Dolat Capital Market Data Analyst Interview Questions, Process, and Tips

Basics related to stats and aptitude questions. Level was moderate to tough.

Very easy coding question for the python or c programming language

Q1. First round of interview was the tecnical interview , data analysis and basic data managemet questions were asked.
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Asked about Numpy , Panads . Some operations of data slicing . 3 problems of python coding .

Q1. What is standard deviation ? state formula for standard deviation
Ans. 
Standard deviation is a measure of the amount of variation or dispersion of a set of values.
• Standard deviation is calculated as the square root of the variance.

• It measures how spread out the values in a data set are around the mean.

• A higher standard deviation indicates more variability in the data.

• Formula for standard deviation: sqrt(Σ(x - μ)² / N), where x is each value, μ is the mean, and N is the number of values.
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Q2. What is correlation , regression , gradient descent
Ans. 
Correlation measures the relationship between two variables, regression predicts a dependent variable based on independent variables, and gradient descent is an optimization algorithm for finding the minimum of a function.
• Correlation measures the strength and direction of a linear relationship between two variables. It ranges from -1 to 1.

• Regression is a statistical technique used to model the relationship between a de...read more
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Q3. What is R square
Ans. 
R square is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable.
• R square ranges from 0 to 1, with 1 indicating a perfect fit.

• It is used to evaluate the goodness of fit of a regression model.

• A higher R square value indicates that the model explains a larger proportion of the variance in the dependent variable.
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Q4. How will you select columns ?
Ans. 
I will select columns based on relevance to the analysis goals and data quality.
• Identify columns relevant to the analysis goals

• Consider data quality and completeness of each column

• Remove redundant or irrelevant columns

• Use statistical methods or domain knowledge to prioritize columns
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Q1. Basic Numpy, Pandas and Matplotlib Question on written paper not online
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