ffreedom app Data Science Intern Interview Questions and Answers

Categories of the CAT exam include Quantitative Aptitude, Verbal Ability, Data Interpretation and Logical Reasoning, and Graphical questions.

Medium level. Focus on SQL subqueries application.

Q1. Statistical questions related to different hypothesis testing
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Q2. Questions related to different machine learning model
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Round duration - 60 minutes
Round difficulty - Easy

Timing: 8:00-9:00 PM
There was 1 MCQ and 3 SQL queries. The platform was easy to use and navigate.

Round duration - 30 minutes
Round difficulty - Medium

Timing: 11 AM-11:30 AM
The interviewer was very kind and helpful. He helped me by giving hints whenever I was stuck.

Round duration - 30 minutes
Round difficulty - Medium

Timing: 12:15 PM to 12:45 PM.
The interviewer was very helpful and friendly. He was more interested in my approach rather than the final answer.

Q1. Puzzle Question
What new feature would you like to add in Uber?
Ans. 
Tip 1 : Creativity has no limit in such questions
Tip 2 : Suggest any of the dreams that you would like to see in your dream company
Tip 3 : I suggested ideas such as Uber Ambulance, Uber Flights, etc
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Round duration - 30 minutes
Round difficulty - Hard

Timing: 3:00PM to 3:30PM
This was Hiring Manager round. He asked me case study type of questions.

Q1. Questions on Past Project
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Q2. SQL Dense Rank - also having the option to do in Pandas
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Q3. Pandas groupby on a dataset given - required to calculate group wise yoy rate of a column
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Q1. Past project ( which he chose , he chose my very first project , which I had forgotten) so ended up screwing it
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Q2. SQL / Pyspark question - difficult question
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Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Easy topics arrays, sequence sum.

Q1. Explain TFIDF and explain CNN
Ans. 
TFIDF is a technique to quantify the importance of a word in a document. CNN is a deep learning algorithm commonly used for image recognition.
• TFIDF stands for Term Frequency-Inverse Document Frequency and is used to evaluate the importance of a word in a document relative to a collection of documents.

• TFIDF is calculated by multiplying the term frequency (number of times a word appears in a document) by the inverse docu...read more
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Q2. Mysql window functions and exceute same in pandas
Ans. 
Window functions in MySQL and pandas are used for performing calculations across a set of rows related to the current row.
• In MySQL, window functions can be used with OVER() clause to perform calculations like ranking, cumulative sum, moving average, etc.

• In pandas, window functions can be applied using the rolling() and expanding() methods to calculate statistics over a specified window of rows.

• Example: In MySQL, to cal...read more
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Q1. Asked firstly some question over my resume then 2 case study type problems where i have to improve the recommdation system for zomato, and finally one coding question based on two pointer
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Q1. Breadth of data science, Linear algebra, prob&stats, Lin reg, Log reg, DT, RF
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Q1. Grilled in Mathematics behind Lin Reg, closed form solution, scalebility of lin reg
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Q1. System design, took a project from my resume and asked me to design the whole solution. Project was a chatbot
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