Статистическое мышление from Coursera

Цель: Сформировать у слушателей статистическую парадигму восприятия действительности (в качестве альтернативы причинной), а также навыки для успешной деятельности в современном мире, имеющем статистическую (вероятностную) природу. Задачи: • сформировать у слушателей понимание базовых концепций статистики, теории вероятностей, теории информации, обработки больших данных, планирования и оценки результатов эксперимента; • сформировать у слушателей базовые навыки обработки данных в Excel; • сформировать у слушателей базовые навыки презентации итогов анализа средствами Excel; • сформировать у слушателей навыки сбора данных, и...

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Develops statistical cognition and critical thinking skills, which are essential for making informed decisions in an era of uncertainty and big data

Provides hands-on experience with Excel, a widely used tool for data analysis and presentation

Taught by instructors from MFTI-ROSNANOTechnological Entrepreneurship Department and "eNano" whose research and expertise enhances the course content

Effective for professionals seeking to enhance their data analysis and decision-making capabilities

Builds a foundational understanding of statistics, probability, and data processing

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 Статистическое мышление with these activities:

Read articles on statistics and data analysis

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Reading articles on statistics and data analysis will help to refresh prior knowledge and provide new perspectives.

Browse courses on Statistics

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Identify reputable sources for articles on statistics and data analysis.
Read articles on topics that are relevant to the course.
Summarize the key points of the articles.

Review basic probability theory

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Reviewing basic probability theory will help to refresh prior knowledge and ensure a solid foundation for the course.

Browse courses on Probability Theory

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Revisit your notes or textbooks from previous courses on probability theory.
Solve practice problems to test your understanding.

Complete online tutorials on Excel data analysis

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Completing online tutorials will help to develop proficiency in Excel, which is essential for data analysis in the course.

Browse courses on Excel

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Find online tutorials on Excel data analysis.
Follow the tutorials step-by-step.
Practice using the techniques learned in the tutorials.

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Solve probability problems

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Practice solving probability problems to improve understanding of probability concepts.

Browse courses on Probability Distributions

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Review probability concepts from the course materials.
Find practice problems online or in textbooks.
Solve the problems and check your answers.

Participate in a study group

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Engaging in discussions and sharing knowledge with peers helps to reinforce learning and improve critical thinking skills.

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Find or create a study group with other students in the course.
Meet regularly to discuss course materials, solve problems, and share insights.

Create a data visualization

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Creating a data visualization helps to solidify understanding of statistical concepts and improve communication skills.

Browse courses on Data Visualization

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Choose a dataset and a visualization type.
Prepare the data for visualization.
Create the visualization using visualization software.
Present the visualization and explain the results.

Write a blog post on a statistical topic

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Writing a blog post helps to improve understanding of statistical concepts and develop writing skills.

Browse courses on Statistics

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Choose a statistical topic that you are interested in.
Research the topic thoroughly.
Write the blog post in a clear and concise manner.
Publish the blog post on a platform of your choice.

Participate in data analysis competitions

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Participating in data analysis competitions puts skills to the test and helps to develop problem-solving abilities.

Browse courses on Data Analysis

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Find data analysis competitions that are relevant to the course.
Form a team or compete individually.
Analyze the data, develop a solution, and present the results.

Career center

Learners who complete Статистическое мышление will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. This course in Statistical Thinking provides a strong foundation in these areas by covering topics such as probability theory, statistical inference, and data visualization. By gaining proficiency in these subjects, learners can enhance their ability to gather and interpret data, making them well-suited for a career as a Statistician.

See salaries and explore the career path for Statistician

Biostatistician

Biostatisticians apply statistical methods to solve problems in the field of medicine and healthcare. This course in Statistical Thinking provides a strong foundation for this role by introducing learners to the fundamentals of probability, statistical inference, and data analysis. By gaining proficiency in these areas, Biostatisticians can effectively analyze medical data, design clinical trials, and contribute to the development of new treatments and therapies.

See salaries and explore the career path for Biostatistician

Quantitative Analyst

Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment decisions. This course in Statistical Thinking can provide a solid foundation for this role by introducing learners to the fundamentals of probability, statistical inference, and data analysis. By gaining proficiency in these areas, Quants can develop the skills needed to build and validate financial models, analyze market trends, and make informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Epidemiologist

Epidemiologists investigate the causes and patterns of health-related events in populations. This course in Statistical Thinking provides a solid foundation for this role by covering concepts such as probability, statistical inference, and data analysis. By gaining proficiency in these areas, learners can develop the skills needed to analyze and interpret health data, identify risk factors, and make evidence-based recommendations for improving public health.

See salaries and explore the career path for Epidemiologist

Data Analyst

Data Analysts collect, clean, and analyze data to provide insights for businesses. This course in Statistical Thinking can be highly beneficial for Data Analysts by providing a comprehensive foundation in statistical concepts, data visualization techniques, and data analysis methods. Mastering these skills can enable Data Analysts to effectively extract meaningful insights from data, identify patterns, and make data-driven recommendations.

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Data Scientist

Data Scientists apply a foundational understanding of probability and statistics to analyze trends, forecast future scenarios, and make predictions for businesses. This course in Statistical Thinking helps build a foundation for these concepts through its exploration of probability theory, statistical analysis, and data visualization in Excel. Studying the skills taught in this course could lead to success as a Data Scientist by providing the theoretical background and practical knowledge needed to excel in the field.

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Market Researcher

Market Researchers gather and analyze data about customer behavior, market trends, and competitor strategies to inform marketing decisions. This course in Statistical Thinking can greatly benefit Market Researchers by equipping them with the statistical knowledge and data analysis skills necessary to conduct effective market research. Understanding concepts such as sampling, hypothesis testing, and data visualization can enhance their ability to collect and interpret data, leading to more informed decision-making.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve complex business problems. This course in Statistical Thinking can provide a strong foundation for this role by covering topics such as probability theory, optimization, and data analysis. By gaining proficiency in these areas, learners can develop the skills needed to analyze data, build mathematical models, and make recommendations for improving business operations.

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Actuary

Actuaries use statistical and mathematical methods to assess and manage financial risks. This course in Statistical Thinking can provide a solid foundation for this role by covering topics such as probability theory, statistical modeling, and data analysis. By gaining proficiency in these areas, learners can develop the skills needed to analyze data, quantify risks, and make informed decisions in the insurance and finance industries.

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Risk Manager

Risk Managers identify, assess, and mitigate risks within organizations. This course in Statistical Thinking can be highly beneficial for Risk Managers by providing a foundation in statistical concepts, data analysis techniques, and probability theory. Mastering these skills can enable Risk Managers to effectively evaluate risks, develop mitigation strategies, and make data-driven decisions to protect organizations from potential losses.

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Business Analyst

Business Analysts use data to analyze business operations and make recommendations for improvement. This course in Statistical Thinking provides a valuable foundation for this role by introducing learners to statistical concepts, data visualization techniques, and data analysis methods. Mastering these skills can empower Business Analysts to make data-driven decisions and contribute to the success of organizations.

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Financial Analyst

Financial Analysts evaluate and make recommendations on investment opportunities. This course in Statistical Thinking can provide a valuable foundation for this role by introducing learners to the concepts of probability, statistical inference, and data analysis. Proficiency in these areas can help Financial Analysts assess the risk and return of investments, make informed predictions, and develop effective investment strategies.

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Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models to solve real-world problems. This course in Statistical Thinking can provide a valuable foundation for this role by introducing learners to the fundamentals of probability, statistical inference, and data analysis. Proficiency in these areas can help Machine Learning Engineers understand the statistical principles underlying machine learning algorithms, develop effective models, and evaluate their performance.

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Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure. This course in Statistical Thinking may be useful for Data Engineers by providing a foundation in statistical concepts and data analysis techniques. Understanding these concepts can help Data Engineers understand the data they are working with, design efficient data pipelines, and ensure the quality and integrity of data.

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Software Engineer

Software Engineers design, develop, and maintain software systems. While this course in Statistical Thinking is not directly related to software engineering, the skills taught in this course, such as data analysis and problem-solving, may be transferable to this role. Software Engineers can benefit from understanding statistical concepts and data analysis techniques to improve the quality and performance of their software systems.

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