Armenian Independent University – Summer School – June 28 to July 9, 2021


Syllabus – Data Science


Course Description – This track will introduce students to what data science is and what data scientists do. Audience will discover the applicability of data science across fields and learn how data analysis can help you make data driven decisions. In addition to the basic principles of data science, students will grasp concepts like market research, storytelling, uplift modeling, causal inference, and learn about best practices on how to interpret data.

Requirements: Intermediate Microeconomics and Macroeconomics, Basic Statistics and Econometrics.


Course Plan: 1.

Market Research – Marusya Nersesyan (Head of Innovation & Product Development at Kantar Russia)

  1. What are the benefits?

  • what is marketing research and who needs it?

  • major players in the market research.

  1. What is a brief and what does a project look like?

  • what questions of manufacturers do marketing research answer?

  • research project stages;

  • how to design a research.

  1. Storytelling or how to interpret data.

  • how the story is built;

  • case (work in groups): building a story based on ready-made data.

Introduction to Data Science – Vagan Sargsyan (Cerge-ei, Data Scientist at NetSuite)

  • What is Machine Learning

  • Advantages of Machine Learning

  • Econometrics and Regressions

  • Deep Learning

  • Personal experience and popular cases

Elen Tevanian (HSE, Lead Data Scientist at X5 Retail Group)

  • Predictive Models: How Companies Predict Demand and Revenue

      • corporate business tasks in the form of a demand forecast;

      • boosting over trees;

      • forecast of traffic, average check or churn.

  • Uplift-modeling

  • Causal Research: How to Test the Effectiveness of Decisions (causal inference).

Armen Beklaryan (HSE, Lead Engineer at HUAWEI) (in Russian)

  • General classification of machine learning models

  • Examples of deep learning models o text analysis, text generation;

        • image and sound analysis, speech generation;

        • neural networks in medicine.

  • Examples of Reinforcement Learning Models o self-driving taxi;

        • analysis of the ecological situation in Armenia;

        • conflict simulation.

  • Social modeling o models in epidemiology;

        • models of social competition;

        • election and voting models.