Practice More Quizzes:
Q:
What could be wrong with drawing generalized conclusions for society from digital footprint data from Google searches, dating websites, or Twitter feeds?
Q:
In lecture, we used the analogy that “digital footprint is to the social sciences, what the microscope was for biology”. What did this refer to in the context of social science?
Q:
In lecture, we used the analogy that “digital footprint is to the social sciences, what the telescope was to physics”. What did this refer to in the context of social science?
Q:
What digital trace data did Professor Blumenstock use in the presented case study to predict poverty in Afghanistan?
Q:
What digital trace data did Professor Blumenstock use in the presented case study to predict poverty in Afghanistan?
Q:
Which of the following is in line with Professor Blumenstock’s work in using new data and new methods to understand causes and consequences of poverty?
Q:
Since a census is the traditional procedure of systematically acquiring information about all members of a given population, it is crucial to understand the people that make societies. It comes with many challenges. What is NOT one of them?
Q:
In developing countries, the most commonly available sources for digital footprint data are:
Q:
In order to understand the relationship between digital trace data and poverty, Prof. Blumenstock from UC Berkeley used the following data sources in a case study in Rwanda:
Q:
…Why does Prof. Blumenstock from UC Berkeley work with both mobile phone log data and survey responses, collected by calling people?
Q:
Why does Prof. Blumenstock from UC Berkeley work with both mobile phone log data and survey responses, collected by calling people?
Q:
Why does Professor Blumenstock finally compare the poverty predictions made on basis of his mobile phone trace data with poverty data from public sources?
Q:
In data science, as used by Prof. Blumenstock from UC Berkeley, what is “feature engineering”?
Q:
In contrast to traditional programming approaches, machine learning approaches feed what into the computer, in order to produce what in return?
Q:
In order to predict poverty from mobile phone log data, Prof. Blumenstock from UC Berkeley used ‘supervised machine learning’. What is this and what did he do?
Q:
Prof. Blumenstock from UC Berkeley used survey data to detect features in mobile phone log data, and then tests the accuracy of the obtained model by comparing it to National Household survey data from the National Institute of Statistics (in this case study, from Rwanda). How high was the obtained accuracy (correlation) of the ‘big data based’ prediction?
Q:
We have seen several benefits arising when mobile phone log data is used to predict poverty levels. Which one was NOT one of them?
Q:
At the end of his lecture, Prof. Blumenstock from UC Berkeley concluded that digital trace data:
Q:
For what research task did Prof. Blumenstock from UC Berkeley use so-called ‘feature engineering’?
Subscribe
0 Comments
Find Questions in This Page: "CTRL+F"
