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The recruiter asked my Introduction then do I have any excel knowledge? If I tell you to download a car race video from internet then how will you download? Download the USA x states driving licence form? Do you know about skype? Is it can be downloaded from internet if yes then what is the procedure? How to get a sum in Excel? What is raw data? What is the shortcut key to Bold & Italic a Word?
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Research Analyst

Interviewed at Globsyn Technologies

2.9★
Feb 7, 2020

The recruiter asked my Introduction then do I have any excel knowledge? If I tell you to download a car race video from internet then how will you download? Download the USA x states driving licence form? Do you know about skype? Is it can be downloaded from internet if yes then what is the procedure? How to get a sum in Excel? What is raw data? What is the shortcut key to Bold & Italic a Word?

The population of desolate, remote planet is kept alive by a life support system. The population quadruples every 54 years, but the life support system capacity can only be doubled every 54 years. If the initial population is 2 people and the initial life support system has capacity for 16384 people, how many years will pass before the population reaches the capacity of the support system?
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Research Analyst

Interviewed at WorldQuant

4.2★
Apr 2, 2018

The population of desolate, remote planet is kept alive by a life support system. The population quadruples every 54 years, but the life support system capacity can only be doubled every 54 years. If the initial population is 2 people and the initial life support system has capacity for 16384 people, how many years will pass before the population reaches the capacity of the support system?

Gaussian linear models are often insufficient in practical applications, where noise can be heavy- tailed. In this problem, we consider a linear model of the form yi = a · xi + b + ei. The (ei) are independent noise from a distribution that depends on x as well as on global parameters; however, the noise distribution has conditional mean zero given x. The goal is to derive a good estimator for the parameters a and b based on a sample of observed (x, y) pairs. 1.1 Instructions: 1. Load the data, which is provided as (x, y) pairs in CSV format. Each file contains a data set generated with different values of a and b. The noise distribution, conditional on x, is the same for all data sets. 2. Formulate a model for the data-generating process. 3. Based on your model, formulate a loss function for all parameters: a, b, and any additional parameters needed for your model. 4. Solve a suitable optimization problem, corresponding to your chosen loss function, to obtain point estimates for the model parameters. 5. Formulate and carry out an assessment of the quality of your parameter estimates. 6. Try additional models if necessary, repeating steps 2 − 5.
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Member of the Research Staff

Interviewed at Voleon

4.5★
Apr 28, 2017

Gaussian linear models are often insufficient in practical applications, where noise can be heavy- tailed. In this problem, we consider a linear model of the form yi = a · xi + b + ei. The (ei) are independent noise from a distribution that depends on x as well as on global parameters; however, the noise distribution has conditional mean zero given x. The goal is to derive a good estimator for the parameters a and b based on a sample of observed (x, y) pairs. 1.1 Instructions: 1. Load the data, which is provided as (x, y) pairs in CSV format. Each file contains a data set generated with different values of a and b. The noise distribution, conditional on x, is the same for all data sets. 2. Formulate a model for the data-generating process. 3. Based on your model, formulate a loss function for all parameters: a, b, and any additional parameters needed for your model. 4. Solve a suitable optimization problem, corresponding to your chosen loss function, to obtain point estimates for the model parameters. 5. Formulate and carry out an assessment of the quality of your parameter estimates. 6. Try additional models if necessary, repeating steps 2 − 5.

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