Tiger Analytics Interview Questions, Process, and Tips

Q1. What are the relevant projects in Data science & expertise in whatt all tools & technologies
Ans. 
Relevant projects in Data Science and expertise in tools and technologies
• Projects: Predictive modeling, Natural Language Processing, Computer Vision, Recommender Systems, Time Series Analysis

• Tools: Python, R, SQL, Tableau, Hadoop, Spark, TensorFlow, Keras, Scikit-learn

• Technologies: Machine Learning, Deep Learning, Big Data, Cloud Computing, Data Visualization
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Q1. He asked only coding question in Python & time duration is less & expectations are pretty unclear .
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1. Coding Test Conducted on Hackereath.
There were 10 MCQ each carrying 10 marks.
2 programming questions (40pts, 60pts)
Question were based on DP and Segment Tree.

Q1. Q1. Implement python Collection Counter from Scratch.
Ans. 
Implementing Python Collection Counter from Scratch
• Create an empty dictionary to store the elements and their count

• Iterate through the input list and add elements to the dictionary with their count

• Return the dictionary

• Example: input_list = ['apple', 'banana', 'apple', 'orange', 'banana']

• Output: {'apple': 2, 'banana': 2, 'orange': 1}
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Q2. Q2. Do Matrix Multiplication. Q3. Implement Factorial and Fibonacci Series with different Approaches.
Ans. 
Matrix multiplication, factorial and Fibonacci series implementation
• Matrix multiplication involves multiplying two matrices to get a third matrix

• Factorial is the product of all positive integers up to a given number

• Fibonacci series is a sequence of numbers where each number is the sum of the two preceding ones

• Factorial can be implemented using recursion or iteration

• Fibonacci series can be implemented using recursion or
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Q3. Q4. What is the probability of getting 5 Sundays in 31 day month.
Ans. 
The probability of getting 5 Sundays in a 31 day month is less than 1%.
• There are 7 days in a week, so the probability of any given day being a Sunday is 1/7.

• In a 31 day month, there are 4 full weeks and 3 extra days.

• The probability of the first 4 weeks having 4 Sundays is (1/7)^4.

• The probability of the remaining 3 days being Sundays is (3/7).

• Multiplying these probabilities gives a total probability of less than 1%.
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Q4. Q5. There were 100 coins. 99 Unbiased Coins, 1. Coin is biased. Derive the probability of getting 10 heads given the even of unbiased coins using Bayes Theorem.
Ans. 
Using Bayes Theorem, find the probability of getting 10 heads given 99 unbiased coins and 1 biased coin.
• Identify the prior probability of getting 10 heads with unbiased coins

• Calculate the likelihood of getting 10 heads with the biased coin

• Use Bayes Theorem to calculate the posterior probability of getting 10 heads given the mix of coins

• Consider the impact of the biased coin on the overall probability
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Q1. Q1. Implement a Program to check if a number is power of 3 .
Ans. 
Program to check if a number is power of 3
• Use logarithm to check if the result is an integer

• Check if the number is greater than 0

• Check if the remainder is 0 when the number is divided by 3 repeatedly
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Q2. Q2. What will be the approach If all the features are categorical in Linear Regression. Q3. What is Dummy variable trap? If we don't remove dummy variable what will be the issue and does it impact performa...read more
Ans. 
Categorical features in Linear Regression require encoding using dummy variables. Removing one dummy variable avoids the dummy variable trap.
• Categorical features need to be encoded using dummy variables to be used in Linear Regression

• Dummy variable trap occurs when one dummy variable can be predicted from the others

• Removing one dummy variable avoids the issue of multicollinearity and improves model performance

• Example: ...read more
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Q3. Q4. You are standing in a field. Chances of seeing atleast 1 plane in 10 minutes is 15%. What is the probability of seeing atleast 1 plane in next 30 minutes?
Ans. 
Probability of seeing a plane in 30 minutes given 15% chance in 10 minutes.
• Calculate the probability of not seeing a plane in 10 minutes

• Use the formula P(X>=1) = 1 - P(X=0)

• Calculate the probability of not seeing a plane in 30 minutes using the above probability

• Calculate the probability of seeing atleast 1 plane in 30 minutes using the formula P(X>=1) = 1 - P(X=0)
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Q4. Q5. If we select a random point in a circle of 1 unit radius what is the probability of appearing that point closer to the circumference , not closer to the centre.
Ans. 
Probability of a random point in a circle of 1 unit radius being closer to the circumference than the center.
• The probability is 1/4 or approximately 0.785.

• This is because the area of the circle closer to the circumference is 1/4th of the total area.

• This can be calculated using the formula for the area of a circle: A = πr^2.
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Python Coding Questions Revolve around 1 basic and 1 medium level of python and 1-2 Sql Question and MCQ based On Stats, Data Science related topics

Q1. Please be Prepared to Discuss Your Experience or the Projects you mentioned on the resume?And why i you have selected this algorithm and all related question related to why?
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Q2. Whats the evaluation mertics for classification and regression model?bias and variance
Ans. 
Evaluation metrics for classification and regression models are different. Bias and variance are important factors to consider.
• Classification metrics include accuracy, precision, recall, F1 score, ROC curve, and AUC.

• Regression metrics include mean squared error, mean absolute error, R-squared, and adjusted R-squared.

• Bias refers to the difference between the predicted values and the actual values, while variance refers ...read more
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Q3. What are decision Trees and All the algorithms that you have used in ur project?
Ans. 
Decision Trees are a type of supervised learning algorithm used for classification and regression tasks.
• Decision Trees are used to create a model that predicts the value of a target variable based on several input variables.

• The algorithm splits the data into subsets based on the most significant attribute and continues recursively until a leaf node is reached.

• Some of the algorithms used in my project include Random For...read more
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Q1. Hr will discuss your salary with you and try to get it as high as possible
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Q2. They will pressurize to join the company as soon as possible such is their tactics
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SQL , tableau dashboarding

Case study ppt presentation and general discussion

Q1. 2 python programming questions
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Q1. Why accuracy score should not be used on imbalanced dataset?
Ans. 
Accuracy score can be misleading on imbalanced datasets.
• Accuracy score can be high even if the model is not performing well on the minority class.

• F1 score, precision, and recall are better metrics for imbalanced datasets.

• Stratified sampling, oversampling, and undersampling can help balance the dataset.

• Example: A model predicting cancer in a dataset with only 1% positive cases.

• Using accuracy score, a model that always p...read more
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Q1. Sample T test. What is it?
Ans. 
Sample T test is a statistical test used to determine if there is a significant difference between the means of two groups.
• It is used to compare the means of two groups.

• It assumes that the data is normally distributed.

• It is commonly used in research studies to determine if a treatment has a significant effect.

• Example: A sample T test can be used to compare the mean weight of two groups of people who followed different
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Q2. What is P-value in regression summary?
Ans. 
P-value in regression summary measures the probability of observing a test statistic as extreme as the one computed from the sample data.
• P-value is used to determine the statistical significance of the regression coefficient.

• A low P-value (less than 0.05) indicates that the coefficient is statistically significant.

• A high P-value (greater than 0.05) indicates that the coefficient is not statistically significant.

• P-value...read more
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2 Interview rounds for technical
1st Interview
Duration - 1 hr
Topics - Python DSA question,

Q1. SQL Joins, query , performance
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Q2. Python scripting , pandas, pyspark
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