American Broadcasting Company Data Scientist Interview Questions and Answers

Q1. Write a SQL query to find all duplicate emails in a table named person
Ans. 
SQL query to find duplicate emails in a table named person
• Use GROUP BY and HAVING clause to group emails and count their occurrences

• Select only those emails which have count greater than 1

• Example: SELECT email, COUNT(*) FROM person GROUP BY email HAVING COUNT(*) > 1;
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Q2. Given a weather table, write a sql query to find all date's ids with higher temperature compared to it's previous dates
Ans. 
SQL query to find date ids with higher temperature compared to previous dates in weather table
• Use self join to compare temperature of current date with previous dates

• Order the table by date to ensure correct comparison

• Select date ids where temperature is higher than previous dates
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2hrs - sections include aptitude, machine learning, deep learning and two easy python coding questions

Q1. Resume scrutiny with projects and internships
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Q2. How to measure 45 mins duration using two identical wires
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Q3. Partition a round cake into eight equal parts within three cuts
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Q4. 10 coins puzzle (5 heads up and 5 tails up)
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DSA and ML, AI, Coding question

Q1. Case study which was easy
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Q1. In depth questions on ML
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The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.

Normal aptitude questions

Test was conducted on datacamp assessments. Overall, there were three tests.
1. Stats test
2. ML test
3. Python/coding test

Q1. Questions on ML techniques and practices, how to handle large data in python, lots of logical questions and handling overfitting, underfitting, etc in model building.
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Q1. Metrics and related questions
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Q1. Machine learning algorithms.
Ans. 
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms require training data to learn patte...read more
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Q2. Credit risk life cycle
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Q3. Pandas related questions
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Q1. Steps of developing a credit risk model
Ans. 
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
• 1. Define the problem and objectives of the credit risk model.

• 2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.

• 3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.

• 4. Select a suitable machine learning algorithm such as logi...read more
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Q2. Pandas related questions
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Q3. Bagging and Boosting
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Q1. Explain AIC and BIC
Ans. 
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
• AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.

• BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...read more
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Q2. Difference between xgboost and lightgbm
Ans. 
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
• XGBoost is known for its accuracy and performance on structured/tabular data.

• LightGBM is faster and more memory-efficient, making it suitable for large datasets.

• LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
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Q3. Bagging and boosting
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