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Topics covered are Python, SQL, ML, Statistics, Model Deployments, Interview Question Answers and AWS Sagemaker from Beginner level to Interview level.
Website Link : www.cloudyml.com/ai-for-all-discount
CloudyML - Data Science & Analytics pinned «If you want to start your career in Data Science and want to be interview ready then join "AI for ALL" course for complete Hands-on learning and 1-1 Course Support Mentorship. Topics covered are Python, SQL, ML, Statistics, Model Deployments, Interview Question…»
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What do you think the distribution of time spent per day on Facebook looks like? What metric would you use to describe the distribution.
In terms of the distribution of time spent per day on Facebook (FB), one can imagine there may be two groups of people on Facebook:
1. People who scroll quickly through their feed and don’t spend too much time on FB.
2. People who spend a large amount of their social media time on FB.
Based on this, we make claim about the distribution of time spent on FB. The metrics to describe our distribution can be
1) Centre (mean, median, mode)
2) Spread (standard deviation, inter quartile range
3) Shape (skewness, kurtosis, uni or bimodal)
4) Outliers (Do they exist?)
We can give you a sample answer for your interview: –
If we assume that a person is visiting Facebook page, there is a probability(p) that after one unit of time(t) has passed that she will leave the page.
With a probability of p her visit will be limited to 1 unit of time. With a probability of (1−p)p her visit will be limited to 2 units of time. With a probability of (1−p)2p her visit will be limited to 3 units of time and so on. The probability mass function of this distribution is therefore (1−p)tp, and hence we can say this a geometric distribution.
In terms of the distribution of time spent per day on Facebook (FB), one can imagine there may be two groups of people on Facebook:
1. People who scroll quickly through their feed and don’t spend too much time on FB.
2. People who spend a large amount of their social media time on FB.
Based on this, we make claim about the distribution of time spent on FB. The metrics to describe our distribution can be
1) Centre (mean, median, mode)
2) Spread (standard deviation, inter quartile range
3) Shape (skewness, kurtosis, uni or bimodal)
4) Outliers (Do they exist?)
We can give you a sample answer for your interview: –
If we assume that a person is visiting Facebook page, there is a probability(p) that after one unit of time(t) has passed that she will leave the page.
With a probability of p her visit will be limited to 1 unit of time. With a probability of (1−p)p her visit will be limited to 2 units of time. With a probability of (1−p)2p her visit will be limited to 3 units of time and so on. The probability mass function of this distribution is therefore (1−p)tp, and hence we can say this a geometric distribution.
CloudyML Newsletter: What is the difference between WHERE and HAVING in SQL?
WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter records from the groups based on the specified condition
WHERE Clause implements in row operations. HAVING Clause implements in column operation.
WHERE Clause can be used without GROUP BY clause.
HAVING Clause cannot be used without GROUP BY clause.
WHERE Clause is used before GROUP BY clause. HAVING Clause is used after GROUP BY clause.
WHERE Clause is used with single row functions like UPPER, LOWER etc. HAVING Clause is used with multiple row functions like SUM, COUNT etc.
WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter records from the groups based on the specified condition
WHERE Clause implements in row operations. HAVING Clause implements in column operation.
WHERE Clause can be used without GROUP BY clause.
HAVING Clause cannot be used without GROUP BY clause.
WHERE Clause is used before GROUP BY clause. HAVING Clause is used after GROUP BY clause.
WHERE Clause is used with single row functions like UPPER, LOWER etc. HAVING Clause is used with multiple row functions like SUM, COUNT etc.
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Company: CRED
Role: Data Scientist
1. What do you understand by the term Normal Distribution?
2. Tell us how to effectively represent data using 5 dimensions.
3. Explain bias, variance trade off.
4. How can you compute significance using p-value?
5. What is Gradient Descent? Why don’t gradient descent methods always converge to the same point?
6. Differentiate between a multi-label classification problem and a multi-class classification problem.
7. If the training loss of your model is high and almost equal to the validation loss, what does it mean? What should you do?
8. How will you define the number of clusters in a clustering algorithm?
9. Why L1 regularizations cause parameter sparsity whereas L2 regularization does not?
10. How can you deal with different types of seasonality in time series modelling?
11. Can you give some examples where a false negative important than a false positive?
12. What do you understand by statistical power of sensitivity and how do you calculate it?
13. Given that you let the models run long enough, will all gradient descent algorithms lead to the same model when working with Logistic or Linear regression problems?
14. What is the advantage of performing dimensionality reduction before fitting an SVM?
15. How will you find the correlation between a categorical variable and a continuous variable?
Role: Data Scientist
1. What do you understand by the term Normal Distribution?
2. Tell us how to effectively represent data using 5 dimensions.
3. Explain bias, variance trade off.
4. How can you compute significance using p-value?
5. What is Gradient Descent? Why don’t gradient descent methods always converge to the same point?
6. Differentiate between a multi-label classification problem and a multi-class classification problem.
7. If the training loss of your model is high and almost equal to the validation loss, what does it mean? What should you do?
8. How will you define the number of clusters in a clustering algorithm?
9. Why L1 regularizations cause parameter sparsity whereas L2 regularization does not?
10. How can you deal with different types of seasonality in time series modelling?
11. Can you give some examples where a false negative important than a false positive?
12. What do you understand by statistical power of sensitivity and how do you calculate it?
13. Given that you let the models run long enough, will all gradient descent algorithms lead to the same model when working with Logistic or Linear regression problems?
14. What is the advantage of performing dimensionality reduction before fitting an SVM?
15. How will you find the correlation between a categorical variable and a continuous variable?
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