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Almost every Data Science interview I've had has had a case study portion. The case is representative of a business problem that a Data Scientist would face. I'm looking for a resource with practice cases that match the above description and example solutions. I am also interested if you have alternate methods for preparing for this type of interview.
Ex: We want to implement a new feature, how would you assess it's viability? What data would you want to see? What is your recommendation?
Bumper, passenger side: front fender, door, area above the door, and rear fender are damaged and need to be replaced. I am a novice, but from what I have seen on youtube replacing front fender, rear fender and passenger door are doable projects. I plan to buy OEM parts and have a body shop paint the parts for me. Then remove the old and install the new, avoiding autobody markups.
Assuming the replacements go well, is there anything that might tip the dealership off that I did the repairs myself?
edit: removed unneccessary information
The assignment consists of 3 csv's and a pdf with 10 questions. The questions are mostly SQL related, but they mention that including visualizations is encouraged if they will help with conveying the results.
I'm currently working in a Jupyter notebook, using standard python libraries and psycopg2. Wondering if psycopg2 is the best library for sql queries to a postgres database? And if Jupyter is a good way to go about presenting my work?
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Hello, this is a classification problem with limited opportunity for feature engineering. I'm using a random RandomForestClassifier, and GridsearchCV to tune the hyper parameters. I have two questions 1. Have I implemented the random forest in the best possible way (will share my notebook) 2. Where should I focus the rest of my efforts, try more classifiers, or do more with the random forest.
Down to chat wherever.
I'm going through a bunch of tutorials on matplotlib, pandas, numpy, sklearn etc., and a lot of them are based on Jupyter notebooks, which seem to be pretty cool. But for me, following along, I want to write my code in a file so that it's easy to review. While writing the shell scripts in a file I I have run into some small, but disruptive, diferences. Should I start gettting used to doing data science in the shell? Or should I continue to find work arounds?