Network Analytics for Business 专项课程

Despite a large variety of different courses on analytics, the courses that offer a broad overview of the field are rare. From practice of teaching statistics, it became clear that it is difficult for learners to put together a broad field map if they have taken only a few of the different topics on analytical tools. As a result, they do not see the overall picture of everything that the field of data analysis has to offer.

This course is designed to fill this gap. It is a survey course on state-of-the-art in interdisciplinary methods of data analysis, applicable to business and academia alike. Unlike other statistical courses, which focus on specific methods, this course will focus on the broader areas within statistics and data analytics. There are five major topics it will cover. It will start with the root of it all - the data – and some of the problems with the data. Then it will move through the contemporary approaches to descriptive, inferential, predictive and prescriptive analytics. Within each broader topic, the course will offer the theoretical foundation behind the methods without focusing too much on the mathematics. It will provide historical references, examples, explanations and case studies to illustrate the main concepts within each broader topic. In doing so, it will introduce the applied, problem-based approach to using specific tools. Then, it will discuss some of the specific of a particular approach. Overall, after taking this course, the students will get a good understanding of the state-of-the-art tools that the field of data analysis currently has to offer. The course consists of two parts. There is a review part with six lectures, providing the description of the major data analysis areas. This 6-lecture course is offered as part of the “Network analytics for business” specialization. For students of the “Master of data and network analytics” program, there are six additional lectures on specific topics. They are designed to illustrate some of the specific state-of-the-art approaches within the broader areas. This Course is part of HSE University Master of Data and Network Analytics degree program. Learn more about admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/WMKM6.

People are social entities. They build complex relationships with others around them, form communities and social circles, belong to organizations. Every decision to make a connection to others is based a large variety of variables (called attributes). Every connection, in turn, affects people’s attitudes, behavior, and actions. This relationship between the structure of people’s connections to others and everything that this structure affects is called social dynamics.

Social dynamics is the focus of social network analysis. In this course, we will introduce this exciting field, starting with the very basics – the definitions of network concepts. You will quickly learn that network analysis allows to answer questions and find insights not available with any other approaches. In business, where relationships are essential to efficiency and effectiveness of an organization, it is crucial that analysts know how to analyze these relationships. Therefore, we will not only show you the network concepts, but apply them immediately to real-life business datasets. The possibilities of network analysis are quite broad. In this course, we divide the complex field according to the three major theoretical concepts in social relations: social selection, social influence, and community building. Models of social influence help explain why networks can affect individual behavior. Models of social selection help us understand how people create their network. Community detection models allow us to find the communities that people build, to better understand the structure of such communities. Taken together with network statistics, these models are being demonstrated on real-life datasets collected in real companies. Learners can immediately see how much more powerful relational analysis (networks) are relative to standard statistics alone. They are designed to illustrate some of the specific state-of-the-art approaches within the broader areas. This Course is part of HSE University Master of Data and Network Analytics degree program. Learn more about admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/WMKM6.

In this online course, you will learn about the next big thing in applied analytics – text analysis. This course is self-contained: you will learn everything from basic programming skills to advanced natural language modelling for topic discovery. This course is designed around a problem-oriented approach, meaning that we will not spend too much time learning theoretical concepts but instead focus on applying them to practical problems.

a. The goal of this online course is to equip students with the necessary knowledge and skills for analysing text data with R programming language. b. We do not assume any specific prerequisites for this course. However, some knowledge of natural language processing or R programming might ease the dive into the course materials. c. Each week on the course is accompanied by tests, gradable and non-gradable programming assignments, and links to additional material for those who want to dig deeper into the course material. At the end of the course, you’ll have to complete a project and then review your peers' projects. d. R (programming language), RStudio e. This course is heavily tilted toward practical skills. During this course, students will dive into the basics of R for text analysis, tidy text approach, regular expressions, different algorithms for topic modelling and text classification with machine learning and deep learning approaches, and many more. Various synthetic and real-world databases will help participants see how to apply these techniques to extract insights from user reviews, social media posts, short descriptions of the products. This distance learning opportunity is brought to you by HSE University, one of the top think tanks in Russia, by instructors experienced in using text analysis for business-oriented projects. The online course consists on short pre-recorded lectures, 5 to 15 minutes in length. Each week will have a graded test with 10 to 15 questions. At the end of the last week, students will have to complete a project utilising the skills learned in the course, and then review and grade the projects of their peers. The course gives students an opportunity to learn the methods on natural language processing (NLP) and then apply these methods to problems in students’ own areas of interest.