Contemporary Data Analysis:
Survey and Best Practices

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

  • The course is related to the online specialization "Network Analytics for Business"
  • Flexible Terms
  • 7 weeks (2 credits)
  • Time to completion: 24 hours
  • Online course
  • Certificate
Apply for the specialization

About the Course

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

Course Objectives


Understand the theoretical foundation behind the methods without focusing too much on the mathematics


Learn the applied, problem-based approach to using specific tools


Get a good understanding of the state-of-the-art tools that the field of data analysis currently has to offer

Learning Outcomes

1. Know the basic types of data and data classification

2. Understand the issues that arise when working with real-life data

3. Know the basic numeric measures and approaches to selecting best measures

4. Understand how descriptive analytics is used to generate business analytics cases

Course Syllabus

Week 1. Introduction and the data

Week 2. Data issues that go bump in the night

Week 3. Descriptive Analytics

Week 4. Inferential analytics

Week 5. Predictive Analytics

Week 6. Prescriptive Analytics

Week 7. Final assignment

Valentina Kuskova

Visiting professor


No specific background required

Graduation Document

Earn a Certificate upon completion



Learning Activities



Low-Stakes Assignments


High-Stakes Assignments 

Final project

Cost and Conditions

17 000 ₽

Full access to the learning materials + Graduation document

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