine learning techniques, which aim to make complex models more understandable and explainable. It provides practical guidance and examples on how to interpret and communicate the results of data analysis in a clear and meaningful way.
Explores data-intensive text processing using MapReduce, a distributed computing framework. It covers techniques for text mining, natural language processing, and machine learning, providing a specialized perspective on data analysis for text-based data.
Explores advanced data analysis techniques using Apache Spark, a powerful distributed computing framework. It covers topics such as data engineering, machine learning, graph processing, and stream processing, providing insights into scalable and efficient data analysis.
Introduces Bayesian analysis, a statistical approach that combines prior knowledge with observed data to make inferences. It provides a detailed explanation of Bayesian concepts and techniques, making it suitable for those with a solid foundation in statistics.

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