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I am a data analyst with a strong background in statistics and data visualization.
Graduated with a degree in Statistics
Proficient in programming languages like Python and R
Experience in analyzing large datasets and creating data visualizations
Strong problem-solving skills
I applied via Company Website and was interviewed in Oct 2020. There was 1 interview round.
I applied via Campus Placement
I appeared for an interview before Nov 2019.
I applied via Walk-in and was interviewed in Sep 2020. There was 1 interview round.
I applied via Walk-in and was interviewed before May 2020. There were 3 interview rounds.
I applied via Approached by Company and was interviewed in Jan 2023. There were 3 interview rounds.
Three Data model questions will be given to solve within 24 hours.
A case study on the number of green T-shirts sold in the US.
Identify the target audience for green T-shirts
Analyze the market demand for green T-shirts
Study the sales data of green T-shirts in the US
Identify the popular brands and styles of green T-shirts
Analyze the impact of seasonality on sales
Consider the pricing strategy of green T-shirts
Identify potential marketing opportunities to increase sales
I applied via Campus Placement and was interviewed before Apr 2023. There were 2 interview rounds.
Question:
Suppose you are trying to detect if a particular credit card transaction is fraudulent or not. The credit score of the individual to which the card belongs to had a very healthy credit score. All bills were paid in time and average transaction amount was not that high ($800). The individual had not been out of the country in the last couple of decades. Here is a list of transactions:
1) Gold jwelleries worth $5000
2) Groceries worth $35
3) Second hand car worth $8,000
4) Burgers worth $10
Which transaction looks fraudulent to you?
There is no specific answer. They just want to see how you think through the problem. One can potentially make use of data in order to deal with this problem. From that, one can estimate the probability of each of these transactions being fraudulent. Econometrically, one can develop a potential binary logit model. That would involve identifying certain features that belong to individuals like the one considered above and use these features to come up with an estimate of the probability of the transaction being a fraud.
Not just that, this also needs to include not individual specific features but external features as well. For example, the first transaction might not be as fraudulent as it looks like, because in heavily regulated markets, the risk associated with reselling the gold or exchanging it for money might be high enough to disincentivise the fraudster from buying gold. Thus regulation might also be a valid feature, and different from features describing an individuals characteristics.
Ofcourse problems of overfitting would arise ifan excessive number of features are used. Various means of finding the optimal Degrees of Freedom can be employed.
Obviously one can do better with more complicated decisioning algorihms that involve machine learning models as well.
Eventually one needs to estimate at what threshold of probability will the trasaction be declared fraudulent.
Three sections of apti, ML and Case study
Factors such as foot traffic, proximity to banks, crime rates, and demographics should be considered for ATM placements in a city.
Foot traffic in the area
Proximity to banks or financial institutions
Crime rates in the neighborhood
Demographics of the area (income levels, age groups)
Accessibility and visibility of the location
Local regulations and zoning laws
Availability of power and network connections
Competition from ot...
SQL query using CASE WHEN THEN statement
Use CASE WHEN statement to create conditional logic in SQL queries
Syntax: SELECT column_name, CASE WHEN condition1 THEN result1 WHEN condition2 THEN result2 ELSE result3 END AS new_column_name FROM table_name
Example: SELECT name, CASE WHEN age < 18 THEN 'Minor' ELSE 'Adult' END AS age_group FROM customers
SQL query to join tables based on a common key
Use JOIN keyword to combine rows from two or more tables based on a related column between them
Specify the columns to be joined in the ON clause
Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN
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