Today's Interview QnAs
Company Name - Ford
Role: Data Scientist
Q. In experimental design, is it necessary to do randomization? If yes, why?
A. Yes, in experimental design, randomization is required. Randomization eliminates biases and ensures that the outcomes are balanced. The sample that is randomly picked is designed to be representative of the population, and it is fairly selected because it is not influenced by the researcher. You can achieve the best cause-effect linkages between the variables by randomizing the experiments.
Q. What is the benefit of weight initialisation in neural networks?
A. The fundamental goal of weight initialization is to prevent exploding or vanishing gradients in layer activation outputs during forward propagation. If either of these issues arises, loss gradients will be either too great or too little, and the network will take longer to converge, if it can at all. If we appropriately set the weights, we will achieve our goal of optimising the loss function in the shortest time possible; otherwise, converging to a minimum via gradient descent will be impossible.
Q. How will you evaluate the performance of a logistic regression model?
A. The confusion matrix can be used to evaluate a logistic regression model. The accuracy, sensitivity, and specificity of the model can be useful indicators of what you want to do with it - focusing on true positives or false negatives. We can also utilize precision and recall to evaluate your model, as well as the f1 score.
Q. How can you make data normal using Box-Cox transformation?
A. A Box Cox transformation turns non-normal dependent variables into normal shapes. The Lambda value specifies the level of data that should be raised to. The Box-Cox power transformation does this by searching from Lambda = -5 to Lambda = +5 until the best value is discovered.
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Company Name - Ford
Role: Data Scientist
Q. In experimental design, is it necessary to do randomization? If yes, why?
A. Yes, in experimental design, randomization is required. Randomization eliminates biases and ensures that the outcomes are balanced. The sample that is randomly picked is designed to be representative of the population, and it is fairly selected because it is not influenced by the researcher. You can achieve the best cause-effect linkages between the variables by randomizing the experiments.
Q. What is the benefit of weight initialisation in neural networks?
A. The fundamental goal of weight initialization is to prevent exploding or vanishing gradients in layer activation outputs during forward propagation. If either of these issues arises, loss gradients will be either too great or too little, and the network will take longer to converge, if it can at all. If we appropriately set the weights, we will achieve our goal of optimising the loss function in the shortest time possible; otherwise, converging to a minimum via gradient descent will be impossible.
Q. How will you evaluate the performance of a logistic regression model?
A. The confusion matrix can be used to evaluate a logistic regression model. The accuracy, sensitivity, and specificity of the model can be useful indicators of what you want to do with it - focusing on true positives or false negatives. We can also utilize precision and recall to evaluate your model, as well as the f1 score.
Q. How can you make data normal using Box-Cox transformation?
A. A Box Cox transformation turns non-normal dependent variables into normal shapes. The Lambda value specifies the level of data that should be raised to. The Box-Cox power transformation does this by searching from Lambda = -5 to Lambda = +5 until the best value is discovered.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
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Learn from Scratch and Switch to Data Science from any Non-Coding Background
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Bonus
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🟢Job Referrals after course completion
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A Self-Paced course with lifetime access.
🔴So long running offer gonna end Tonight by 11:59PM
Don't Miss this opportunity. You Never gonna get all these features at such price.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Note: From tomorrow, price will become 3499/- for the same package, so Hurry up !
Today's Interview QnAs
Company Name - Siemens
Role: Data Scientist
Q. How to add a border that is filled with 0s around an existing array?
A. For doing this the pad function of numpy can be used by passing a pad width of 1 and constant values as 0.
Q. An array has shape (7,4,2) what is the index (x,y,z) of the 20th element?
A. The answer is (2, 2, 0). This can be found out using the unravel_index function of numpy. Use "print (np.unravel_index(20, (7,4,2)))"
Q. For a model an accuracy of 100% is obtained but with the validation set, the accuracy score is 75%. What should be looked out for?
A. There could be an issue with overfitting. When analysing machine learning algorithms, there are a few key strategies to avoid overfitting: To estimate model accuracy, use a resampling strategy, hold back a validation dataset, use extra training data, or pick relevant features.
Q. How is skewness different from kurtosis?
A. Skewness is a metric for symmetry, or more specifically, the lack of it. If a distribution, or data collection, looks the same to the left and right of the centre point, it is said to be symmetric. Kurtosis is a measure of how heavy-tailed or light-tailed the data are in comparison to a normal distribution. Data sets having a high kurtosis are more likely to contain heavy tails, or outliers. Light tails or a lack of outliers are common in data sets with low kurtosis. The main distinction between skewness and kurtosis is that the former refers to the degree of symmetry in the frequency distribution, whilst the latter refers to the degree of peakedness.
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Company Name - Siemens
Role: Data Scientist
Q. How to add a border that is filled with 0s around an existing array?
A. For doing this the pad function of numpy can be used by passing a pad width of 1 and constant values as 0.
Q. An array has shape (7,4,2) what is the index (x,y,z) of the 20th element?
A. The answer is (2, 2, 0). This can be found out using the unravel_index function of numpy. Use "print (np.unravel_index(20, (7,4,2)))"
Q. For a model an accuracy of 100% is obtained but with the validation set, the accuracy score is 75%. What should be looked out for?
A. There could be an issue with overfitting. When analysing machine learning algorithms, there are a few key strategies to avoid overfitting: To estimate model accuracy, use a resampling strategy, hold back a validation dataset, use extra training data, or pick relevant features.
Q. How is skewness different from kurtosis?
A. Skewness is a metric for symmetry, or more specifically, the lack of it. If a distribution, or data collection, looks the same to the left and right of the centre point, it is said to be symmetric. Kurtosis is a measure of how heavy-tailed or light-tailed the data are in comparison to a normal distribution. Data sets having a high kurtosis are more likely to contain heavy tails, or outliers. Light tails or a lack of outliers are common in data sets with low kurtosis. The main distinction between skewness and kurtosis is that the former refers to the degree of symmetry in the frequency distribution, whilst the latter refers to the degree of peakedness.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
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✅Capstone End-to-end Projects
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Bonus(You gonna get these only if you enroll today)
🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Don't Miss this opportunity. You Never gonna get all these features at such price.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
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Hey Guys, super excited to share that we are launching our Official "CloudyML Mobile App" today on Google Play Store.
An app dedicated to Data science. There are so many future updates coming on this app soon
Features available now
1. Company wise Interview QnAs
2. Topic wise Interview QnAs
3. Free Career Counselling over Chat
4. Free Videos and Study Material
Help us to grow so we can make more better platform for learning Data Science
Download Now👉🏻 https://ai.cloudyml.com/Download-App
An app dedicated to Data science. There are so many future updates coming on this app soon
Features available now
1. Company wise Interview QnAs
2. Topic wise Interview QnAs
3. Free Career Counselling over Chat
4. Free Videos and Study Material
Help us to grow so we can make more better platform for learning Data Science
Download Now👉🏻 https://ai.cloudyml.com/Download-App
Today's Interview QnAs
Company Name - Fractal
Role: Data Scientist
Q. What is the difference between squared error and absolute error?
A. Squared error is the squared difference between the predicted values and the actual value. Absolute Error is the difference between the measured value and true value. The squared error is differentiable everywhere, whereas the absolute error is not (its derivative is undefined at 0). This makes the squared error more susceptible to mathematical optimization strategies.
Q. Under what aspects Naive Bayes is bad?
A. In Naive Bayes, all predictors (or traits) are assumed to be independent, which is rarely the case in real life. This limits the algorithm's usability in real-world scenarios. The 'zero-frequency problem' occurs when an algorithm assigns zero probability to a categorical variable whose category in the test data set was not present in the training dataset. To get over this problem, you should employ a smoothing approach. You shouldn't take its probability outputs seriously because its estimations can be off in some instances.
Q. How will you tackle an exploding gradient problem?
A. The problem of exploding gradients can be solved by reducing the number of layers in the network. Using a smaller batch size when training the network may also be beneficial. Long Short-Term Memory (LSTM) memory units and maybe similar gated-type neuron structures can be used to reduce exploding gradients. When you have an exploding gradient problem, you can use gradient clipping. First, we choose a threshold value, and if the value produced by the gradient function is greater than this threshold, we change it to something else. It may also be beneficial to use suitable weight initialization techniques.
Q. How will you prevent overfitting when creating a statistical model?
A. Cross-validation is an effective tool for avoiding overfitting. The aim is to create many micro train-test splits using your initial training data. These divisions can be used to fine-tune your model. More data can help algorithms recognize the signal more accurately. Also, make a list of relevant variables and terms that you'll probably use in your own model.
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Company Name - Fractal
Role: Data Scientist
Q. What is the difference between squared error and absolute error?
A. Squared error is the squared difference between the predicted values and the actual value. Absolute Error is the difference between the measured value and true value. The squared error is differentiable everywhere, whereas the absolute error is not (its derivative is undefined at 0). This makes the squared error more susceptible to mathematical optimization strategies.
Q. Under what aspects Naive Bayes is bad?
A. In Naive Bayes, all predictors (or traits) are assumed to be independent, which is rarely the case in real life. This limits the algorithm's usability in real-world scenarios. The 'zero-frequency problem' occurs when an algorithm assigns zero probability to a categorical variable whose category in the test data set was not present in the training dataset. To get over this problem, you should employ a smoothing approach. You shouldn't take its probability outputs seriously because its estimations can be off in some instances.
Q. How will you tackle an exploding gradient problem?
A. The problem of exploding gradients can be solved by reducing the number of layers in the network. Using a smaller batch size when training the network may also be beneficial. Long Short-Term Memory (LSTM) memory units and maybe similar gated-type neuron structures can be used to reduce exploding gradients. When you have an exploding gradient problem, you can use gradient clipping. First, we choose a threshold value, and if the value produced by the gradient function is greater than this threshold, we change it to something else. It may also be beneficial to use suitable weight initialization techniques.
Q. How will you prevent overfitting when creating a statistical model?
A. Cross-validation is an effective tool for avoiding overfitting. The aim is to create many micro train-test splits using your initial training data. These divisions can be used to fine-tune your model. More data can help algorithms recognize the signal more accurately. Also, make a list of relevant variables and terms that you'll probably use in your own model.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
✅Hands on Guided assignments
✅Capstone End-to-end Projects
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🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Hey guys,
CloudyML is going to conduct FREE Live Webinar on various Data Science topics every week and it is starting from today.
Today's Topic : Introduction of Python for Data Science, variables, Data type and Data structure.
Timing: 15/01/2022 07:00PM (IST)
After webinar, there will be Live QnA Session.
If you wanna attend today's Free Webinar, download our mobile App Now
App Link👉🏻https://ai.cloudyml.com/Download-App
Note: Free courses, Live webinar, Interview QnAs, Chat Features etc is available in the app.
CloudyML is going to conduct FREE Live Webinar on various Data Science topics every week and it is starting from today.
Today's Topic : Introduction of Python for Data Science, variables, Data type and Data structure.
Timing: 15/01/2022 07:00PM (IST)
After webinar, there will be Live QnA Session.
If you wanna attend today's Free Webinar, download our mobile App Now
App Link👉🏻https://ai.cloudyml.com/Download-App
Note: Free courses, Live webinar, Interview QnAs, Chat Features etc is available in the app.
Today's Interview QnAs
Company Name - Tredence
Role: Data Scientist
Q. Explain how the filter function works in python?
A. The filter() method filters a series using a function that checks if each element in the sequence is true or not. The filter() function takes two arguments: function - a function and iterable - an iterable like sets, lists, tuples etc.
Q. How to remove duplicate elements from a list?
A. First we have a List that contains duplicates. Create a dictionary, using the List items as keys. This will automatically remove any duplicates because dictionaries cannot have duplicate keys. Then, convert the dictionary back into a list.
Q. Difference between a shallow and a deep copy?
A. It's faster to do shallow repetitions. It does, however, handle pointers and references in a "lazy" manner. It just copies over the pointer price rather than producing a current copy of the specific knowledge the pointer links to. As a result, each of the initial and subsequent copies can have pointers that relate to the same underlying knowledge. Deep repetition clones the underlying data completely. It is not shared by the first and, as a result, by the copy.
Q. What is TF/IDF vectorization?
A. The TF-IDF statistic, which stands for term frequency–inverse document frequency, is a numerical measure of how essential a word is to a document in a collection or corpus. [1] It's frequently used in information retrieval, text mining, and user modelling searches as a weighting factor. The tf–idf value rises in proportion to the number of times a word appears in a document and is offset by the number of documents in the corpus that contain the term, which helps to compensate for the fact that some words appear more frequently than others.
Q. In what terms DBSCAN is better than K- Means Clustering?
A. Outliers and noisy datasets are easily handled by DBScan clustering, whereas outliers and noisy datasets are not adequately handled by K-means clustering. The number of clusters does not need to be stated in DBScan clustering. The number of clusters specified in K-means clustering is important.
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Company Name - Tredence
Role: Data Scientist
Q. Explain how the filter function works in python?
A. The filter() method filters a series using a function that checks if each element in the sequence is true or not. The filter() function takes two arguments: function - a function and iterable - an iterable like sets, lists, tuples etc.
Q. How to remove duplicate elements from a list?
A. First we have a List that contains duplicates. Create a dictionary, using the List items as keys. This will automatically remove any duplicates because dictionaries cannot have duplicate keys. Then, convert the dictionary back into a list.
Q. Difference between a shallow and a deep copy?
A. It's faster to do shallow repetitions. It does, however, handle pointers and references in a "lazy" manner. It just copies over the pointer price rather than producing a current copy of the specific knowledge the pointer links to. As a result, each of the initial and subsequent copies can have pointers that relate to the same underlying knowledge. Deep repetition clones the underlying data completely. It is not shared by the first and, as a result, by the copy.
Q. What is TF/IDF vectorization?
A. The TF-IDF statistic, which stands for term frequency–inverse document frequency, is a numerical measure of how essential a word is to a document in a collection or corpus. [1] It's frequently used in information retrieval, text mining, and user modelling searches as a weighting factor. The tf–idf value rises in proportion to the number of times a word appears in a document and is offset by the number of documents in the corpus that contain the term, which helps to compensate for the fact that some words appear more frequently than others.
Q. In what terms DBSCAN is better than K- Means Clustering?
A. Outliers and noisy datasets are easily handled by DBScan clustering, whereas outliers and noisy datasets are not adequately handled by K-means clustering. The number of clusters does not need to be stated in DBScan clustering. The number of clusters specified in K-means clustering is important.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
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🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
#Job #Openings
Role: Data Scientist
Company: Qdegrees Services pvt ltd
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Role: Data Scientist
Company: Qdegrees Services pvt ltd
Exp- 0-2yr
Link to Apply- https://bit.ly/3qFYE1b
Role: ML & DS - Technical Content Engineer - Intern
Company: HackerEarth
Exp- 0-1yr
Link to Apply- https://bit.ly/3rgMgU4
Role: Data Scientist / Senior DS/ Team Lead
Company: NebulARC Technologies Private Limited
Exp- 0-4yr
Link to Apply- https://bit.ly/33EtrSE
Role: Staff Software Engineer - Machine Learning
Company: Twitter
exp- 0-4yr
Link to Apply- https://bit.ly/3qw69Y6
Today's Interview QnAs
Company Name - FORD
Role- Data Scientist
Q. What are Entropy and Information gain in Decision tree algorithm?
A. Entropy is a measure of impurity or uncertainty in a set of data used in information theory. It determines how data is split by a decision tree. The quantity of information improved in the nodes before splitting them for making subsequent judgments can be characterized as the information obtained in the decision tree.
Q. What Will Happen If the Learning Rate Is Set inaccurately (Too Low or Too High)?
A. A high learning rate in gradient descent will cause the learning to jump over global minima, whereas a low learning rate will cause the learning to take too long to converge or become stuck in an unwanted local minimum.
Q. What is meant by ‘curse of dimensionality’?
A. The problem produced by the exponential rise in volume associated with adding extra dimensions to Euclidean space is known as the "curse of dimensionality." The curse of dimensionality states that as the number of characteristics grows, the error grows as well. It refers to the fact that high-dimensional algorithms are more difficult to build and often have a running duration that is proportional to the dimensions. A higher number of dimensions theoretically allows for more information to be stored, but in practice, it rarely helps because real-world data contains more noise and redundancy.
Q. Difference between remove, del and pop?
A. remove function removes the first matching value/object. It does not do anything with the indexing. del function removes the item at a specific index. And pop removes the item at a specific index and returns it.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
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Company Name - FORD
Role- Data Scientist
Q. What are Entropy and Information gain in Decision tree algorithm?
A. Entropy is a measure of impurity or uncertainty in a set of data used in information theory. It determines how data is split by a decision tree. The quantity of information improved in the nodes before splitting them for making subsequent judgments can be characterized as the information obtained in the decision tree.
Q. What Will Happen If the Learning Rate Is Set inaccurately (Too Low or Too High)?
A. A high learning rate in gradient descent will cause the learning to jump over global minima, whereas a low learning rate will cause the learning to take too long to converge or become stuck in an unwanted local minimum.
Q. What is meant by ‘curse of dimensionality’?
A. The problem produced by the exponential rise in volume associated with adding extra dimensions to Euclidean space is known as the "curse of dimensionality." The curse of dimensionality states that as the number of characteristics grows, the error grows as well. It refers to the fact that high-dimensional algorithms are more difficult to build and often have a running duration that is proportional to the dimensions. A higher number of dimensions theoretically allows for more information to be stored, but in practice, it rarely helps because real-world data contains more noise and redundancy.
Q. Difference between remove, del and pop?
A. remove function removes the first matching value/object. It does not do anything with the indexing. del function removes the item at a specific index. And pop removes the item at a specific index and returns it.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
✅Hands on Guided assignments
✅Capstone End-to-end Projects
✅1-1 Skype Live Doubt Clearance
✅Frequently Asked Interview QnAs PDF collection
✅Course completion Certificate
Bonus
🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Today's Interview QnAs
Company Name - HP
Role: Data Scientist
Q. Is skewness in data bad for the model? Why?
A. In a statistical distribution, skewed data is defined as a curve that seems deformed or skewed to the left or right. Many statistical models fail when there is too much skewness in the data. The tail portion of skewed data may act as an outlier for the statistical model, and we know that outliers have a negative impact on model performance, particularly regression-based models.
Q. How to train a model robust to outliers?
A. You can employ an outlier-resistant model. Outliers have little effect on tree-based models, but they do alter regression-based models. If you're doing a statistical test, instead of using a parametric test, use a non-parametric one. A robust error metric can be used: The influence of outliers is reduced by switching from mean squared error to mean absolute difference. Set a limit on how much data you can collect. Try a log transformation if your data has a strong right tail.
Q. Show me how lamda and map function works together in python
A. In Python, the map() function accepts two arguments: a function and a list. The function is called with a lambda function and a list, and it returns a new list with all of the lambda modified items returned by that function for each item.
Q. Combat Overfitting?
A. When a model performs well on training data but not on new data, it is said to be overfitted. To avoid overfitting, enhance training data and simplify the model. During the training phase, you should end sooner rather than later (have an eye over the loss over the training period as soon as loss begins to increase stop training). Ridge Regularization and Lasso Regularization are two types of regularisation. To combat overfitting in neural networks, use dropout.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
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🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Company Name - HP
Role: Data Scientist
Q. Is skewness in data bad for the model? Why?
A. In a statistical distribution, skewed data is defined as a curve that seems deformed or skewed to the left or right. Many statistical models fail when there is too much skewness in the data. The tail portion of skewed data may act as an outlier for the statistical model, and we know that outliers have a negative impact on model performance, particularly regression-based models.
Q. How to train a model robust to outliers?
A. You can employ an outlier-resistant model. Outliers have little effect on tree-based models, but they do alter regression-based models. If you're doing a statistical test, instead of using a parametric test, use a non-parametric one. A robust error metric can be used: The influence of outliers is reduced by switching from mean squared error to mean absolute difference. Set a limit on how much data you can collect. Try a log transformation if your data has a strong right tail.
Q. Show me how lamda and map function works together in python
A. In Python, the map() function accepts two arguments: a function and a list. The function is called with a lambda function and a list, and it returns a new list with all of the lambda modified items returned by that function for each item.
Q. Combat Overfitting?
A. When a model performs well on training data but not on new data, it is said to be overfitted. To avoid overfitting, enhance training data and simplify the model. During the training phase, you should end sooner rather than later (have an eye over the loss over the training period as soon as loss begins to increase stop training). Ridge Regularization and Lasso Regularization are two types of regularisation. To combat overfitting in neural networks, use dropout.
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Today's Quiz
What is file extension of jupyter notebook?
What is file extension of jupyter notebook?
Anonymous Quiz
22%
.py
3%
.css
66%
. ipynb
9%
.jv
Media is too big
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Today's Interview QnAs
Company Name - Course i5
Role: Data Scientist
Q. How can outlier values be treated?
A. An outlier is an observation in a dataset that differs significantly from the rest of the data. This signifies that an outlier is much larger or smaller than the rest of the data.
Given are some of the methods of treating the outliers: Trimming or removing the outlier, Quantile based flooring and capping, Mean/Median imputation.
Q. What is root cause analysis?
A. A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity.
Q. What is bias and variance in Data Science?
A. The model's simplifying assumptions simplify the target function, making it easier to estimate. Bias is the difference between the Predicted Value and the Expected Value in its most basic form. Variance refers to how much the target function's estimate will fluctuate as a result of varied training data. In contrast to bias, variance occurs when the model takes into account the data's fluctuations, or noise.
Q. What is a confusion matrix?
A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. This gives us the following: "True positive" for event values that were successfully predicted. "False positive" for event values that were mistakenly predicted. For successfully anticipated no-event values, "true negative" is used. "False negative" for no-event values that were mistakenly predicted.
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Company Name - Course i5
Role: Data Scientist
Q. How can outlier values be treated?
A. An outlier is an observation in a dataset that differs significantly from the rest of the data. This signifies that an outlier is much larger or smaller than the rest of the data.
Given are some of the methods of treating the outliers: Trimming or removing the outlier, Quantile based flooring and capping, Mean/Median imputation.
Q. What is root cause analysis?
A. A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity.
Q. What is bias and variance in Data Science?
A. The model's simplifying assumptions simplify the target function, making it easier to estimate. Bias is the difference between the Predicted Value and the Expected Value in its most basic form. Variance refers to how much the target function's estimate will fluctuate as a result of varied training data. In contrast to bias, variance occurs when the model takes into account the data's fluctuations, or noise.
Q. What is a confusion matrix?
A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. This gives us the following: "True positive" for event values that were successfully predicted. "False positive" for event values that were mistakenly predicted. For successfully anticipated no-event values, "true negative" is used. "False negative" for no-event values that were mistakenly predicted.
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Day 21 | What's the difference between list and array in python ? | Data Science Interview QnA
👉🏻https://youtu.be/dVXwSwldsgE
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Day 21 | What's the difference between list and array in python ? | Data Science Interview QnA
Understand the difference between list and python.
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Today's Interview QnAs
Company Name - Genpact
Role: Data Scientist
Q. How will you measure the Euclidean distance between the two arrays in NumPy?
A. The magnitude or length of the line segment between two locations is known as the Euclidean distance in mathematics. We first create two numpy arrays in the first procedure. The Euclidean distance is then computed directly using numpy's linalg.norm() function.
Q. Difference between Normalisation and Standardization?
A. Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance).
Q. What is Ensemble Learning?
A. Ensemble learning is the process of systematically generating and combining many models, such as classifiers or experts, to tackle a specific computational intelligence problem. Ensemble learning is largely used to improve a model's performance (classification, prediction, function approximation, etc.) or to lessen the risk of an unintentional poor model selection.
Q. Difference between Batch and Stochastic Gradient Descent?
A. Batch Gradient Descent is highly slow on very big training sets since it entails calculations over the entire training set at each step. As a result, Batch GD becomes extremely computationally expensive. SGD is stochastic in nature, which means it chooses up a "random" instance of training data at each step and then computes the gradient, which is significantly faster than Batch GD because there is much less data to modify at once.
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Company Name - Genpact
Role: Data Scientist
Q. How will you measure the Euclidean distance between the two arrays in NumPy?
A. The magnitude or length of the line segment between two locations is known as the Euclidean distance in mathematics. We first create two numpy arrays in the first procedure. The Euclidean distance is then computed directly using numpy's linalg.norm() function.
Q. Difference between Normalisation and Standardization?
A. Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance).
Q. What is Ensemble Learning?
A. Ensemble learning is the process of systematically generating and combining many models, such as classifiers or experts, to tackle a specific computational intelligence problem. Ensemble learning is largely used to improve a model's performance (classification, prediction, function approximation, etc.) or to lessen the risk of an unintentional poor model selection.
Q. Difference between Batch and Stochastic Gradient Descent?
A. Batch Gradient Descent is highly slow on very big training sets since it entails calculations over the entire training set at each step. As a result, Batch GD becomes extremely computationally expensive. SGD is stochastic in nature, which means it chooses up a "random" instance of training data at each step and then computes the gradient, which is significantly faster than Batch GD because there is much less data to modify at once.
P.S: Learn from scratch and switch to Data science from any non-coding background with our Assignments based Data Science course.
Features
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✅ Capstone End-to-end Projects
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