Machine Learning Top Interview Questions & Answers 2018

                                       MACHINE LEARNING

With an increasing popularity for ML, there’s a clear increase in demand for business professionals and new graduates in this field of technology. Coming to the job role, an ML engineer utilises his or her understanding of mathematics coupled with strong programming skills to solve tech-oriented problems.

Machine learning interview questions are an integral part of the data science interview and the path to becoming a data scientist, machine learning engineer or data engineer.

 Here we present the top interview questions that are generally asked in companies to assess the candidate’s expertise in machine learning. 

General Questions:

Q1.What is Machine Learning?

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" with data, without being explicitly programmed.

Q2. How is ML different from artificial intelligence?

AI involves machines that execute tasks which are programmed and based on human intelligence, whereas ML is a subset application of AI where machines are made to learn information. They gradually perform tasks and can automatically build models from the learnings.

Q3. Mention the difference between Data Mining and Machine learning?

Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed.  While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns.  During this process machine, learning algorithms are used.

Q4. What is inductive machine learning?

The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

Q5. What is ‘Overfitting’ in Machine learning?

In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs.  When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.

Q6.  Why causes overfitting?

The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.

Q7. What are the basic differences between Machine Learning and Deep Learning

Machine Learning Vs Deep Learning
	Machine Learning	Deep Learning
Definition	Sub-discipline of AI	Subset of machine learning
Data	Parses the data	Creates an artificial neural network
Accuracy	Requires manual intervention means decreased accuracy	Self-learning capabilities mean higher accuracy
Interpretability	Machine Learning is Faster	10 Times Faster than ML
Output	ML models produce a numerical output	DL algorithms can range from an image to text or even an audio
Data dependencies	High	Low
Hardware dependencies	Can work on low-end machines.	Heavily depend on high-end machines
Future	Effective with image recognition and face recognition in mobiles	Not much effective due to data processing limitations

Q8.What are neural networks and where do they find their application in ML? Elaborate.

Neural networks are information processing models that derive their functions based on biological neurons found in the human brain. The reason they are the choice of technique in ML is because, they help discover patterns in data that are sometimes too complex to comprehend by humans.

Q9. What are the five popular algorithms of Machine Learning?

a)      Decision Trees

b)      Neural Networks (back propagation)

c)       Probabilistic networks

d)      Nearest Neighbor

e)      Support vector machines

Q10.      What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are

a)      Supervised Learning

b)      Unsupervised Learning

c)       Semi-supervised Learning

d)      Reinforcement Learning

e)      Transduction

f)       Learning to Learn

Q11. What are the three stages to build the hypotheses or model in machine learning?

a)      Model building

b)      Model testing

c)       Applying the model

Q12. What is the difference between supervised and unsupervised machine learning?

A Supervised learning is a process where it requires training labeled data.  When it comes to Unsupervised learning it doesn’t require data labeling.

Q13. What is ‘tuning’ in ML?

Generally, the goal of ML is to automatically provide accurate output from the vast amounts of input data without human intervention. Tuning is a process which makes this possible and it involves optimising hyperparameters for an algorithm or a ML model to make them perform correctly.

Q14. What is optimisation in ML?

Optimisation in general refers to minimising or maximising an objective function (in linear programming). In the context of ML, optimisation refers to tuning of hyperparameters which result in minimising the error function (or loss function).

Q15. What is the difference between Bias and Variance?

Bias:

Bias can be defined as a situation where an error has occurred due to use of assumptions in the learning algorithm.

Variance:

Variance is an error caused because of the complexity of the algorithm that is been used to analyze the data.

Q16. Differentiate between a parameter and a hyperparameter?

Parameters are attributes in training data that can be estimated during ML. Hyperparameters are attributes that cannot be determined beforehand in the training data. Example: Learning rate in neural networks.

Q17.What is ‘tuning’ in ML?

Generally, the goal of ML is to automatically provide accurate output from the vast amounts of input data without human intervention. Tuning is a process which makes this possible and it involves optimising hyperparameters for an algorithm or a ML model to make them perform correctly.

Q18.What is optimisation in ML?

Optimisation in general refers to minimising or maximising an objective function (in linear programming). In the context of ML, optimisation refers to tuning of hyperparameters which result in minimising the error function (or loss function).

Q19.What is the use of gradient descent?

The use of gradient descent plainly lies with the fact that it is easy to implement and is compatible with most of the ML algorithms when it comes to optimisation. This technique works on the principle of cost function.

Q20. Explain what is precision and Recall?

Recall:

It is known as a true positive rate. The number of positives that your model has claimed compared to the actual defined number of positives available throughout the data.

Precision:

It is also known as a positive predicted value. This is more based on the prediction. It is a measure of a number of accurate positives that the model claims when compared to the number of positives it actually claims.

Q20 What is the difference between Type 1 and Type 2 errors?

Type 1 error is classified as a false positive. I.e. This error claims that something has happened but the fact is nothing has happened. It is like a false fire alarm. The alarm rings but there is no fire.

Type 2 error is classified as a false negative. I.e. This error claims that nothing has happened but the fact is that actually, something happened at the instance.

The best way to differentiate a type 1 vs type 2 error is:

Calling a man to be pregnant- This is Type 1 example

Calling pregnant women and telling that she isn’t carrying any baby- This is type 2 example.

Q21. List down various approaches for machine learning?

The different approaches in Machine Learning are

a)      Concept Vs Classification Learning

b)      Symbolic Vs Statistical Learning

c)       Inductive Vs Analytical Learning

Q22. Explain what is the function of ‘Unsupervised Learning’?

a)      Find clusters of the data

b)      Find low-dimensional representations of the data

c)       Find interesting directions in data

d)      Interesting coordinates and correlations

e)      Find novel observations/ database cleaning

Q23   Explain what is the function of ‘Supervised Learning’?

a)      Classifications

b)      Speech recognition

c)       Regression

d)      Predict time series

e)      Annotate strings

Q24. Explain Principal Component Analysis (PCA).

PCA is a dimensionality-reduction technique which mathematically transforms a set of correlated variables into a smaller set of uncorrelated variables called principal components.

Q25. Is rotation necessary in PCA? If yes, Why? What will happen if you don’t rotate the components?

Answer: Yes, rotation (orthogonal) is necessary because it maximizes the difference between variance captured by the component. This makes the components easier to interpret. Not to forget, that’s the motive of doing PCA where, we aim to select fewer components (than features) which can explain the maximum variance in the data set. By doing rotation, the relative location of the components doesn’t change, it only changes the actual coordinates of the points.

If we don’t rotate the components, the effect of PCA will diminish and we’ll have to select more number of components to explain variance in the data set.

Q26. What is the F1 score?        

The F1 score is defined as a measure of a model’s performance.

Q27. How is F1 score is used?

The average of Precision and Recall of a model is nothing but F1 score measure. Based on the results, the F1 score is 1 then it is classified as best and 0 being the worst.

Q28. Explain any data preprocessing technique for ML.

Standardisation: It is mainly used for algorithms following a Gaussian distribution. It can be done through scikit learn Standardscaler class (for Python).

Q29. What value do you optimise when using a support vector machine (SVM)?

For a linear function, SVM optimises the product of input vectors as well as the coefficients. In other words, the algorithm with the linear function can be restructured into a dot-product.

Q30. On what basis do you choose a classifier?

Classifiers must be chosen based on the accuracy it provides on the trained data. Also, the size of the dataset sometimes affects accuracy. For example, Naive Bayes classifiers suit smaller datasets in terms of accuracy due to higher asymptotic errors.

Q31.Which is better for image classification- Supervised or unsupervised classification.? Justify.

In a supervised classification, the images are interpreted manually by the ML expert to create feature classes whereas this is not the case in unsupervised classification wherein the ML software creates feature classes based on image pixel values. Therefore, it is better to opt for supervised classification for image classification in terms of accuracy.

Q32. Explain the Bias-Variance Tradeoff.

Predictive models have a tradeoff between bias (how well the model fits the data) and variance (how much the model changes based on changes in the inputs).

Simpler models are stable (low variance) but they don't get close to the truth (high bias).

More complex models are more prone to being overfit (high variance) but they are expressive enough to get close to the truth (low bias).

The best model for a given problem usually lies somewhere in the middle.

Q33. Why is naive Bayes so ‘naive’ ?

Naive Bayes is so ‘naive’ because it assumes that all of the features in a data set are equally important and independent. As we know, these assumption are rarely true in real world scenario.

Q34. Explain prior probability, likelihood and marginal likelihood in context of naiveBayes algorithm?

 Prior probability is nothing but, the proportion of dependent (binary) variable in the data set. It is the closest guess you can make about a class, without any further information. For example: In a data set, the dependent variable is binary (1 and 0). The proportion of 1 (spam) is 70% and 0 (not spam) is 30%. Hence, we can estimate that there are 70% chances that any new email would  be classified as spam.

Likelihood is the probability of classifying a given observation as 1 in presence of some other variable. For example: The probability that the word ‘FREE’ is used in previous spam message is likelihood. Marginal likelihood is, the probability that the word ‘FREE’ is used in any message.

Q35. What are the three stages to build the model in machine learning:

Model building

Model testing

Applying the model

Q36. How Recall and True positive rate are related?

The relation is

True Positive Rate = Recall.

Q37. Assume that you are working on a data set, explain how would you select important variables?

The following are few methods can be used to select important variables:

Use of Lasso Regression method.

Using Random Forest, plot variable importance chart.

Using Linear regression.

Q38. Explain how we can capture the correlation between continuous and categorical variable?

Yes, it is possible by using ANCOVA technique. It stands for Analysis of Covariance.

It is used to calculate the association between continuous and categorical variables

Q39. How is KNN different from k-means clustering?

K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.

The critical difference here is that KNN needs labeled points and is thus supervised learning, while k-means doesn’t — and is thus unsupervised learning.

Q40. What is Bayes’ Theorem? How is it useful in a machine learning context?

Bayes’ Theorem gives you the posterior probability of an event given what is known as prior knowledge.

Mathematically, it’s expressed as the true positive rate of a condition sample divided by the sum of the false positive rate of the population and the true positive rate of a condition. Say you had a 60% chance of actually having the flu after a flu test, but out of people who had the flu, the test will be false 50% of the time, and the overall population only has a 5% chance of having the flu. Would you actually have a 60% chance of having the flu after having a positive test?

Bayes’ Theorem says no. It says that you have a (.6 * 0.05) (True Positive Rate of a Condition Sample) / (.6*0.05)(True Positive Rate of a Condition Sample) + (.5*0.95) (False Positive Rate of a Population)  = 0.0594 or 5.94% chance of getting a flu.

Q41. Explain the difference between L1 and L2 regularization.

L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.

Q42.  What’s a Fourier transform?

A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain — it’s a very common way to extract features from audio signals or other time series such as sensor data.

Q43. What is deep learning, and how does it contrast with other machine learning algorithms?

Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.

Q44. What’s the difference between a generative and discriminative model?

A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.

Q45. What cross-validation technique would you use on a time series dataset?

Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data — it is inherently ordered by chronological order. If a pattern emerges in later time periods for example, your model may still pick up on it even if that effect doesn’t hold in earlier years!

You’ll want to do something like forward chaining where you’ll be able to model on past data then look at forward-facing data.

fold 1 : training [1], test [2]

fold 2 : training [1 2], test [3]

fold 3 : training [1 2 3], test [4]

fold 4 : training [1 2 3 4], test [5]

fold 5 : training [1 2 3 4 5], test [6]

Q46. What are the advantages and disadvantages of decision trees?

Advantages: Decision trees are easy to interpret, nonparametric (which means they are robust to outliers), and there are relatively few parameters to tune.

Disadvantages: Decision trees are prone to be overfit. However, this can be addressed by ensemble methods like random forests or boosted trees.

Q47. What are the advantages and disadvantages of neural networks?

Advantages: Neural networks (specifically deep NNs) have led to performance breakthroughs for unstructured datasets such as images, audio, and video. Their incredible flexibility allows them to learn patterns that no other ML algorithm can learn.

Disadvantages: However, they require a large amount of training data to converge. It's also difficult to pick the right architecture, and the internal "hidden" layers are incomprehensible.

Q48. How can you choose a classifier based on training set size?

If training set is small, high bias / low variance models (e.g. Naive Bayes) tend to perform better because they are less likely to be overfit.

If training set is large, low bias / high variance models (e.g. Logistic Regression) tend to perform better because they can reflect more complex relationships.

Q49. Explain Latent Dirichlet Allocation (LDA).

Latent Dirichlet Allocation (LDA) is a common method of topic modeling, or classifying documents by subject matter.

LDA is a generative model that represents documents as a mixture of topics that each have their own probability distribution of possible words.

The "Dirichlet" distribution is simply a distribution of distributions. In LDA, documents are distributions of topics that are distributions of words.

Q50. What is the ROC Curve and what is AUC (a.k.a. AUROC)?

The ROC (receiver operating characteristic) the performance plot for binary classifiers of True Positive Rate (y-axis) vs. False Positive Rate (x-axis).

AUC is area under the ROC curve, and it's a common performance metric for evaluating binary classification models.

It's equivalent to the expected probability that a uniformly drawn random positive is ranked before a uniformly drawn random negative.

Q51. Why is Area Under ROC Curve (AUROC) better than raw accuracy as an out-of- sample evaluation metric?

AUROC is robust to class imbalance, unlike raw accuracy.

For example, if you want to detect a type of cancer that's prevalent in only 1% of the population, you can build a model that achieves 99% accuracy by simply classifying everyone has cancer-free.

Q52. How is a decision tree pruned?

Pruning is what happens in decision trees when branches that have weak predictive power are removed in order to reduce the complexity of the model and increase the predictive accuracy of a decision tree model. Pruning can happen bottom-up and top-down, with approaches such as reduced error pruning and cost complexity pruning.

Reduced error pruning is perhaps the simplest version: replace each node. If it doesn’t decrease predictive accuracy, keep it pruned. While simple, this heuristic actually comes pretty close to an approach that would optimize for maximum accuracy.

Q53. Which is more important to you– model accuracy, or model performance?

This question tests your grasp of the nuances of machine learning model performance! Machine learning interview questions often look towards the details. There are models with higher accuracy that can perform worse in predictive power — how does that make sense?

Well, it has everything to do with how model accuracy is only a subset of model performance, and at that, a sometimes misleading one. For example, if you wanted to detect fraud in a massive dataset with a sample of millions, a more accurate model would most likely predict no fraud at all if only a vast minority of cases were fraud. However, this would be useless for a predictive model — a model designed to find fraud that asserted there was no fraud at all! Questions like this help you demonstrate that you understand model accuracy isn’t the be-all and end-all of model performance.

Q54. How would you handle an imbalanced dataset?

An imbalanced dataset is when you have, for example, a classification test and 90% of the data is in one class. That leads to problems: an accuracy of 90% can be skewed if you have no predictive power on the other category of data! Here are a few tactics to get over the hump:

1- Collect more data to even the imbalances in the dataset.

2- Resample the dataset to correct for imbalances.

3- Try a different algorithm altogether on your dataset.

What’s important here is that you have a keen sense for what damage an unbalanced dataset can cause, and how to balance that.

Q55. Why are ensemble methods superior to individual models?

They average out biases, reduce variance, and are less likely to overfit.

There's a common line in machine learning which is: "ensemble and get 2%."

This implies that you can build your models as usual and typically expect a small performance boost from ensembling.

Q56. Explain bagging.

Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided into multiple subsets through resampling.

Then, each subset is used to train a model, and the final predictions are made through voting or averaging the component models.

Bagging is performed in parallel.

Q57. Name an example where ensemble techniques might be useful.

Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data).

You could list some examples of ensemble methods, from bagging to boosting to a “bucket of models” method and demonstrate how they could increase predictive power.

Q58. How would you evaluate a logistic regression model?

A subsection of the question above. You have to demonstrate an understanding of what the typical goals of a logistic regression are (classification, prediction etc.) and bring up a few examples and use cases.

Q59. What’s the “kernel trick” and how is it useful?

The Kernel trick involves kernel functions that can enable in higher-dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space. This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates. Many algorithms can be expressed in terms of inner products. Using the kernel trick enables us effectively  run  algorithms in a high-dimensional space with lower-dimensional data.

Q60. Pick an algorithm. Write the psuedo-code for a parallel implementation.

This kind of question demonstrates your ability to think in parallelism and how you could handle concurrency in programming implementations dealing with big data. Take a look at pseudocode frameworks such as Peril-L and visualization tools such as Web Sequence Diagrams to help you demonstrate your ability to write code that reflects parallelism.

Q61. Describe a hash table.

A hash table is a data structure that produces an associative array. A key is mapped to certain values through the use of a hash function. They are often used for tasks such as database indexing.

Q62. - How can we use your machine learning skills to generate revenue?

This is a tricky question. The ideal answer would demonstrate knowledge of what drives the business and how your skills could relate. For example, if you were interviewing for music-streaming startup Spotify, you could remark that your skills at developing a better recommendation model would increase user retention, which would then increase revenue in the long run.

Q63. You are given a data set on cancer detection. You’ve build a classification model and achieved an accuracy of 96%. Why shouldn’t you be happy with your model performance? What can you do about it?

If you have worked on enough data sets, you should deduce that cancer detection results in imbalanced data. In an imbalanced data set, accuracy should not be used as a measure of performance because 96% (as given) might only be predicting majority class correctly, but our class of interest is minority class (4%) which is the people who actually got diagnosed with cancer. Hence, in order to evaluate model performance, we should use Sensitivity (True Positive Rate), Specificity (True Negative Rate), F measure to determine class wise performance of the classifier. If the minority class performance is found to to be poor, we can undertake the following steps:

We can use undersampling, oversampling or SMOTE to make the data balanced.

We can alter the prediction threshold value by doing probability caliberation and finding a optimal threshold using AUC-ROC curve.

We can assign weight to classes such that the minority classes gets larger weight.

We can also use anomaly detection.

Q64. You came to know that your model is suffering from low bias and high variance. Which algorithm should you use to tackle it? Why?

Low bias occurs when the model’s predicted values are near to actual values. In other words, the model becomes flexible enough to mimic the training data distribution. While it sounds like great achievement, but not to forget, a flexible model has no generalization capabilities. It means, when this model is tested on an unseen data, it gives disappointing results.

In such situations, we can use bagging algorithm (like random forest) to tackle high variance problem. Bagging algorithms divides a data set into subsets made with repeated randomized sampling. Then, these samples are used to generate  a set of models using a single learning algorithm. Later, the model predictions are combined using voting (classification) or averaging (regression).

Also, to combat high variance, we can:

Use regularization technique, where higher model coefficients get penalized, hence lowering model complexity.

Use top n features from variable importance chart. May be, with all the variable in the data set, the algorithm is having difficulty in finding the meaningful  signal.

Q65. While working on a data set, how do you select important variables? Explain your methods.

Following are the methods of variable selection you can use:

Remove the correlated variables prior to selecting important variables

Use linear regression and select variables based on p values

Use Forward Selection, Backward Selection, Stepwise Selection

Use Random Forest, Xgboost and plot variable importance chart

Use Lasso Regression

Measure information gain for the available set of features and select top n features accordingly.

Q66. What is the difference between covariance and correlation?

 Correlation is the standardized form of covariance.

Covariances are difficult to compare. For example: if we calculate the covariances of salary ($) and age (years), we’ll get different covariances which can’t be compared because of having unequal scales. To combat such situation, we calculate correlation to get a value between -1 and 1, irrespective of their respective scale.

Q67.  Is it possible capture the correlation between continuous and categorical variable? If yes, how?

 Yes, we can use ANCOVA (analysis of covariance) technique to capture association between continuous and categorical variables.

Q68. Both being tree based algorithm, how is random forest different from Gradient boosting algorithm (GBM)?

The fundamental difference is, random forest uses bagging technique to make predictions. GBM uses boosting techniques to make predictions.

In bagging technique, a data set is divided into n samples using randomized sampling. Then, using a single learning algorithm a model is build on all samples. Later, the resultant predictions are combined using voting or averaging. Bagging is done is parallel. In boosting, after the first round of predictions, the algorithm weighs misclassified predictions higher, such that they can be corrected in the succeeding round. This sequential process of giving higher weights to misclassified predictions continue until a stopping criterion is reached.

Random forest improves model accuracy by reducing variance (mainly). The trees grown are uncorrelated to maximize the decrease in variance. On the other hand, GBM improves accuracy my reducing both bias and variance in a model.

Q69. You’ve got a data set to work having p (no. of variable) > n (no. of observation). Why is OLS as bad option to work with? Which techniques would be best to use? Why?

In such high dimensional data sets, we can’t use classical regression techniques, since their assumptions tend to fail. When p > n, we can no longer calculate a unique least square coefficient estimate, the variances become infinite, so OLS cannot be used at all.

To combat this situation, we can use penalized regression methods like lasso, LARS, ridge which can shrink the coefficients to reduce variance. Precisely, ridge regression works best in situations where the least square estimates have higher variance.

Among other methods include subset regression, forward stepwise regression.

Q70. ‘People who bought this, also bought…’ recommendations seen on amazon is a result of which algorithm?

The basic idea for this kind of recommendation engine comes from collaborative filtering.

Collaborative Filtering algorithm considers “User Behavior” for recommending items. They exploit behavior of other users and items in terms of transaction history, ratings, selection and purchase information. Other users behaviour and preferences over the items are used to recommend items to the new users. In this case, features of the items are not known.

Q71. In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbors. Why not manhattan distance ?

 We don’t use manhattan distance because it calculates distance horizontally or vertically only. It has dimension restrictions. On the other hand, euclidean metric can be used in any space to calculate distance. Since, the data points can be present in any dimension, euclidean distance is a more viable option.

Example: Think of a chess board, the movement made by a bishop or a rook is calculated by manhattan distance because of their respective vertical & horizontal movements.

Q72.When does regularization becomes necessary in Machine Learning?

Regularization becomes necessary when the model begins to ovefit / underfit. This technique introduces a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and hence reduce cost term. This helps to reduce model complexity so that the model can become better at predicting (generalizing).

Q73. What do you understand by Bias Variance trade off?

 The error emerging from any model can be broken down into three components mathematically. Following are these component :

Q74. What are some key business metrics for (S-a-a-S startup | Retail bank | e-Commerce site)?

Thinking about key business metrics, often shortened as KPI's (Key Performance Indicators), is an essential part of a data scientist's job. Here are a few examples, but you should practice brainstorming your own.

Tip: When in doubt, start with the easier question of "how does this business make money?"

S-a-a-S  startup : Customer lifetime value, new accounts, account lifetime, churn rate, usage rate, social share rate

Retail bank: Offline leads, online leads, new accounts (segmented by account type), risk factors, product affinities

e-Commerce: Product sales, average cart value, cart abandonment rate, email leads, conversion rate.

Q75 How can you help our marketing team be more efficient?

The answer will depend on the type of company. Here are some examples.

Clustering  algorithms to build custom customer segments for each type of marketing campaign.

Natural language  processing  for headlines to predict performance before running ad spend.

Predict conversion probability based on a user's website behaviour in order to create better re-targeting campaigns.