Academics

HSE University

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Curriculum

The overarching goal of this degree is to provide state-of-the art Master’s education in applied data analytics with an emphasis on network analytics.

In addition to focusing on foundational data analysis, this program involves examining, transforming and arranging a given data set in specific ways for a specific purpose. Data analytics is an overarching science that encompasses the complete management of data, including data collection, organization, storage, and all of the tools and techniques of the broader field of data analysis that extends beyond statistics alone.

In modern times, decision-making in all forms, from academic to professional to personal, is becoming more and more data driven. However, the majority of data science education programs are still catching up to this growing trend. That’s why HSE’s program approaches statistics education from an applied perspective. Through this approach, you’ll learn to map actual problems into formal statistical models and compute inferences.

Network analysis is a special field focused on analysis of relational data. Networks are everywhere, taking the form of the internet and other infrastructure networks; social, political and economic networks; scientometric and text-representational networks; and food webs and molecular-level biological networks. This program will emphasize network data analysis.

Courses include:

  • Introduction to Programming in R and Python
  • Foundations of Statistics
  • Contemporary Data Analysis: Survey and Best Practices
  • Contemporary Data Analysis: Review of Advanced Methods
  • Introduction to Network Analysis
  • Applied Network Analysis
  • Data Mining
  • Introduction to Text Mining with R
  • Text Mining: Advanced Concepts
  • Business Analytics: Diversity of Business Applications
  • Business Analytics: Visualization for Better Decision-Making
  • Introduction to Probability Theory
  • Advanced Probability Concepts and Stochastic Processes
  • Practical Regression Analysis
  • Theoretical Foundation of Linear Models
  • Applied Linear Models
  • Dimensionality Reduction Methods
  • Foundations of Time-series Analysis
  • Advanced Time-series Analysis
  • Contemporary Decision Sciences: an Integrated Perspective
  • Contemporary Decision Sciences: Special Models
  • Statistical Models of Network Analysis
  • Time Series in Networks
  • Foundations of Computational Social Sciences
  • Network modeling: business applications
  • Network modeling: advanced models
  • Categorical Data Analysis
  • Machine Learning: Basic Concepts
  • Neural Networks and Advanced Concepts
  • Statistical consulting
  • Introduction to Covariance Structures
  • Covariance Structure Analysis: Advanced Models
  • Introduction to Nonparametric Statistics
  • Nonparametric Statistics: Special Models for Decision-Making
  • Introduction to Bayesian Methods
  • Advanced Applications of Bayesian Analysis

NEW! Specialization & Open Courses

The Specialization, Network Analytics for Business, is now open for enrollment on Coursera. There are four courses in this Specialization:

If you are admitted to the full program, any progress you make in these courses will count towards your degree learning.

Program Length

The program is designed so students can enroll from anywhere in the world and complete the program in 24 months.

Flexibility

The program is 100% online. The online format of the MDNA also allows students to interact with instructors and teaching assistants regularly through live chats and video conferences.

Coursera on Mobile

Access all course materials anywhere with the mobile app, used by over 80 percent of degree students on Coursera. Available on iOS and Android.

Using the mobile app, learners can:

  • Save a week’s worth of content for offline access with one click
  • Save and submit quizzes offline
  • View text transcripts of lecture videos
  • Take notes directly in the app
  • Set reminder alerts to help you make progress

Download Coursera's mobile app

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Coursera does not grant credit, and does not represent that any institution other than the degree granting institution will recognize the credit or credential awarded by the institution; the decision to grant, accept, or transfer credit is subject to the sole and absolute discretion of an educational institution.

We encourage you to investigate whether this degree meets your academic and/or professional needs before applying.