In today’s article, we will focus on certain Data engineer interview questions and thought processes that you need to sit down with and interview in data science. From there, we’ll make the questions a little bit more complicated and try to answer what kind of skill sets people are typically looking at.
When you appear for a data engineer interview in any company, different roles depend on the complexity and variety, and questions may change. Still, this article will try to cover the basic level stuff.
Basic Interview Questions and Answers
So prepare, on a very common basis, regardless of what role you’re applying for, you might be able to get at least a perspective of what data science as such Data engineer interview questions.
So with that note, let’s see how we’re going to structure the various Data engineer interview questions that will come up today on statistics, data analysis, machine learning, and probability, so these are the four headings under which the questions will come.
How to Approach a Particular Question
We will discuss the Data engineer interview questions. How to approach a particular Data engineer interview questions in a structured approach in a properly structured way, and once we understand the problem, we will see how the thought process of finding the solution acts properly; so you all know, write down the data Science in bloom to answer Data engineer interview questions.
Many new opportunities are opening up worldwide in different companies; regardless of which company it is or which specific business it is, there’s potential for some data science applications.
- There are a lot of data, and different enterprise systems that generate data at a tremendous speed and variety of volume, so with this kind of data asset that every company has now, it’s now they want to use it to grow the business to the next level.
- So some of these statistics here you have like this is more than enough motivation that getting into data science and would defend you to some really good professional career, okay, so let’s start with Data engineer interview questions, which they directly fill in here at the beginning.
- We will start to focus on some basic questions which are useful for you. When an interviewer asks you this Data engineer interview question, for example, many people ask about data science.
- Although some online resources and blogs describe data science very briefly, this is what boils down to a person who understands computer algorithms very well.
- A person who understands statistics and mathematical ideas and applies them to computer science and math knowledge to a specific application right into a business application where somebody sees value coming out of the data, so how do you apply it?
Data Science Approach
So that’s how data science approaches it when you combine these two powerful concepts from computer science and math into a real-world application. The kind of result from a particular data science project etc., goes in a direction where people see our return on investment right.
Data science helps us understand big data and handle it at the same time as well.
Concepts to Brush up
Some important subjects are computer science, applied mathematics, linear algebra, and others. Computer science algorithms and data structures can be very useful for mathematics or statistics and things like mathematical linear algebra matrix factorization and similar concepts and applications; it’s more from your industry experience.
- If you’ve worked in retail, you know how the business process in retail works properly, so people often also ask tech, and we need some experience in Python, real Python, or, for example, mining programs.
- So Python is one of the most sought-after cramming skills, especially when you want to build data science solutions, and with the availability of libraries like NumPy.
- Python now strongly introduced functions specifically for data science and provides a robust framework for properly designing data science solutions via things it has, like lists of dictionaries, tuples, and states of its abilities that put Python in a league of its own as a programming language.
It’s well-suited to designing solutions, so there are many other libraries besides building machine learning algorithms. Still, these are some common libraries you wouldn’t find people using and distributions like an anaconda.
- Python has shown its capabilities even for dealing with the degree of rake protection in which you ensure all the dependencies that one particular library has for building data science.
- Although our programming is also fully good for making a quick prototype for most of your modeling tasks, the button is moving into a production class where things can be deployed after a certain prototype to a production environment and can face a programmer someday.
So with Python comes certain capabilities, so let’s talk about something more specific with data.
When analyzing data, we usually face something which is known as selection bias, so what exactly is selection bias? The place where you start exploring the data is picking a simple representative, right? That’s where we normally begin doing any analysis, so if you’re like working for a company with a toy 1 billion records in their databases.
Like 1 billion is likely a large number of which represent different customer data, or it could also be depending on what feature you are working on and so on, so if you put them all together, it could very easily be one billion records which is nothing but cultured state number of rows so with this enormous amount of data and analysis you do could have a lot of filters like say I only want to analyze a certain feature in my products like I don’t want my customers from region X Y Z which is for the top 1 region or regions.
So you might want to put a lot of functions or filters like this. Still, later on, if you’re going to do some analysis that covers most of your customer base right, there’s a tricky situation where you can’t use the full volume of 1 billion records simultaneously. Suppose you want to do a good study or analysis based on your data, so in statistics. In that case, we normally use this idea of doing a kind of random sampling, so with this random sampling, we make sure that we pick a small subset of those 1 billion records.
Let’s say 1 million represents the entire population, but what happens with this random selection of 1 million records? Here is a chance that they could have some bias in the analysis because you’re not using the whole population, so taking low-level polls can be unethical at first but has its values close to real probabilities.
- A very common example is if you want to analyze a particular election’s exit poll before the election results come out. You didn’t choose a representative sample to do an exit poll analysis.
- That’s what I mean; you only asked some questions for a selective view from a particular constituency, and they have an opinion about one candidate, but that doesn’t represent the real opinion of the people in that constituency.
- Hence, it’s very important to handle sampling bias. People often use random sampling or sampling techniques like stratified sampling to minimize that selection bias, so these are some very general questions.
- Let’s start with some Statistical Data Engineer interview questions and also by dealing with different data types, okay, so doing any data with structured information.
- A lot of rows and columns are there, and it looks more like tabular data, so when there’s data like that, there’s like two different formats one says long and split.
- Suppose you have a record of two customers, right, and you store only two values, height and weight, so they are like columns. So for these two customers, this height and weight are separate columns in one format like a long format.
Structured and Unstructured Data
- Let’s say you change it by having only one specific column, which will display the attribute directly by bringing these two columns as one column by calling it an attribute and putting the values in one column, so this kind of format is called long.
- So what is, instead of having two separate columns for two of the records, you put either or both of them in one column, giving you many advantages that you have in hand.
- Especially in data visualization, you would need to put your attributes not as a separate column but rather as one column that can have attribute names that could then go directly into creating your legends.
- So these kinds of techniques are very common formats being longer and split, and very often, people will like to deal with both of these data formats depending on what task they’re doing.
- Especially when we’re creating visualization panels, well, we’re talking a little bit more about the data analysis perspective of people like they know that in statistics, the normal distribution is the one like The Godfather of you has a lot of distributions that normally people try to figure out if these are in my data or not.
- Things are somewhat easy to understand when people see the normal distribution of any data. Typically, any distribution of any data you would like to find out given data for analysis gives us many characteristics around what information is on time.
Database Management Systems
- Let’s say we analyze the salaries of employees in my company, maybe I see that some employees are such a thick crust in the center, where most people sit with a moderate level of the salary range, and then there are such extremes left, and right, so you’re like very often people refer, whenever you talk about salaries, to the bell curve on the right, and then they start talking about the top 25 percent of performers in my company, the bottom 25 and the middle 25 that you like is normal performance,
- So this kind of bell curve distribution is very commonly understood, and just as it is used in a lot of data analysis, so does the other tributes, but the normal distribution has its own. This is stored data.
- So when someone asks you something related to the non-normal distribution, the first thing you should imagine is a symmetrical bell-shaped curve, and the moment you get this bell-shaped curve in your imagination, to avoid a corrupt data block.
- Start thinking of certain properties like the mean of the normal distribution, the standard deviation of the normal distribution, and in particular, a special case of the normal distribution we call the standard normal distribution in this standard normal distribution from various data sources.
- We know that the mean is exactly zero all the time, and the standard deviation is exactly right, so there are various places where the normal distribution is used to reduce duplicate data points from the data warehouse team.
- And suppose you are comfortable with ideas like the central limit theorem or the law of large numbers. In that case, you can also relate them to the normal distribution, especially the central limit theorem, and you are on your way to becoming a successful data engineer.
- All these properties of that distribution are exposed, so it’s important to analyze the distribution of data, the normal distribution, like very common, and in many statistical techniques in exercises to build even models.
- Suppose you have something in the normal distribution. Many other possibilities of using certain modeling (relational database) techniques are emerging in that case.
- There are also many other modeling techniques in statistics and machine learning where there is an underlying assumption that things should follow the normal distribution. If it doesn’t, the model is incorrect, hence poor relational databases.
SAP Data Management
There are many cases when we know what the normal distribution is. Hence XML configuration files are present but put in a symmetric bell-shaped curve. The test is quite a popular approach, especially for those working with the right product. What happens when you have locked down many features inside the product, for example, if LinkedIn is a company with the right website?
SAP Data Services
So what happens as an analyst? You can work with Lincoln and say, one fine day, they may come up with a new feature, a new design, or a new kind of change to their website. You, as a person, say, well, this is my framework for testing how you change by defining a metric, so my metric will tell if I change this website from A to B if my website traffic goes down or not, so this is my metric. If successful, I will find that after the launch of this new website.
- The number of customers visiting my website will not decrease. Now we will introduce this new feature.
- So normally, we would have two groups of users in this framework who would identify the specific risk associated with introducing this new feature to the platform.
- We randomly put one user group and exposed them to an older site and later exposed them to new features when we compared the results of a specific metric, such as the number of clicks or the number of purchases, and so on.
- This study showed that with better software people are more likely to buy or check out your sites or services.
They should be able to see that the two groups are either completely the same or completely different, and if it is on the negative side of the difference, we said that the feature is not good for data redundancy.
And even if there is no difference at all, we say, even if we bring this new feature, nothing happens, so this a/b testing framework is quite robust in a way and a very common Data engineer interview question that you’ve enjoyed working on as a data analyst or do you expect to sit behind this data analysis and the role of not knowing the a/b testing framework is very important.
- So in this sensitivity, there’s one of those methods or a similar matrix that we normally evaluate. I’ll show you something that normally refers to the confusion matrix name, so I’ll spend some time explaining that.
- So let’s say you’re building a model directly to predict whether a particular customer will come out of my platform within a month or not.
- It is a very simple problem statement that can have a lot of variables that we would put in.
- Then we’d end up building this model, and this is my final model that says with 90% accuracy if my customer buys from my platform, which is an online e-commerce platform, within the next month or not so.
- So now, without going into the details of the model, let’s assume that after you create the model, you have results.
- So while we analyze and evaluate the result, we can come up with a confusion matrix, so what it says when you create a model that follows the way of supervised learning.
- You will see from the historical data whether people bought on the platform during the next month, whether they bought or not.
- So, of course, I can create a really good training data set that will have information after people make their first transaction with me in the next month, whether they’re buying another product or not.
Big Data Solution
- So, I train my model with this data set, and the confusion matrix says that my actual data says something and you predicted something correctly.
- My actual data says that the customer will buy, and you also predict the same, so we call it a true positive prediction in a positive direction. So let’s get a little bit into the details.
- When we say that purchase is now like moving it or diagonally across from this TP, which is this TN just negative, your model’s prediction that the customer won’t buy matches the actual data and is accurate to a large extent.
- So both of these values are true positives, and true negatives for these two cases are correct predictions from your model. Consider the off-diagonal elements, false negatives, and positives.
- In those two cases, it’s a bit of a mistake because your real data says the customer won’t buy this house. Still, your forecast says he will accept, so the forecast is positive, while the actual data is on the negative side, so it’s a false forecast.
- So you have your, in this case, your FP, which is false positive cases, and on the off-diagonal element, if you look at this one which is FN which is your false negative for cases where you assume the customer won’t buy but on the real data it says that the customer brought the product.
- In this case, the model is wrong, so type 1 and type 2 errors must be taken care of when you build any machine learning model; if those errors are low, your model runs with 100% accuracy.
- But normally, every machine learning model has its limitations. Specifically, there is one metric that we prefer we call sensitivity. What happens is that these true positives and true negatives have to be properly controlled.
Big Data Analytics
- Suppose my model is very good and true positive, like positive cases where the customer buys but does a very bad job, then the customer doesn’t buy cases.
- When the customer doesn’t buy, then the model has some issues in one place, it does just fine, but if it’s very bad on the other one, so I have to find that by some bad trick, the sensitivity helped us find out, which is put as the ratio between the true positive in the denominator.
We have all the cases of optimistic predictions.
- Now imagine if the type 1 error increases, my sensitivity decreases, so if my true positives are very high, the sensitivity will be high too.
- So this is statistical power if that sensitivity is really good. I would say that my positive cases are predicted well, and the exact opposite of sensitivity is what we know from specificity.
- So we need to ensure that the modeling model’s sensitivity and specificity are balanced in a very good machine.
- So it would look like this: the ratio of true positives to the total number of positive events there. As I mentioned, both of these sensitivities and specificities play a good role when properly evaluating the models’ output.
- So these Data engineer interview questions can immediately follow when people ask you about sensitivity and specificity because you know it’s the output of machine learning models. Right after the model is done, you understand whether the model is good or not.
- So in these cases, we also run into issues like overfitting or underfitting, so these words are very common, and the idea depends on the complexity of your model. You can see that you want to fit your data points very precisely, or you might want to make generalizations. So, for example, I have these red and blue dots right here.
- Suppose I draw this curve that separates the red from the blue, and when that separation happens, I’m building a classifier using some modeling technique but now imagine drawing a smooth curve like the one listed in black. Maybe you’re generalizing too much; by that, I mean there might be some Junes on the other side of that border dots, of course. In that case, you can see it, but at the moment, I’m a little more flexible if I draw this green border that covers it all.
- The green border took care of these red dots on the other side of the border. When we build any model properly, the idea is that you have to generalize to the pattern found in the data, so if you don’t make that generalization well, you’re underfitting, but if you make that generalization too specific, you’re overfitting, so some polynomial might represent that curve, but that zigzag kind of polynomial might be a little more complicated than a smooth curve like the one shown in black, so when building the model you have to be very careful, especially in the cases of regression models where a straight line and a polynomial represent it.
- You need to ensure that the polynomial is not so complex and, at the same time, not so simple. You either end up in an overfitting or underfitting situation, so we have to have a good balance between these two.
- Most questions would be about basic statistical properties because you would be very aware of things like means, standard deviation, how to interpret media, and how to interpret quartiles correctly the first quartile, second quartile, and soon.
- These are some basic questions that are a little more complex. They can be over-sensitivity discussions under customization, so you want to prepare maybe from a basic level using properties like average standard deviations, etc.
Like overfitting, underfitting sensitivity, and specificity statistics kind of ideas, it will take your stomach a little more. You’re stronger going into interviews, and that’s at least the bare minimum to understand in these statistical terms if something less than what you would like. You face some difficulties in the interview, but now let’s talk about the Data engineer interview questions, which are related a little bit more to data analysis, so let’s look at how data analysis questions might come up in an interview.
Analyze Structured Data
- So some general Data engineer interview questions like this, people usually analyze structured data in rows and columns. Still, there can be cases where the data is not so well structured, and in those places, the data can be text.
- For example, on Twitter, if you did any, Algorithms like sentiment analysis are pretty commonly known algorithms, so in that case, sentiment analysis could be for a brand for an election campaign or maybe something else.
- Your product features and so on, so text analytics in your own or a little bit of a great domain and Python-like as well as several libraries.
Hadoop Distributed File System
- In particular, they have libraries like TM, a straight-up whining Python package. There are so many packages like Pandas, like the NumPy ones, and also packages like NLTK, which is only built for natural language processing so that it can deal with many different kinds of approaches to text mining or text analysis approaches.
- So if you’re talking about it, as I said, the robustness in Python is much more than in R, but in terms of o functions, they’re both powerful enough with the libraries and packages it offers.
- When doing any analysis, one of the basic starting places is when you’re given a set of data. You’re asked to do a basic analysis of what that data tells you, which can usually be Data engineer interview questions like I’m in retail. My sales are down in a certain regions.
This concept can be useful for Data engineer interview questions.
- Understand the problem with the drop in sales, so if it’s the type of data you’ve been given, maybe you should first look at the transactional information in the system. Then you might also want to go outside of your network. Perhaps you can get the sentiments of your customers from social media platforms and so on, so there will be different sources of data that you will collect, but often collecting data is not only the right task, and not as Building a model or doing statistical analysis could come at this stage very later.
- But what comes before you collect your data is to ensure that the integrity of the data is maintained to get rid of any unwanted noise from the data, and finally, prepare the data to do some modeling exercises or to do descriptive analysis.
- So cleaning and understanding the data and doing a lot of graph exploration takes almost 70 to 80 percent of your time in any data analytics task.
- So if a company maintains the data well in a structured way, this heavy time that we spend on data analysis can be prepared, where data cleaning could be reduced. Otherwise, it would help if you used it for any new project you are taking up for which the data was unavailable. So it’s very important; if you don’t clean the part and understand the data well, the analysis, or the models, you create, you can end up with very poor performance.
- It is very important; as I said, 80% of the time people normally spend on this task, correctly and often when you analyze things like in the example I told my prod they’re going down, what do I do, it’s not possible to come up with answers to complex problems like this with only one variable, so you might want to go beyond one variable at some point.
- Let’s say how to do multivariate or bivariate analysis, so often these Data engineer interview questions come up when you want us to distinguish between this univariate and multivariate analysis. The idea is very simple in any analysis. It is not only one variable that decides the final output of your analysis; more factors are involved.
- So if multiple factors are involved, you can also look at correlation.
- There are many variables. Now, you want to see if there is any correlation between these things.
- “Suppose sales are going down because my sales reps aren’t going to the market, my products are bad, or there are some other reasons.”
- With all the variables in one place, you might want to go deeper to find out whether there are any relationships between the variables or not. When we combine all those variables and analyze the problem, you will get clear answers to what you’re trying to analyze properly.
- There are also times when people do some clustering directly with the data sample, you get a dataset on your system or whatever server you’re analyzing, but it can be locked many times if even randomly selecting the right representative from the population.
- You can reduce in those cases, analyze the problem with only five regions in my mind and with five areas in which I create different clusters or in systematic sampling.
- You can also say that with my five regions, I might want to properly analyze only one product that does not do well in detail.
- So these kinds of sampling techniques are like basic cluster or systematic sampling techniques, and there are different names for those who might be able to give a very good interpretation of what went wrong.
- One more example is sales decline, but you can adapt it to other analyses. Still, the idea is instead of doing random sampling, and we’re not too sure what kind of data is coming into the dataset we want to use for analysis.
- But suppose you do it based on clusters or clusters or systematic sampling. In that case, you know exactly which clusters or areas in this example you want to analyze, and at the end of the analysis, I will be able to say that this is not like a random sample I took.
- But from these five areas, there are many ways to do cluster order grouping or kind of systematic sampling that helps in that particular result of your analysis in the right perspectives instead of random sampling.
- Another useful idea broadly borrowed from linear algebra relates to what we saw earlier between the transition from one variable to more.
- A concept borrowed from linear algebra for variables that directly have eigenvalues and eigenvectors.
- It helps us in some way to approximate a linear combination of different variables. For example, a given data set may have many columns in some complex analyses.
- Suppose that you have a data set with 1 million rows and, say, 10,000 columns, so in those 10,000 columns, there are some complex problems like that.
- But mostly, like most of the time, not all 10,000 variables are useful directly to the input variables, so what we can do is that we might want to transform this dataset into a lower dimensional space.
- Thich means that these 10,000 columns can be reduced to, say, only 100 columns, so eigenvalue and eigenvectors are these ideas that help us in this transformation.
- And the idea is whether these 100 variables can be represented as some linear combination of 10,000 variables, and if I can do that, my dimensionality is reduced.
- The time I need to do the analysis is also reduced, and the ability to represent that comes with only 100 variables will go up.
- So a pretty strong idea, eigenvalues, and eigenvectors, and like I said, eigenvectors are like a linear combination of many different variables. These calculations around ego vectors normally happen for a correlation or covariance kind of matrix.
- This, as you know, measure correlation is also about how two variables are related or how well two variables are correlated correctly. That’s why we also say that eigenvectors can help us compress the data we have because one eigenvector can accurately represent 100 columns of 100 variables.
Large Datasets like PCA
So it kind of works, it’s a pretty powerful idea, and there are commonly used methods to minimize the dimensionality of a large dataset like PCA. The Principal Component Analysis is based on similarity values and eigenvectors.
- So if someone asks you about eigenvalue and eigenvectors and interview, also talk about PCA principal component analysis based on these two concepts since these are highly related.
- So it will give him a good idea for the interview. You can also think about their application, like in PCA.
- So we’re talking about these false positives and false negatives in our parent matrix example, so it’s the same thing that you were also talking about type 1 and type 2.
- Okay, but let’s also drill down now and, say, examples or scenarios where false positives are important in scenarios where false positives are important.
We’re even allowed to make a mistake in one of the positive or negative cases, for example, given here.
Drug Domain Example
- If I take an example in the surgical domain, where we have, let’s say, a process called chemotherapy, which is commonly given to cancer patients, which is a radioactive type of therapy that kills the cancer cells, so it’s a very targeted therapy to the cancer cells.
- Let’s say you’re building a model to detect cancer directly from a CT scan, and that model obviously wouldn’t be 100 percent correct. The whole machine learning model has its limitations.
- But here, you are required to predict whether the patient has cancer, and based on that, the radiologist could decide whether chemotherapy is right for this patient.
- Now imagine if you predicted that someone would have cancer. Still, the patient doesn’t have cancer cells there, so in those cases, you might say, let’s do chemotherapy. Still, the side effects of chemotherapy are very adverse because you’re giving these therapies to healthy cells.
Interviews can never be predictable, and you should prepare your best and hope for the best. It is good practice to stay calm during the interview and focus on what you know. Data science can be tricky so being present is extremely important.
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