Setup

run setup.sh script first to setup commit hooks. This helps checking various things before a commit is pushed.

Instructions

Structure

• There are .md files which has the questions
• Each Directory has numberd sub-directories e.g., Google/1, Google/2
• Inside each numbered directory there are five files e.g. inside Microsoft/1, there are
• 1.cpp, 1.py, 1.in, 1.out, 1.md
• the .in file has the input that should be passed to the program as input.
• the .out has the expected output. the verifier will run compare the program output with the .out file
• .md file has the question and other documentation. See Google/1/1.md, Google/2/2.md etc for more details

to verify the programs

run ./utils/verify -r from the PROJECT_HOME directory. If the output of the program matches the corresponding .out file then the program will succeed otherwise it will print error.

to run individual program

• Build the program g++ Google/1/1.cpp -o 1.exe
• Run the program by passing the input 1.in and get the output in a separate file 1.prog.out ./1.exe < Google/1/1.in > 1.prog.out
• The python program is run directly so we can run the following command python3 Google/1/1.py < Google/1/1.in > 1.prog.out
• Compare the output with the existing .out file. diff 1.prog.out Google/1/1.out

If the diff does not print anything that means there is no difference. If there is a difference then we need to fix it.

Machine Learning, Data Science coding and conceptual questions

Coding questions

• Collect a list of coding questions and write the answer in python, if C++ is possible then do that otherwise only python.
• Follow the structure of company based questions for the input,output,.md,.py file.
• Put as much detail in .md file as possible. e.g., see Microsoft/4/4.md

Concept questions

• Many websites/blogs have a good collection of such questions: -- https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science/, -- https://www.simplilearn.com/machine-learning-interview-questions-and-answers-article, -- https://www.analyticsvidhya.com/blog/2017/04/40-questions-test-data-scientist-machine-learning-solution-skillpower-machine-learning-datafest-2017/, -- https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/?utm_source=40-questions-test-data-scientist-machine-learning-solution-skillpower-machine-learning-datafest-2017 -- https://www.javacodemonk.com/google-data-scientist-interview-questions-with-answers-f7e88581)
• Use the above websites for guidance and do your research as well, try to put helpful questions only to reduce noise.
• Each question and answer should be in a separate .md file
• Pay attention to grammar and readability.