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Data analysis in R

Gain practical skills in quantitative analysis and data handling in R

Requirements:
  • R Foundation

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Who is the course for

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The course focuses on improving quantitative analysis with emphasis on the use of appropriate methods, reporting results and practical work with data in R. Participants will gain theoretical and practical skills in statistical inference, econometric models and working with different types of data structures.

Target group

  • Data scientists

  • Data analysts

  • Qualitative experts

  • Anyone who wants to learn how to program

What will you learn

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  • Learn to apply statistical methods
  • Understand data analysis and its practical application in research and decision making
  • Get practical tips on how to avoid the most common mistakes in quantitative analysis
  • Understand how to work with data in R, including preparation, manipulation and visualization of different types of data
  • You will learn how to formulate hypotheses and effectively present results using tables and graphs

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Timeline

1st Day

09:00 – 10:30 Introduction to quantitative analysis
  • Basics of hypotheses and statistical inference
10:30 – 10:45 Coffee break
10:45 – 12:15 Reporting hypotheses and results
  • Interpretation of p-values and confidence intervals
  • How to present tables and graphs effectively
12:15 – 13:45 Lunch
13:45 – 15:15 Working with data
  • Data types (cross-sectional, panel, multilevel data)
  • Data preparation and management in R
15:15 – 15:30 Coffee break
15:30 – 17:00 Linear regression as an example of modelling
  • Basics of regression
  • Interpretation and diagnostics of models

2nd Day

09:00 – 10:30 Advanced methods in statistical models
  • Models with limited dependent variable
  • Robust inference
10:30 – 10:45 Coffee break
10:45 – 12:15 Working with data and visualisation
  • Data manipulation in R
  • Data visualization and interpretation of results
12:15 – 13:45 Lunch
13:45 – 15:15 Panel data and hierarchical models
  • Working with panel data
  • Application of hierarchical models
15:15 – 15:30 Coffee break
15:30 – 17:00 Conclusion and practical tips
  • The most common mistakes in quantitative analysis
  • Block length 90
  • Teaching hours 16
  • Refreshments Yes
  • Exam No

The course provides practical skills in quantitative analysis and working with data in R, focusing on statistical inference, visualization, advanced models and effective reporting for decision support.

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