This research study was conducted to analyze the (potential) relationship between hardware and data set sizes. 100 data scientists from France between Jan-2016 and Aug-2016 were interviewed in order to have exploitable data. Therefore, this sample might not be representative of the true population.

What can you do with the data?

• Look up whether Kagglers has "stronger" hardware than non-Kagglers
• Whether there is a correlation between a preferred data set size and hardware
• Is proficiency a predictor of specific preferences?
• Are data scientists more Intel or AMD?
• How spread is GPU computing, and is there any relationship with Kaggling?
• Are you able to predict the amount of euros a data scientist might invest, provided their current workstation details?

I did not find any past research on a similar scale. You are free to play with this data set. For re-usage of this data set out of Kaggle, please contact the author directly on Kaggle (use "Contact User"). Please mention:

• Your intended usage (research? business use? blogging?...)
• Your first/last name

Arbitrarily, we chose characteristics to describe Data Scientists and data set sizes.

Data set size:

• Small: under 1 million values
• Medium: between 1 million and 1 billion values
• Large: over 1 billion values

For the data, it uses the following fields (DS = Data Scientist, W = Workstation):

• DS_1 = Are you working with "large" data sets at work? (large = over 1 billion values) => Yes or No
• DS_2 = Do you enjoy working with large data sets? => Yes or No
• DS_3 = Would you rather have small, medium, or large data sets for work? => Small, Medium, or Large
• DS_4 = Do you have any presence at Kaggle or any other Data Science platforms? => Yes or No
• DS_5 = Do you view yourself proficient at working in Data Science? => Yes, A bit, or No
• W_1 = What is your CPU brand? => Intel or AMD
• W_2 = Do you have access to a remote server to perform large workloads? => Yes or No
• W_3 = How much Euros would you invest in Data Science brand new hardware? => numeric output, rounded by 100s
• W_4 = How many cores do you have to work with data sets? => numeric output
• W_5 = How much RAM (in GB) do you have to work with data sets? => numeric output
• W_6 = Do you do GPU computing? => Yes or No
• W_7 = What programming languages do you use for Data Science? => R or Python (any other answer accepted)
• W_8 = What programming languages do you use for pure statistical analysis? => R or Python (any other answer accepted)
• W_9 = What programming languages do you use for training models? => R or Python (any other answer accepted)

You should expect potential noise in the data set. It might not be "free" of internal contradictions, as with all researches.

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