In recent years, data science has grown into a popular career, with thousands of organizations wanting data scientist to join their teams. Each company has a unique approach to the topic, and their data scientist interview questions may vary depending on that.
Large organizations tend to take data science a step further by incorporating machine learning, artificial intelligence, and deep learning in the midst. While those concepts are now considered crucial to data science, not all companies go for them.
Small and medium-sized businesses look for data scientists who can quickly collect, analyze, and visualize datasets for them. That’s usually done to predict consumer response, behavior, and other things that cannot be deduced using metrics only.
However, no matter what your final goal is, you should have a few fundamental skills, knowledge, and expertise.
In this article, we’ll go over the data scientist interview questions you should be able to answer at all times.
Let’s dive right in.
38 Data Science Interview Questions and Answers
Ever since the Harvard Business Review (HBR) posted an article on why the data science job is and will be the hottest job of the century, a lot of people have started to look into how to become a data scientist.
If you’re one of them, you probably know by now that there’s a lot of technical stuff involved, especially if you’re looking to become a Facebook, Amazon, or Google data scientist, or at any other large company.
It’s a lot of mathematics, statistics, programming, modeling, and more. That’s why the following data scientist interview questions are divided into various categories. These categories don’t reflect everything that’s part of the data science job, but they show everything that’s expected from a data scientist today.
Basic Interview Questions
1. Why did you become interested in data science?
Most interviewers prefer getting to know a little background before going into the nitty-gritty details. It helps them develop a baseline of expectations; that helps them structure the interview from that point onward.
For the interviewee, this is a great opportunity. That’s because if you manage to impress the other person in that start, there’s a good chance they’ll overlook minor mistakes and irregularities.
Therefore, it’s crucial to prepare for general questions too. In this case, a lot of recruiters expect data scientists to have a certain passion or reasoning behind choosing this profession.
That’s because it’s a very technical career, and it’s not something you end up choosing because you have nothing else to do. You need a very specific reason to get into it and stick to it.
When you’re answering this question, you should be excited to tell the story of how you were introduced to the subject, why you decide to pursue it, and how you’re doing right now. It should be an opportunity for you to showcase your passion for data science.
Maybe you like statistics, or you may like programming, or maybe you like analyzing data and finding trends. Whatever the case, you should talk about why you decided to be a data scientist and why it interests you to this day.
2. If you had to summarize data science and explain it to the average person, how would you do it?
Another practice data scientist recruiters observe is that they like to see how well you understand the fundamentals of data science yourself.
For example, if you do something enough times, you may learn to do it in a minimal capacity. However, if you understand the concepts, develop your own reasoning, and work on it based on your understanding, you should be able to produce better results, be more efficient, and be better.
It’s the same with data science; you may know how to code, have a list of statistical models, and have access to template models, but that doesn’t mean you’re a good data scientist. Recruiters tend to check that very thing early on to ensure you’re well-versed in all things data science.
Typically, it’s believed that if you can explain something in layman’s terms, you have a solid understanding of the topic. That’s why some scientists are more famous than others; the famous ones can summarize extremely complex topics into bite-sized chunks that are easily grasped by the common person.
Therefore, you need to be able to lay down what data science is, what it does, and its implications in a concise and easy-to-understand explanation.
Here’s an example.
Data science is the process of leveraging programming, mathematics, statistics, and critical thinking to turn large datasets into meaningful insights. It helps weed out trends, patterns, and behaviors that can then help businesses in adjusting their strategies to create tangible business value.
3. How advanced are you as a data scientist? What would you say is your expertise?
Your interviewer will most likely want to know how much you know about data science from you. While the interview itself is there to gauge your understanding of data science and to see if you’re a good fit, but the interviewer also wants to know what level do you think you are at.
Asking this question at the start of the interview helps the interviewer structure the interview better. Usually, they try to ask questions related to what you talked about; therefore, it’s best to be absolutely honest and speak true to your skills and expertise.
Ideally, you would want to start off by talking about how much time you’ve spent in the data science industry. Start by explaining when you started to get into data science, including the first time you heard about it. Explain how you managed to learn it, including details of your degree. If you don’t have a degree, explain where you learned data science, such as a data science bootcamp, certifications, courses, or any other sources.
When you’ve established a base, talk about how you developed your first data science project, and then explain where you went from there. If you don’t have direct work experience, focus on your practice projects.
Furthermore, talk about what you specialize in. For example, are you good with machine learning models, deep learning, artificial intelligence, statistical models, and more. You may understand all of those things, but what is the one thing you’re the best at? That’s what you focus on.
4. What programming languages are you familiar with, and what’s your expertise level with each language?
Programming plays a key role in data science; in fact, it makes up for most of a data scientist’s time. Being a good programmer is key to developing data science projects, models, and testing.
Therefore, it’s safe to say that you’re applying for a data science job, you know programming. However, every programmer has a certain skill that they’re very good at. For example, you may be an expert in Python but not so good with R.
Since you’ve probably developed a lot of data science projects, whether it was done professionally or as practice, you should be able to distinguish your expertise level with each programming language.
Therefore, you should answer this question with timeline-based examples. For example, you developed your first data science model using Python, and now you’re a master at it. However, you started with R later on, and that’s why you’re not as proficient at it.
If something like that is the case, you need to make it clear. Talk about all the programming languages you’ve learned; list them down and explain how proficient you are with each of them.
For example, if we’re talking about Python, tell them when you first started working on it, how much you’ve learned it, whether you have some certifications, and list some notable Python-based programs or tasks you’ve completed.
5. What original algorithms have you created, if any?
Creating algorithms is crucial to developing models in data science. Many people tend to use pre-developed algorithms or get help from sites like GitHub.
It saves a lot of time, and it’s much easier. It doesn’t really affect the quality of one’s work; if anything, it makes everything more efficient.
However, at times, you may not find an algorithm for something. That mostly happens when you’re doing some completely original research. For example, you’re looking to find how many data scientists have become billionaires (not many, yet). You probably won’t find any research on it, and that means you need to develop your own algorithms to make it work.
At most, you might find some research with slightly different independent and dependent variables. You can use those to make it easier when developing your algorithm.
All in all, there’s a good chance you’ve managed to develop original algorithms at some point, especially machine learning algorithms. Talk about those, explain what you used them for, and talk about its success rate.
6. Do you work on open-source projects? What was the last contribution you made?
Open-source projects are the collective efforts of several professionals. They don’t pay anything, usually, but they are a great way to build up experience. It also gives you a chance to work on something meaningful without jumping through unnecessary hoops.
Recruiters like to ask about this because it provides a sense of understanding. Working on open-source projects gives the impression that you’re willing to learn, contribute, and go above and beyond.
In short, it shows drive.
However, if you haven’t worked on any open-source projects, that’s okay too. You can just take this opportunity to talk about your practice projects and how you managed to complete them.
If you have worked on open-source projects, mention the ones you’re proud of. Explain your contribution to the projects, including how it affected each project. If you’ve been recognized for it, make sure you can show that too.
7. Explain the Hadoop framework, including its main components?
If we were to summarize it, Hadoop is an open-source software framework that is used to store data and run applications on several commodity hardware. The cool thing about Hadoop is that it provides a lot of processing power, practically limitless data storage, and the ability to run countless tasks and jobs.
The paragraph above is a good summary of the Hadoop framework, but you shouldn’t use it. It just serves as an example. You shouldn’t use it because it’s a boilerplate explanation. What you want is to provide a custom explanation based on your understanding. Use your own words to explain the Hadoop framework, including some examples, if possible.
Then, mention the three components of Hadoop.
- HDFS (Hadoop Distributed File System) – is for managing large data sets with high volume. It is the primary storage system, allows you to read and write the files, and splits them into smaller files that are replicated across additional servers.
- Hadoop MapReduce – is the software framework that processes a lot of the data in parallel. Some of its components include the JobTracker, TaskTrackers, and JobHistoryServer.
- Hadoop YARN (Yet Another Resource Negotiator) – is a resource management tool that helps cut through clusters and scheduling applications. You can essentially manage and monitor entire workloads using YARN.
Talk about the components mentioned above and follow the same process. Explain them in your own terms, based on how your interview is going so far.
8. What’s the best way to sort a large list of numbers?
Big data questions should always be expected. They are coming in one way or another, and that means you should prepare for it beforehand. At this point, you may be getting completely customized questions, but keep in mind that they’re just variations of commonly asked questions.
This question is where you start to talk about sorting algorithms. Now, you can either start with sorting algorithms in general and build up your answer. Or, you can directly answer the question. It may take more time, but it will be more fruitful to go with the former.
In case you go with the former, you should mention selection sort, bubble sort, insertion sort, merge sort, and quicksort.
Don’t go into much detail with each of them; just a basic rundown is good enough.
When it comes to the best way to sort a large list of numbers, you probably want to go with bubble sort. It’s a simple algorithm and can be used to make sense of any number of clusters. That’s because bubble sort works very well with large data sets where it takes only one iteration to detect the list sort status.
It’s also usually pretty fast with numbers.
9. How would you deal with outliers in your datasets?
In short, an outlier is any data point or piece of data that significantly differs from the rest of the data and observations.
An outlier can be very problematic if you’re developing a machine learning model. That’s because it can mess up the accuracy of your machine learning model. It’s similar to how having one person with bad grades can mess up the collective grade of an entire batch of students.
However, if the outlier is the result of a measurement error, you can remove it from the data set.
There are two ways you can mention.
- Standard Deviations/Z-Score – is a classic statistical tool that allows you to divide various data into subsets. Usually, you’re left with the majority of your data within specific ranges, and the outliers end up outside the range. However, the data has to follow the rule of normal distribution.
- Interquartile Range (IQR) – is the same concept used to develop boxplots. The interquartile range is the difference between the 1st and the 3rd Therefore, if the outlier is beyond the ranges of the IQR, you chalk it off.
You can also mention other methods, including Isolation Forests, Robust Random Cut Forests, and DBScan clustering.
10. How is memory managed in Python?
Technically, memory is managed in a private heap space in Python. Therefore, all the data is located in a private heap.
The catch is that the programmer can’t access the heap; only the Python interpreter can handle it. However, the core API enables the programmer to get access to various Python tools so they can start coding.
All in all, the memory manager allocates the heap space for all the data, while the garbage collector recycles free memory to boost heap space.
Questions regarding Python will come up for sure, considering Python is one of the most crucial programming languages in data science.
It’s best to polish up and revise those Python concepts because you might know how to do something but may have a hard time explaining it.
11. What data types does Python support?
Data types in Python play a key role in determining where it’s used and how the data is classified. This question aims to check your understanding of how data in Python works.
Typically, the standard data types in Python can be grouped into various classes. For the most part, they include numeric types, sequences, sets, and mappings.
To go into more detail, you can mention the following five data types in Python.
- Numeric – This data type is for any data that has a numeric value associated with it. The value can be a floating number, integer, or complex number. They’re defined as float, int, and complex classes in Python.
- Sequence Type – This is where a collection of similar or different data types is ordered. It’s a way of storing multiple values in a more efficient and organized manner.
- Boolean – Boolean data types are about two built-in values, True or False. This is where you see false positives, false negatives, true positives, and true negatives. Non-Boolean objects can also be determined as true or false using a Boolean context. In Python, it’s written as the bool class.
- Set – An unordered data that can be iterated, mutated, and has no duplicate elements is considered a set data type.
- Dictionary – Keeping it simple, a Python dictionary is a collection of unordered data values that store stuff like a map. They are unique because they only hold a single value as an element.
Providing examples of each data type can also go a long way. It’s even better if you can give an example of a project you’ve worked on.
12. What packages in the Python Standard Library do you know?
Since Python has become the leading language for statistics, predictive analytics, machine learning, and data analytics, it has become crucial to the data science workspace.
That’s why there are a few packages in the Python Standard Library that are very useful in data science projects. And, organizations expect data scientists to have experience with them.
- NumPy – Numerical Python (NumPy) is a principle package for data science projects. It’s used to process high-level mathematical functions, matrices, multidimensional arrays, and more. Over the years, NumPy has seen tons of improvements. Today, it also makes for a robust library because it processes data faster, uses lesser code than lists, and makes data analysis much more simple.
- Pandas – Pandas is another Python library that offers high-level data structures and tools for analysis. The good thing about it is that you can convert complex operations into a single command. Some of the key features of Pandas include built-in combining, filtering, and grouping capabilities, along with time-series functionality and speed indicators.
- SciPy – A Python library for scientific computing, SciPy was created by using NumPy as a baseline. It has extremely powerful tools that can help solve problems and tasks related to linear algebra, integral calculus, probability theory, and more.
The Python Standard Library constantly undergoes improvements with better integrations, more support, and increased optimization.
Again, try to give real-world examples of where you’ve used the aforementioned Python packages.
13. How much experience do you have with R? What are some types of sorting algorithms available in it?
R is another crucial language in data science, and you’re expected to have a decent amount of experience in it. It’s not about how many years of experience you have, but how much you’ve achieved in that time.
That’s why it’s a good practice to mention how long it has been since you learned R and, since that time, how you have utilized it. That can include key projects where you used the language, any certifications you have, and more.
The second part of this question can be answered while you’re talking about your R experience. However, the information you’re supposed to impart is that R supports two different types of sorting.
There’s comparison-based sorting where the key values of said input vector are compared to each other before ordering.
And, the second is non-comparison-based sorting, where the computations are performed on each separate key value; the subsequent ordering is based on the computed values.
Some of the sorting algorithms used are:
- Insertion Sort
- Bubble Sort
- Selection Sort
- Shell Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Bin Sort/Radix Sort
If you don’t have any direct experience with R, you should at least be able to define each sorting algorithm.
14. What data objects can be found in R?
Getting a little more technical, recruiters will want to go deeper into the R language. This particular question is usually asked because it shows a basic understanding of the language.
The point is to check whether your basics are established or not. Advanced use of R is rarely asked about since that would steer the interview in another direction. Therefore, such questions tend to make up for it.
When answering this question, you have to take into account that there are core data types and derived data types. It’s smart to mention both types while taking ten seconds to explain each data type.
Core data types define how any given value is stored in the computer. There are three core data types.
- Numeric – It’s the simplest data type that consists of integers or doubles.
- Character – This data type consists of letters and words, including numbers represented by words and characters.
- Logical – There are two values when it comes to logical values: True or False. They can also be represented by 1 and 0.
Derived data types are stored as a core data type but have additional attribute information that allows the objects to be used with certain functions in R. There are six derived data types.
- Factor – They are used to group variables into unique levels and categories.
- Rearranging Level Order – For defining a hierarchy for the factor.
- Subsetting Table – The table can then be subset by level.
- Date – Initially stored as a number, data values need to be defined as a date.
- NA and NULL – In case there are missing or unknown values, it’s better to assign the NA and NULL elements rather than just a 0.
- NA Data Types – NA values can be determined as any data type.
Make sure you can define all of the above.
15. How would you use R and Hadoop together for analysis?
If this is the first mention of Hadoop, take some time to talk about it first. Hadoop is a Java-based programming framework that lets you process large data sets in various computing environments.
Together, R and Hadoop make analysis and data visualization much easier, especially in the case of big data.
Moving on, you can mention any of the following four ways of using Hadoop and R together.
- ORCH – The Oracle R Connector for Hadoop is a collection of R packages that offer interfaces that work with Hive tables, Oracle database tables, the local R environment, and the Apache Hadoop compute infrastructure.
- RHadoop – It’s a collection of three R packages, including rmr, rhdfs, and rhbase. The rmr package gives Hadoop MapReduce capabilities in R, rhdfs gives HDFS file management in R, and rhbase provides HBase database management functionality in R.
- RHIPE – In short, RHIPE provides an API for using Hadoop. Otherwise known as R and Hadoop Integrated Programming Environment, RHIPE does what RHadoop does with a different API.
- Hadoop Streaming – It’s a utility that allows any user to create, run, and manage jobs with any executable as the reduce and/or mapper.
If you’ve worked with large data sets in the past, there’s a good chance you’ve combined R and Hadoop. However, if you haven’t, you should at least try to have one practice project that combines the two.
16. Why are group functions used in SQL?
There has to be at least one SQL-based question in any data science interview. Most of the time, these are case-based to test your practical technical skills.
However, don’t go into too much detail when answering such questions. You can keep it simple.
Group functions in SQL are important to get summary statistics of any data set.
You can also provide some examples of group functions, like the following.
Listing the group functions down is enough. However, if you have time, you should go into more detail. If you have any real experience with these group functions, you can talk about that. Providing an example always plays well with the interviewer.
17. Have you designed a model for a client or employer before? Elaborate on how you went about it.
Past experience plays a very important role in your interview. Even if you have no professional experience, you need to work hard on personal data science projects to get as much experience as possible.
Eventually, the recruiter will ask a question where you’ll have to talk about something you did. This can be your biggest opportunity because you can talk about your achievements here.
If you’ve created a model in the past where you did something commendable, this is the time to emphasize it. This also gives you the opportunity to steer the interview a bit.
That’s because you talking about your model will lead to follow-up questions. Therefore, talking about your model will lead to questions that you can answer with complete confidence.
18. What’s the difference between k-NN and k-means clustering?
Modeling questions can become pretty specific; that’s why you have to try to steer the interview your way.
In any case, the answer to this question should be pretty straightforward.
K-nearest neighbors is a simple classification algorithm. K is the integer that describes the number of neighboring data points that influence the classification of any given observation.
K-means is a clustering algorithm. In this case, k is the integer that describes the total number of clusters that are to be created from any given data.
It’s not necessary to give an example in this case. The point is to understand the difference between the two, such that you can explain it in a concise manner.
19. If you had to create a logistic regression model, how would you go about it?
This is another instance where your experience will come in handy. However, if you don’t have any direct experience, you can start by explaining the concept itself.
Logistic regression is a logit model that lets you predict the binary outcome from any linear combination of predictor variables.
You can create a custom example to explain the concept and model. For example, we can use the example of a simple election. For anyone contesting, there is a binary outcome because you either win, or you lose.
That’s where the predictor variables would come in. In the case of an election, those variables could be past political experience, money spent campaigning, time spent campaigning, the number of opponents, and more.
Create such an example and explain how the logistic regression model would work.
Various Data Science Questions
20. How would you differentiate supervised learning from unsupervised learning?
Moving on, you’ll most likely get a ton of data science questions from various outlooks. They may be completely random, or they may be related to the job you’re applying for.
However, up until now, if you’ve managed to steer the interview with your answers, you’ll most likely get questions related to your previous answers.
Coming to supervised learning, it involves learning any functions that map an input to any output (based on input-output pairs).
Let’s say there’s a dataset with two classifier variables, gas in gallons (input) and mileage (output). A supervised learning model could be used to predict the gas mileage based on the number of gallons of gas used.
Alternatively, unsupervised learning is the process of finding patterns and drawing inferences from input data without any referenced outcomes.
For example, you may group students together by average grades to develop competency groups.
21. How can you validate a predictive model that uses multiple regression?
You just have to mention two different primary ways of doing this.
- Adjusted R-Squared – The r-squared measurement tells you the proportion of variance in the dependent variable due to the variance in the independent variable. In layman terms, the coefficients estimate the trends, and r-squared tells you the +- variance along the line of best fit. The more independent variables you add, the greater the r-squared value. That’s where the adjusted r-squared comes in because it ensures that the r-squared value only increases if the variable improves the model within the possible probability. It helps reduce overfitting and improves accuracy.
- Cross-Validation – The more commonly used cross-validation is when you split the data into two sets: training and testing data.
You need to go overboard with your explanation in this case. Keep your answer technical and straightforward.
22. What exactly are neural networks?
Your interview will most likely go into artificial intelligence at some point. Neural networks are practically baby steps in AI; therefore, it’s crucial that you answer confidently here.
Neural networks are multi-layered models that are inspired by the human brain. Similar to how our brains have neurons, neural networks have nodes.
You have three different layers of nodes. The first layer is the input layer, then comes the hidden layers, and lastly, you have the output layer.
The nodes in the hidden layers represent all the functions that the inputs go through, that eventually lead to an output. These functions are also known as sigmoid activation functions.
Your understanding of neural networks should not be limited to a simple diagram. You should be able to think of an example on the spot. What you want is to make the interviewer believe that you understand the concept fully and can develop such models without any issues.
23. If you had to define NLP in one line, how would you do it?
Natural Language Processing (NLP) is a part of artificial intelligence that deals with giving machines the ability to read, understand, and analyze human languages.
In questions where you’re asked to only define a concept, avoid going into details about it. Furthermore, only provide examples if they specifically ask for them.
24. When do you use random forest and SVM, and why?
Generally speaking, random forests tend to be a better choice compared to support vector machines. Here are a few talking pointers.
- Random forest can be built quickly and have a much easier setup process compared to an SVM.
- Unlike SVMs, random forests give you the option of determining the feature’s importance.
- If you’re dealing with multi-class classification problems, SVMs tend to require a one-vs-rest method; that’s extremely memory intensive and not as scalable.
Keep in mind that you might get a question like this out of the blue while you’re focusing on another topic.
25. How do you select and define metrics?
When talking about metrics, you can’t limit yourself to any single metric. It mostly depends on the machine learning model.
However, the chosen metrics for evaluation are based on a few factors, such as:
- The exact business objective (based on any business problems you might be having).
- Whether it’s a classification task or a linear regression task.
- The distribution of the target variable.
Some of the most common metrics used in this case include the following.
- Adjust r-squared
- F1 score
There are plenty of other metrics that can be chosen based on the factors listed above.
26. What exactly is selection bias?
Selection bias is when you choose a data set for analysis that doesn’t achieve proper randomization. That can lead to results that do not reflect and represent the general market.
The level of selection bias depends on how similar data sets are. The more similar they are, the greater the skewing of results. That will ultimately lead to false insights.
Some types of selection biases include:
- Sampling bias
- Time interval
- Observer selection
It should be a priority to minimize selection bias and attain data sets that are truly random.
27. What exactly is a decision tree?
A decision tree is a very popular model that is used in machine learning, strategic planning, and operations research, among other things.
Every element or square in a decision tree is called a node, and the more nodes you have, the more accurate your decision tree (typically).
The leaves of the decision tree are the last nodes where the decision is actually taken.
Decision trees are a great tool to use for brainstorming because they are easy to build and pretty intuitive. However, they can never be too accurate. If you go for accuracy in a decision tree, you may end up with a tree so large; it would be inconvenient to even look at it.
28. What is Naive Bayes?
Naive Bayes is a data science algorithm that is based on the Bayes theorem. It deals with the probability of any event occurring provided that another event has occurred.
The word naive plays a role in it because the algorithm assumes that each variable in the dataset is completely independent of the other.
Such an assumption is exactly that, an assumption, because realistically, that cannot happen.
29. What are some problems you might face with a linear model?
There are a few drawbacks of using a linear model. Here are a few you can list.
- Linear models can’t be used for discrete and/or binary outcomes.
- There are some pretty strong assumptions in linear models, such as assuming multivariate normality, a linear relationship, no auto-correlation, homoscedasticity, and no multicollinearity.
- There’s no model flexibility.
Using an example, in this case, may be preferred.
30. What exactly is a confusion matrix?
A confusion matrix helps estimate the performance of any model. It compares the actual and predicted values in a 2x2 matrix.
There are four results of a confusion matrix.
- True Positive
- True Negative
- False Positive
- False Negative
You can use an example to explain this too.
31. What exactly is A/B testing?
A/B testing is statistical hypothesis testing with two variables using a set of randomized experiments. The variables are known as A and B.
It’s usually used when you need to introduce a new feature, launch a marketing strategy, or in any case where you need to test what would provide the most desirable outcome.
32. What exactly is dimensionality reduction?
Dimensionality reduction is when you convert a dataset that has a high number of dimensions into one with a lower number of dimensions.
This can be done by removing some fields or columns from the dataset. However, it needs to be done carefully because dropping dimensions can’t result in skewed results.
33. What exactly is p-value?
The p-value is a measure of the statistical importance of any observation. It’s basically the probability that showcases the significance of output to the dataset.
You can compute the p-value to figure out the test statistics of any model. In the end, the p-value can help you choose whether you want to accept or reject the null hypothesis.
34. Can you describe different regularization methods?
When asked about regularization methods, you can talk about L1 and L2 regularization. Both of them help reduce the overfitting of training data.
L2 regularization (ridge regression) minimizes the sum of squared residuals plus lambda times the slope squared. This ridge regression penalty increases bias in the model, making the fit worse on the training set.
Replacing the ridge regression penalty with an absolute value of the slope leads to Lasso regression (L1 regularization).
35. What are precision and recall, and how are they related to the ROC curve?
Recall is the percentage of true positives described as positive by the model.
Precision is the percentage of positive predictions that turned out to be correct.
The ROC curve is the relationship between the model recall and specificity. Specificity is the measure of the percentage of true negatives that are described as negative by the model.
36. How would you manage missing data?
Here are a few ways of dealing with missing data:
- Mean/Median/Mode imputation
- Predicting missing values
- Using random forest and other algorithms that support missing values
- Deleting rows with missing data
- Assigning unique values
The simplest way is to just delete the rows with missing data.
37. What is gradient descent?
The gradient measures the change in the output due to changes in the input. Gradient descent is a minimization algorithm that minimizes a function.
38. What is the purpose of data cleaning in data analysis?
Here are a few ways data cleaning can help with data analysis.
- Data cleaning increases the accuracy of machine learning models.
- Transforming data from multiple sources.
- The more the sources, the more time it takes to clean the data due to increased data volume.
The influx of new data always leads to an exponential increase in the amount of data cleaning.
Acing the Data Scientist Interview Questions
There are a lot of data science interview questions that may be overlooked, but that’s only if you steer the interview out of proportion.
You should only try to steer the interview if you’re able to move the focus on something you’re good at.
As for preparing for the interview, there’s no single tutorial that will help you nail it. You need a problem-solving attitude where you can utilize your prior experience to provide meaningful examples in a concise and easy-to-understand manner.
Once you go over the data scientist interview questions and their answers above, use your personal experience to try and answer them yourself, fix up your data scientist resume, and then ace your next data scientist interview.