YOU WERE LOOKING FOR: Probability And Statistics Interview Questions And Answers
Below are the 4 sampling methods: Cluster Sampling: IN the cluster sampling method, the population will be divided into groups or clusters. Simple Random: This sampling method simply follows the pure random division. Stratified: In stratified...
Data Science and Data mining have similarities, both abstracts useful information from data. By combing aspects of statistics, visualization, applied mathematics, computer science Data Science is turning the vast amount of data into insights and...
Here we have listed the most useful 9 interview sets of questions so that the jobseeker can crack the interview with ease. You may also look at the following articles to learn more-.
Practically, everything you need to know about all levels of preparation. We start with a few general data science interview questions. The rest of the technical and behavioral interview questions are categorized by data science career paths - data scientist, data analyst, BI analyst, data engineer, and data architect. General data science interview questions include some statistics interview questions, computer science interview questions, Python interview questions, and SQL interview questions. Usually, the interviewers start with these to help you feel at ease and get ready to proceed with some more challenging ones. Here are 3 examples. How do data scientists use statistics? However, keep in mind that this is a very tricky question. Not because it is hard to answer - on the contrary. But sometimes the question is not asked for the answer itself, but rather for the way you structure your thought process and express an idea.
One of the better ways to achieve that is to frame the question within a framework. Now, we could simplify this framework by ignoring Mathematics as a pillar, as it is the basis of every science. Then we could assume probability is an integral part of statistics and continue simplifying further until reaching three fairly independent fields: Statistics, Economics, and Programming. Programming is just a tool for materializing ideas into solutions. One could argue that Machine learning is a separate field, but it is actually an iterative, programmatically efficient application of statistics. Models such as linear regression, logistic regression, decision trees, etc. Their predictions are nothing more than statistical inferences based on the original distributions of the data and making assumptions about the distribution of the future values.
Deep learning? Data visualizations also could fall under the umbrella of descriptive statistics. After all, a visualization usually aims to describe the distribution of a variable or the interconnection of several different variables. One notable exception is data preprocessing. Finally, there is an exception to the exception — statistical data preprocessing. While preprocessing tasks in their execution, they require solid statistical knowledge. SAS is one of the most popular analytics tools used by some of the biggest companies in the world. It has great statistical functions and graphical user interface. However, it is too pricey to be eagerly adopted by smaller enterprises or individuals.
R, on the other hand, is a robust tool for statistical computation, graphical representation, and reporting. The best part about R is that it is an Open Source tool. As such, both academia and the research community use it generously and update it with the latest features for everybody to use. In comparison, Python is a powerful open-source programming language. Python has a myriad of libraries and community created modules. Its functions include statistical operation, model building and many more. The best characteristic of Python is that it is a general-purpose programming language so it is not limited in any way. Adding a WHERE clause to a query allows you to set a condition which you can use to specify what part of the data you want to retrieve from the database. So, what data scientist interview questions should you practice? Here are 37 real-life examples. What is a Normal distribution? A distribution is a function that shows the possible values for a variable and how often they occur.
To answer this question, you are likely to need to first define what a distribution is. So, in statistics, when we use the term distribution, we usually mean a probability distribution. Here's one definition of the term: A Normal distribution, also known as Gaussian distribution, or The Bell Curve, is probably the most common distribution. There are several important reasons: It approximates a wide variety of random variables. Distributions of sample means with large enough sample sizes could be approximated to Normal, following the Central Limit Theorem All computable statistics are elegant they really are!!! Decisions based on Normal distribution insights have a good track record. What is very important is that the Normal distribution is symmetrical around its mean, with a concentration of observations around the mean.
Moreover, its mean, median and mode are the same. Now, you may be also expected to give an example. Since many biological phenomena are normally distributed it is going to be the easiest to turn to a biological example. Try to showcase all facts that you just mentioned about a Normal distribution. Let focus on the height of people. You know a few people that are very short and a few people that are very tall. You also know a bit more people that are short but not too short, and approximately an equal amount that are tall, but not too tall.
Most of your acquaintances, though have a very similar height, centered around the mean height of all the people in your area or country. There are some differences which are mainly geographical, but the overall pattern is such. R has several packages for solving a particular problem. How do you decide which one is best to use? R has extensive documentation online. There is usually a comprehensive guide for the use of popular packages in R, including the analysis of concrete data sets. These can be useful to find out which approach is best suited to solve the problem at hand.
Just like with any other script language, it is the responsibility of the data scientist to choose the best approach to solve the problem at hand. The choice usually depends on the problem itself or the specific nature of the data i. Something to consider is the tradeoff between how much work the package is saving you, and how much of the functionality you are sacrificing. It bears also mentioning that because packages come with limitations, as well as benefits, if you are working in a team and sharing your code, it might be wise to assimilate to a shared package culture. What are interpolation and extrapolation? Sometimes you could be asked a question that contains mathematical terms.
This shows you the importance of knowing mathematics when getting into data science. Now, interpolation and extrapolation are two very similar concepts. They both refer to predicting or determining new values based on some sample information. There is one subtle difference, though. What is the number in the blank spot? It is obviously 6. By solving this problem, you interpolated the value. Now, with this knowledge, you know the sequence is 2, 4, 6, 8, 10, What is the next value in line? Well, we have extrapolated the next number in the sequence. Finally, we must connect this question with data science a bit more.
If they ask you this question, they are probably looking for you to elaborate on that. Interpolated values are generally considered reliable, while extrapolated ones - less reliable or sometimes invalid. For instance, in the sequence from above: 2, 4, 6, 8, 10, 12, you may want to extrapolate a number before 2. However, the natural domain of your problem may be positive numbers. In that case, 0 would be an inadmissible answer. It is extremely rare to find cases where interpolation is problematic.
What is the difference between population and sample in data? A population is the collection of all items of interest to our study and is usually denoted with an uppercase N. Further, you can spend some time exploring the peculiarities of observing a population. In general, samples are much more efficient and much less expensive to work with. With the proper statistical tests, 30 sample observations may be enough for you to take a data driven decision.
Finally, samples have two properties: randomness and representativeness. A sample can be one of those, both, or neither. To conduct statistical tests, which results you can use later on, your sample needs to be both random and representative. Consider this simplified situation. There are people in each department, so a total of people. You want to evaluate the general attitude towards a decision to move to a new office, which is much better on the inside, but is located on the other side of the city.
You decide you don't really want to ask people, but is a nice sample. Now, we know that the 4 groups are exactly equal. So, we expect that in those people, we would have 25 from each department. Obviously, the opinion of the Sales department is underrepresented. We have a sample, which is random but not representative. I've been working in this firm for quite a while now, so I have many friends all over it.
What are the different types of sorting algorithms available in R language? There are insertion, bubble, and selection sorting algorithms. Read more here. What are the different data objects in R? What packages are you most familiar with? What do you like or dislike about them? How do you access the element in the 2nd column and 4th row of a matrix named M? Elements can be accessed as var[row, column]. What is the command used to store R objects in a file? There are four different ways of using Hadoop and R together. Read about this here. Write a function in R language to replace the missing value in a vector with the mean of that vector. For example, you could be given a table and asked to extract relevant data, then filter and order the data as you see fit, and finally report your findings. If you do not feel ready to do this in an interview setting, Mode Analytics has a delightful introduction to using SQL that will teach you these commands through an interactive SQL environment.
What is the purpose of the group functions in SQL? Give some examples of group functions. Group functions are necessary to get summary statistics of a data set. If a table contains duplicate rows, does a query result display the duplicate values by default? How can you eliminate duplicate rows from a query result? For additional SQL questions that focus on looking at specific snippets of code, check out this useful resource created by Toptal. Examples of similar data science interview questions found on Glassdoor: 3. Modeling Data modeling is where a data scientist provides value for a company. Turning data into predictive and actionable information is difficult, talking about it to a potential employer even more so. Practice describing your past experiences building models—what were the techniques used, challenges overcome, and successes achieved in the process? The group of questions below are designed to uncover that information, as well as your formal education of different modeling techniques.
Take a look at the questions below to practice. Tell me about how you designed a model for a past employer or client. What are your favorite data visualization techniques? How would you effectively represent data with 5 dimensions? How is k-NN different from k-means clustering? K-means is a clustering algorithm, where the k is an integer describing the number of clusters to be created from the given data.
How would you create a logistic regression model? Have you used a time series model? Do you understand cross-correlations with time lags? Explain what precision and recall are. How do they relate to the ROC curve? Recall describes what percentage of true positives are described as positive by the model. Precision describes what percent of positive predictions were correct. The ROC curve shows the relationship between model recall and specificity—specificity being a measure of the percent of true negatives being described as negative by the model. Recall, precision, and the ROC are measures used to identify how useful a given classification model is. Explain the difference between L1 and L2 regularization methods. The key difference between these two is the penalty term. What is root cause analysis? There are many changes happening in your business every day, and often you will want to understand exactly what is driving a given change — especially if it is unexpected.
Understanding the underlying causes of change is known as root cause analysis. What are hash table collisions? There are a few different ways to resolve this issue. In hash table vernacular, this solution implemented is referred to as collision resolution. What is an exact test? This will result in a significance test that will have a false rejection rate always equal to the significance level of the test. In your opinion, which is more important when designing a machine learning model: model performance or model accuracy? How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression? I have two models of comparable accuracy and computational performance. Which one should I choose for production and why? How do you deal with sparsity? Is it better to spend five days developing a percent accurate solution or 10 days for percent accuracy?
What are some situations where a general linear model fails? Do you think 50 small decision trees are better than a large one? When modifying an algorithm, how do you know that your changes are an improvement over not doing anything? Is it better to have too many false positives or too many false negatives? It depends on several factors. Examples of similar data science interview questions found on Glassdoor: 4.
Past Behavior Employers love behavioral questions. They reveal information about the work experience of the interviewee and about their demeanor and how that could affect the rest of the team. From these questions, an interviewer wants to see how a candidate has reacted to situations in the past, how well they can articulate what their role was, and what they learned from their experience.
Recall measures "Of all the actual true samples how many did we classify as true? One day all of a sudden your wife asks -"Darling, do you remember all anniversary surprises from me? This simple question puts your life into danger. To save your life, you need to Recall all 12 anniversary surprises from your memory. Thus, Recall R is the ratio of number of events you can correctly recall to the number of all correct events. However , you might be wrong in some cases. For instance, you answer 15 times, 10 times the surprises you guess are correct and 5 wrong.
Precision is the ratio of number of events you can correctly recall to a number of all events you recall combination of wrong and correct recalls. Regularizations in statistics or in the field of machine learning is used to include some extra information in order to solve a problem in a better way. In the example shown above H0 is a hypothesis. So in L1 variables are penalized more as compared to L2 which results into sparsity. In other words, errors are squared in L2, so model sees higher error and tries to minimize that squared error. Seasonality makes your time series non-stationary because average value of the variables at different time periods. Differentiating a time series is generally known as the best method of removing seasonality from a time series. Seasonal differencing can be defined as a numerical difference between a particular value and a value with a periodic lag i. Compare hold-out vs k-fold cross validation vs iterated k-fold cross-validation methods of testing.
Before we start, let us understand what are false positives and what are false negatives. False Positives are the cases where you wrongly classified a non-event as an event a. And, False Negatives are the cases where you wrongly classify events as non-events, a. In medical field, assume you have to give chemo therapy to patients. Your lab tests patients for certain vital information and based on those results they decide to give radiation therapy to a patient. What will happen to him? Assuming Sensitivity is 1 One more example might come from marketing. Now what if they have sent it to false positive cases? Due to shortage of staff they decided to scan passenger being predicted as risk positives by their predictive model. What will happen if a true threat customer is being flagged as non-threat by airport model? Another example can be judicial system. What if Jury or judge decide to make a criminal go free? In the banking industry giving loans is the primary source of making money but at the same time if your repayment rate is not good you will not make any profit, rather you will risk huge losses.
In this scenario both the false positives and false negatives become very important to measure. These days we hear many cases of players using steroids during sport competitions Every player has to go through a steroid test before the game starts. A false positive can ruin the career of a Great sportsman and a false negative can make the game unfair. Validation set can be considered as a part of the training set as it is used for parameter selection and to avoid Overfitting of the model being built. On the other hand, test set is used for testing or evaluating the performance of a trained machine leaning model. In simple terms ,the differences can be summarized as- Training Set is to fit the parameters i. Test Set is to assess the performance of the model i. Validation set is to tune the parameters. True events here are the events which were true and model also predicted them as true. Selection bias implies that the obtained sample does not exactly represent the population that was actually intended to be analyzed.
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