Discrete Math and Analyzing Social Graphs from Coursera

The main goal of this online course is to introduce topics in Discrete Mathematics relevant to Data Analysis. We will start with a brief introduction to combinatorics, the branch of mathematics that studies how to count. Basics of this topic are critical for anyone working in Data Analysis or Computer Science. We will illustrate new knowledge, for example, by counting the number of features in data or by estimating the time required for a Python program to run. Next, we will apply our knowledge in combinatorics to study basic Probability Theory. Probability is everywhere in Data Analysis and we will study it in much more details later. Our goals for probability section in this course will be to give initial flavor of this field. Finally, we will study the combinatorial structure that is the most relevant for Data Analysis, namely graphs. Graphs can be found everywhere around us and we will provide you with numerous examples. We will mainly concentrate in this course on the graphs of social networks. We will provide you with relevant notions from the graph theory, illustrate them on the graphs of social networks and will study their basic properties. In the end of the course we will have a project related to social network graphs. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in Python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in Data Analysis, starting from motivated high school students. This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.

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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

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

Be better prepared 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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Gather notes, assignments, and other relevant materials.
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.
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.
Solve the problems or exercises.
Run code snippets or examples to verify solutions.

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Follow guided tutorials on Probability

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Reinforce concepts and gain deeper understanding of Probability through guided tutorials.

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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

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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.
Identify topics for discussion and prepare.
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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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

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Apply Graph Theory concepts to a practical problem by analyzing a real-world dataset.

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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.

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Find coding competitions focused on Graph Theory.
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Career center

Learners who complete Discrete Math and Analyzing Social Graphs will develop knowledge and skills that may be useful to these careers:

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Risk Analyst

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.

See salaries and explore the career path for Data Engineer

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.

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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.

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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.

See salaries and explore the career path for Computer Scientist

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.

See salaries and explore the career path for Economist

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.

See salaries and explore the career path for Investment Analyst