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[FREE] Statistical Interview Questions And Answers Pdf | HOT!
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...[GET] Statistical Interview Questions And Answers Pdf | latest
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...
- 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. Missing value treatment is one of the primary tasks which a data scientist is supposed to do before starting data analysis. There are multiple methods for missing value treatment. If not done properly, it could potentially result in selection bias. Let see few missing value treatment examples and their impact on selection- Complete Case Treatment: Complete case treatment is when you remove an entire row in data even if one value is missing.
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You could achieve a selection bias if your values are not missing at random and they have some pattern. Would you remove all those people? Available case analysis: Let say you are trying to calculate a correlation matrix for data so you might remove the missing values from variables that are needed for that particular correlation coefficient. In this case, your values will not be fully correct as they are coming from population sets. Mean Substitution: In this method, missing values are replaced with the mean of other available values. This might make your distribution biased e. Hence, various data management procedures might include selection bias in your data if not chosen correctly. What would you do if you find them in your dataset? Support Vector Machine Learning Algorithm performs better in the reduced space. It is beneficial to perform dimensionality reduction before fitting an SVM if the number of features is large when compared to the number of observations.- Statistical importance of an insight can be accessed using Hypothesis Testing. Tweet: Data Science Interview questions 1 - How would you create a taxonomy to identify key customer trends in unstructured data? Having done this, it is always good to follow an iterative approach by pulling new data samples and improving the model accordingly by validating it for accuracy by soliciting feedback from the stakeholders of the business. This helps ensure that your model is producing actionable results and improving over the time. You can use the analysis of covariance technqiue to find the correlation between a categorical variable and a continuous variable.
- If yes, why? Outlier values can be identified by using univariate or any other graphical analysis method. If the number of outlier values is few then they can be assessed individually but for a large number of outliers, the values can be substituted with either the 99th or the 1st percentile values. All extreme values are not outlier values.
- The most common ways to treat outlier values — 1 To change the value and bring in within a range 2 To just remove the value. Does it speed up or slow down the training process and why? SVM and Random Forest are both used in classification problems. It is the opposite - if your data might contain outliers then Random forest would be the best choice b Generally, SVM consumes more computational power than Random Forest, so if you are constrained with memory go for the Random Forest machine learning algorithm.
40 Statistics Interview Problems And Answers For Data Scientists
Then why people use more? How many observations you will select in each decision tree in a random forest? Each decision tree has a subset of features but includes all the observations from the dataset. In this case, the answer will be as the tree will include all the observations from the dataset. It's very much obvious that you would mention accuracy as the answer to this question but since logistic regression is not the same as linear regression it will mislead. You should mention how you will use the confusion matrix to evaluate the performance and the various statistics related to it like Precision, Specificity, Sensitivity, or Recall. To solve this kind of problem, we need to know — Can you tell if the equation given below is linear or not? What will be the output of the following R programming code?- How many Pianos are there in Chicago? How often would a Piano require tuning? How much time does it take for each tuning? We need to build these estimates to solve this kind of a problem. For every 20 households there is 1 Piano. Now the question how many pianos are there can be answered. There is no exact answer to this question. It could be once a year or twice a year. You need to approach this question as the interviewer is trying to test your knowledge on whether you take this into consideration or not. Thus a piano tuner works for days in a year. Considering this rate, a piano tuner can tune pianos a year. Thus, piano tuners are required in Chicago considering the above estimates. There are 25 horses of which you want to find out the three fastest horses. What is the minimal number of races needed to identify the 3 fastest horses of those 25?
- Divide the 25 horses into 5 groups where each group contains 5 horses. Race between all the 5 groups 5 races will determine the winners of each group. A race between all the winners will determine the winner of the winners and must be the fastest horse. A final race between the 2nd and 3rd place from the winners group along with the 1st and 2nd place of thee second place group along with the third place horse will determine the second and third fastest horse from the group of The first beaker contains 4 litre of water and the second one contains 5 litres of water. How can you our exactly 7 litres of water into a bucket? Do you think the coin is biased? If a girl is born, they plan for another child. If a boy is born, they stop. Find out the proportion of boys to girls in the city. Probability Interview Questions for Data Science There are two companies manufacturing electronic chip.
- The main 3 difference between these two techniques are as follows - In Principal Components Analysis, the components are calculated as linear combinations of the original variables. In Factor Analysis, the original variables are defined as linear combinations of the factors. Principal Components Analysis is used as a variable reduction technique whereas Factor Analysis is used to understand what constructs underlie the data. In Principal Components Analysis, the goal is to explain as much of the total variance in the variables as possible. The goal in Factor Analysis is to explain the covariances or correlations between the variables. What would happen if you define event incorrectly while building a model? Suppose your target variable is attrition. It's a binary variable - 1 refers to customer attrited and 0 refers to active customer.
- In this case, your desired outcome is 1 in attrition since you need to identify customers who are likely to leave. Let's say you set 0 as event in the logistic regression. Logistic Regression Output. The sign of estimates would be opposite which imply opposite behavior of variables towards target variable as shown in the image above. No change. Sensitivity and Specificity score would be swapped see the image below. No change in Information Value IV of variables. Sensitivity and Specificity What is Fisher Scoring in Logistic Regression?
- Logistic regression estimates are calculated by maximizing the likelihood function. The maximization of the likelihood is obtained by an iterative method called Fisher's scoring. It's an optimization technique. In general, there are two popular iterative methods for estimating the parameters of a non-linear equations. They are as follows - Fisher's Scoring Newton-Raphson Both are similar except that the Newton-Raphson uses matrix of second order derivatives of log-likelihood function and Fisher uses Information Matrix.
- The algorithm completes when the convergence criterion is satisfied or when the maximum number of iterations has been reached. Convergence is obtained when the difference between the log-likelihood function from one iteration to the next is small. It includes some tricky questions which requires hands-on experience.
- 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?
100+ Data Science Interview Questions You Must Prepare For 2021
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.
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How many entries and variables did the data set comprise? What kind of data was included? How to Answer Working with large datasets and dealing with a substantial number of variables and columns is important for a lot of hiring managers. Focus on the size and type of data. The data set comprised more than a million records and variables. My team and I had to work with Marketing data which we later loaded into an analytical tool to perform EDA. In your role as a data analyst, have you ever recommend a switch to different processes or tools? What was the result of your recommendation? When talking about the recommendation you made, give as many details as possible, including your reasoning behind it. This brought on many cases of misinterpreted data that caused significant damage to the overall company strategy. I gathered examples and pointed out that working with data dictionaries can actually do more harm than good.- I recommended that my coworkers depend on data analysts for data access. Once we implemented my recommendation, the cases misinterpreted data dropped drastically. How would you assess your writing skills? When do you use written form of communication in your role as a data analyst? How to Answer Working with numbers is not the only aspect of a data analyst job. Data analysts also need strong writing skills, so they can present the results of their analysis to management and stakeholders efficiently.
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I believe I can interpret data in a clear and succinct manner. Have you ever used both quantitative and qualitative data within the same project? How to Answer To conduct a meaningful analysis, data analysts must use both the quantitative and qualitative data available to them. In surveys, there are both quantitative and qualitative questions, so merging those 2 types of data presents no challenge whatsoever. In other cases, though, a data analyst must use creativity to find matching qualitative data. That said, when answering this question, talk about the project where the most creative thinking was required.- However, I realized I can actually enhance the validity of my recommendations by also implementing valuable data from external survey sources. So, for a product development project, I used qualitative data provided by our distributors, and it yielded great results. What is your experience in conducting presentations to various audiences? How to Answer Strong presentation skills are extremely valuable for any data analyst. Employers are looking for candidates who not only possess brilliant analytical skills, but also have the confidence and eloquence to present their results to different audiences, including upper-level management and executives, and non-technical coworkers. Example Answer "In my role as a Data Analyst, I have presented to various audiences made up of coworkers and clients with differing backgrounds.
- I believe the largest so far has been around 30 people, mostly colleagues from non-technical departments. All of these presentations were conducted in person, except for 1 which was remote via video conference call with senior management. Have you worked in an industry similar to ours? How to Answer This is a pretty straightforward question, aiming to assess if you have industry-specific skills and experience. I think the most prominent one is data security. Both industries utilize highly sensitive personal data that must be kept secure and confidential. This leads to 2 things: more restricted access to data, and, consequently, more time to complete its analysis. This has taught me to be more time efficient when it comes to passing through all the security. Moreover, I learned how important it is to clearly state the reasons behind requiring certain data for my analysis. Have you earned any certifications to boost your career opportunities as a Data Analyst?
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How to Answer Hiring managers appreciate a candidate who is serious about advancing their career options through additional qualifications. Certificates prove that you have put in the effort to master new skills and knowledge of the latest analytical tools and subjects. This is why I recently earned a certification in Customer Analytics in Python. The training and requirements to finish it really helped me sharpen my skills in analyzing customer data and predicting the purchase behavior of clients. Depending on the specifics of the job, you might be requested to answer some more advanced statistical questions, too. Here are some real-world examples: 8. What tools or software do you prefer using in the various phases of data analysis and why?- How to Answer Although you might think you should have experience with as many tools as possible to ace this question, this is not the case. Moreover, you can achieve great results with them with the right training. Have you ever created or worked with statistical models? The model in question was built with the purpose of identifying the customers who were most inclined to buy additional products and predicting when they were most likely to make that decision. My job was to establish the appropriate variables used in the model and assess its performance once it was ready. Which step of a data analysis project do you enjoy the most? How to Answer It's normal for a data analyst to have preferences of certain tasks over others. Instead, use this question to highlight your strengths. Example Answer "If I had to select one step as a favorite, it would be analyzing the data. I enjoy developing a variety of hypotheses and searching for evidence to support or refute them.
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Sometimes, while following my analytical plan, I have stumbled upon interesting and unexpected learnings from the data. I believe there is always something to be learned from the data, whether big or small, that will help me in future analytical projects. How to Answer Data analysts should have basic statistics knowledge and experience. That means you should be comfortable with calculating mean, median and mode, as well as conducting significance testing.
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