You will see this screen before each data science question. You will be able to choose your preferred language from a list of all supported languages in the dropdown. You'll be told if you need to record a brief explanation of your solution on video or upload a file explaining your solution after coding is done.
After clicking "start", you will arrive at a page like the one in the first image below. Filtered's Jupyter Notebook instances take a few minutes to initialize, but you'll be able to see the progress on screen.
When loading is complete, you'll have access to a fresh Jupyter Notebook like the one pictured below. If your test has a time limit, you can check the remaining time by clicking the "time" button. Notifications will be shown when the time limit is about to expire.
If you need to record a brief explanation of your solution, you will arrive at the following screen. You can click on the video icon to start recording.
Alternatively, if you need to upload a file explanation of your solution, you will arrive at the following screen. You can click on the button to upload.
Data science challenges
For data science challenges, check Jupiter Notebook for their documentation.
Your notebook environment is configured based on the nature of question (size of data set).
Filtered Notebook Specs:
Playground Environment: Use this to get familiar with the notebook
Limited memory (12GB)
Multi-core environment (2 cores)
Supported languages: Python-3 and R
Standard Environment: You will get this environment if the data set has less than 1 million rows
Limited memory (24GB)
Multi-core environment (4 cores)
Supported languages: Python-3 and R
Big Data Environment: You will get this environment if data set has more than 1 million rows
Limited memory (90GB)
Multi-core environment (16 cores)
Supported languages: Python-3 and R
The default notebook comes with many pre installed packages, you can see them with the following commands:
Python
help("modules")
R
options(repr.matrix.max.rows=600, repr.matrix.max.cols=200) ip = as.data.frame(installed.packages()[,c(1,3:4)]) ip = ip[is.na(ip$Priority),1:2,drop=FALSE] ip
The assessment notebook supports package installation, which can be done with the following steps:
Python
pip install package-name
R
One way is to use simple install, for example
install.packages("ggplot2")
Or you can use our custom defined function which manages dependencies as well, for example:
packages <- c("dplyr", "ggplot2", "data.table", "parallel", "foreach")
ipak(packages)
That's it. Best of luck with your interview!
If you have additional technical questions, you can email us at support@filtered.ai