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Vladimir Podolskii,
Ilya V. Schurov,
Stepan Kuznetsov,
and
Владимир Подольский
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Combinatorics
Probability Theory
Graphs
Social Networks
Python
Algorithms
Data Analysis
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Covers Discrete Mathematics topics relevant to Data Analysis, catering to individuals seeking a solid foundation in this area
Aimed at individuals working or planning to work in Data Analysis, including high school students with strong motivation
Taught by instructors with expertise in Data Analysis, Mathematics, and Computer Science
Prerequisites are basic math, Python programming, and a curious mind, ensuring accessibility to a wide audience
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Reviews summary
Discrete math concepts for data analysis
This course provides a comprehensive introduction to the mathematical concepts used in Data Analysis, including combinatorics, probability theory, and graph theory. With a focus on social network graphs, the course offers a solid foundation for professionals in the field. The instructors are highly knowledgeable and provide clear explanations, making the course accessible to learners with varying backgrounds. Overall, this course is highly recommended for those seeking to enhance their understanding of the mathematical underpinnings of Data Science.
Appropriate level of difficulty; requires effort but is not overwhelming.
"...but manageable...The information in the course material relates enough to the exams, so if you understand the concepts, the assessment is quite doable."
Relevant examples and applications in social network analysis.
"...and gives you numerous examples...We will mainly concentrate in this course on the graphs of social networks."
Practical Python coding assignments reinforce concepts.
"The python codes are a joy to read and practice..."
Instructors provide clear and well-paced explanations.
"The videos and slides and exercises were very well done."
"The classes' length was appropriate."
Good for building a strong foundation in mathematics for data science.
"This is an essential course for anyone looking to build up a strong foundation for mathematics for data science."
Final programming assignment has unclear submission requirements and is not well-structured.
"The final coding project was a complete joke."
"i don't know how some people come with idea that combinatorics and graph theory are taught by some slides. very ridiculous decision because you can't teach mathematics without interactive writing."
Additional visualizations and examples could improve understanding.
"...I (personally) find visualizations a good way in."
"The questions sometimes presented things which were a bit less explained than they might have been..."
Occasional lack of clarity in explanations or assignments.
"First part of the course (Combinatorics) is not so interesting as the others due to not-very-good English of lecturer (sometimes he's not-understandable, sometimes the speach is monotonous)."
"Lectures, quizzes and assignment descriptions are all lack of clarity."
Activities
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before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Discrete Math and Analyzing Social Graphs with these
activities:
Organize and review course materials for Combinatorics
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Stay on top of the course material by organizing, reviewing, and summarizing important concepts.
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Combinatorics
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Gather notes, assignments, and other relevant materials.
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Review and summarize key concepts.
Review a book on Probability Theory
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Enhance understanding of Probability Theory by studying concepts in a comprehensive textbook.
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Read through the book and take notes on key concepts.
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Summarize the main ideas and principles.
Solve practice problems and run code snippets for Combinatorics
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Practice solving problems to solidify understanding of Combinatorics and prepare for working with real-world data.
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Find practice problems or exercises on Combinatorics.
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Follow guided tutorials on Probability
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Follow the tutorials and complete the exercises.
Participate in peer-learning sessions on Combinatorics
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Collaborate with peers to discuss, reinforce, and clarify understanding of Combinatorics.
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Find or form a peer-learning group with shared interests in Combinatorics.
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Identify topics for discussion and prepare.
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Participate actively in discussions and share perspectives.
Attend a workshop on Data Analysis with Graph Theory
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Deepen understanding of how Graph Theory concepts are applied in real-world Data Analysis.
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Graph Theory
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Identify and register for a workshop on Data Analysis with Graph Theory.
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Attend the workshop and take notes on key insights.
Build a Python program to analyze a real-world dataset using Graph Theory concepts
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Apply Graph Theory concepts to a practical problem by analyzing a real-world dataset.
Browse courses on
Graph Theory
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Choose a suitable real-world dataset for analysis.
-
Identify the relevant concepts from Graph Theory to apply.
-
Design and write a Python program to perform the analysis.
Participate in coding competitions on Graph Theory
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Test and sharpen Graph Theory skills by participating in competitive coding challenges.
Browse courses on
Graph Theory
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-
Find coding competitions focused on Graph Theory.
-
Practice solving problems and prepare solutions.
-
Participate in the competition.
Organize and review course materials for Combinatorics
Show steps
Stay on top of the course material by organizing, reviewing, and summarizing important concepts.
Browse courses on
Combinatorics
Show steps
Show steps
Show steps
- Gather notes, assignments, and other relevant materials.
- Review and summarize key concepts.
Review a book on Probability Theory
Show steps
Enhance understanding of Probability Theory by studying concepts in a comprehensive textbook.
Show steps
Show steps
Show steps
- Read through the book and take notes on key concepts.
- Summarize the main ideas and principles.
Solve practice problems and run code snippets for Combinatorics
Show steps
Practice solving problems to solidify understanding of Combinatorics and prepare for working with real-world data.
Browse courses on
Combinatorics
Show steps
Show steps
Show steps
- Find practice problems or exercises on Combinatorics.
- Solve the problems or exercises.
- Run code snippets or examples to verify solutions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow guided tutorials on Probability
Show steps
Reinforce concepts and gain deeper understanding of Probability through guided tutorials.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Identify topics in Probability that need reinforcement.
- Find guided tutorials or online resources on those topics.
- Follow the tutorials and complete the exercises.
Participate in peer-learning sessions on Combinatorics
Show steps
Collaborate with peers to discuss, reinforce, and clarify understanding of Combinatorics.
Browse courses on
Combinatorics
Show steps
Show steps
Show steps
- Find or form a peer-learning group with shared interests in Combinatorics.
- Identify topics for discussion and prepare.
- Participate actively in discussions and share perspectives.
Attend a workshop on Data Analysis with Graph Theory
Show steps
Deepen understanding of how Graph Theory concepts are applied in real-world Data Analysis.
Browse courses on
Graph Theory
Show steps
Show steps
Show steps
- Identify and register for a workshop on Data Analysis with Graph Theory.
- Attend the workshop and take notes on key insights.
Build a Python program to analyze a real-world dataset using Graph Theory concepts
Show steps
Apply Graph Theory concepts to a practical problem by analyzing a real-world dataset.
Browse courses on
Graph Theory
Show steps
Show steps
Show steps
- Choose a suitable real-world dataset for analysis.
- Identify the relevant concepts from Graph Theory to apply.
- Design and write a Python program to perform the analysis.
Participate in coding competitions on Graph Theory
Show steps
Test and sharpen Graph Theory skills by participating in competitive coding challenges.
Browse courses on
Graph Theory
Show steps
Show steps
Show steps
- Find coding competitions focused on Graph Theory.
- Practice solving problems and prepare solutions.
- Participate in the competition.
Career center
Learners who complete Discrete Math and Analyzing Social Graphs will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect and analyze data to help businesses make informed decisions. This course provides a strong foundation in combinatorics, probability theory, and graph theory, which are essential skills for Data Analysts. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with social data.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course provides a solid foundation in the mathematical principles that underlie data science, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is an important area of application for Data Scientists.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a strong foundation in combinatorics, probability theory, and graph theory, which are essential skills for Quantitative Analysts. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with financial data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency of business operations. This course provides a solid foundation in the mathematical principles that underlie operations research, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with complex systems.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. This course provides a strong foundation in combinatorics, probability theory, and graph theory, which are essential skills for Actuaries. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with insurance data.
Statistician
Statistician
Statisticians use data to understand the world around us. This course provides a strong foundation in combinatorics, probability theory, and graph theory, which are essential skills for Statisticians. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with social data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course provides a strong foundation in the mathematical principles that underlie machine learning, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with social data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a strong foundation in the mathematical principles that underlie software engineering, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with complex systems.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and analyze algorithms and data structures. This course provides a strong foundation in the mathematical principles that underlie computer science, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with complex systems.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course provides a strong foundation in the mathematical principles that underlie data engineering, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with large datasets.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial markets and make investment decisions. This course provides a strong foundation in the mathematical principles that underlie financial analysis, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with financial data.
Investment Analyst
Investment Analyst
Investment Analysts use data to analyze investment opportunities and make investment decisions. This course provides a strong foundation in the mathematical principles that underlie investment analysis, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with financial data.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess and mitigate risk. This course provides a strong foundation in the mathematical principles that underlie risk analysis, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with risk data.
Business Analyst
Business Analyst
Business Analysts use data to understand business problems and make recommendations for improvement. This course provides a strong foundation in the mathematical principles that underlie business analysis, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with business data.
Economist
Economist
Economists use data to understand economic trends and make predictions about the future. This course provides a strong foundation in the mathematical principles that underlie economics, including combinatorics, probability theory, and graph theory. The course also includes a project related to social network graphs, which is a valuable experience for those interested in working with economic data.
For more career information including salaries, visit:
OpenCourser.com/course/bhsezi/discrete
Reading list
We've selected 12 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
Discrete Math and Analyzing Social Graphs.
Classic textbook on discrete mathematics that covers a wide range of topics, including combinatorics, graph theory, and probability. It valuable reference for anyone interested in learning more about the mathematical foundations of data analysis.
Provides a comprehensive introduction to social network analysis. It covers the essential concepts of social network analysis, including social network structure, social network dynamics, and social network analysis methods. It valuable resource for anyone who wants to learn more about the mathematical foundations of social network analysis.
Provides a comprehensive introduction to data science for business. It covers the essential concepts of data science, including data collection, data cleaning, data analysis, and data visualization. It valuable resource for anyone who wants to learn more about the practical applications of data science in business.
Provides a comprehensive introduction to Python for data analysis. It covers the essential concepts of Python for data analysis, including data structures, data manipulation, and data visualization. It valuable resource for anyone who wants to learn more about the practical applications of Python in data analysis.
Provides a comprehensive introduction to machine learning for data science. It covers the essential concepts of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about the practical applications of machine learning in data science.
Save
Provides a comprehensive introduction to deep learning. It covers the essential concepts of deep learning, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn more about the practical applications of deep learning in data science.
Save
Provides a comprehensive introduction to probability theory. It covers the essential concepts of probability theory, including random variables, probability distributions, and statistical inference. It valuable resource for anyone who wants to learn more about the mathematical foundations of data analysis.
Provides a comprehensive introduction to statistical inference. It covers the essential concepts of statistical inference, including point estimation, hypothesis testing, and confidence intervals. It valuable resource for anyone who wants to learn more about the mathematical foundations of data analysis.
Provides a comprehensive introduction to regression analysis. It covers the essential concepts of regression analysis, including linear regression, multiple regression, and logistic regression. It valuable resource for anyone who wants to learn more about the mathematical foundations of data analysis.
Save
Provides a comprehensive introduction to time series analysis. It covers the essential concepts of time series analysis, including time series models, time series forecasting, and time series analysis methods. It valuable resource for anyone who wants to learn more about the mathematical foundations of data analysis.
Save
Provides a comprehensive introduction to cluster analysis. It covers the essential concepts of cluster analysis, including clustering algorithms, cluster validation, and cluster interpretation. It valuable resource for anyone who wants to learn more about the mathematical foundations of data analysis.
Provides a comprehensive introduction to discriminant analysis. It covers the essential concepts of discriminant analysis, including discriminant functions, discriminant analysis methods, and discriminant analysis interpretation. It valuable resource for anyone who wants to learn more about the mathematical foundations of data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bhsezi/discrete
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Via
Coursera
Institutions
National Research University Higher School of Economics
HSE University
Instructors
Vladimir Podolskii
Ilya V. Schurov
Stepan Kuznetsov
Владимир Подольский
This course belongs to a
collection
called
Mathematics for Data Science. It's comprised of four courses:
- First Steps in Linear Algebra for Machine Learning
- Discrete Math and Analyzing Social Graphs (viewing)
- Calculus and Optimization for Machine Learning
- Probability Theory, Statistics and Exploratory Data Analysis
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