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Top 65 Data Analyst Interview Questions And Answers For 2019

In the era of 2.5 Quintillion bytes of data being generated every day, data plays a crucial role in decision making for business operations.

A Data Analyst is a professional whose sole role is to play around with data and gather hidden insights for the benefit of a business.

Data analysis is the process of collecting, cleansing, interpreting, transforming and modeling data to gather insights and generate reports to gain business profits.

It mainly focuses on providing valuable information on data attributes such as data type, frequency etc.

But below are a few criteria which I think are a must to be considered to decide whether a developed data model is good or not: Business data keeps changing on a day-to-day basis, but the format doesn’t change.

As and when a business operation enters a new market, sees a sudden rise of opposition or sees its own position rising or falling, it is recommended to retrain the model.

This method is used to impute the missing attribute values which are imputed by the attribute values that are most similar to the attribute whose values are missing.

It lets users do quantitative analysis, statistical analysis with an intuitive interface for data manipulation, so much so that its usage spans across different domains and professional requirements.

With such kind of a chart, you can visually, see how the value from revenue to the net income is obtained when all the costs are deducted.

As you can see in the above image, data is usually distributed around a central value without any bias to the left or right side.

Also known as the split testing, it is an analytical method that estimates population parameters based on sample statistics.

This test compares two web pages by showing two variants A and B, to a similar number of visitors, and the variant which gives better conversion rate wins.

The differences between univariate, bivariate and multivariate analysis are as follows: Eigenvectors: Eigenvectors are basically used to understand linear transformations.

If your sample mean is equal to 7 and the null hypothesis value is 2, then the signal would be equal to 5.

So, if I have to summarize for you, the 1-Sample T-test determines how a sample set holds against a mean, while the 2-Sample T-test determines if the mean between 2 sample sets is really significant for the entire population or purely by chance.

The different types of hypothesis testing are as follows: To represent a Bayesian Network in the form of Markov Random Fields, you can consider the following examples: Consider two variables which are connected through an edge in a Bayesian network, then we can have a probability distribution that factorizes into a probability of A and then the probability of B.

Now, if you have to convert this into Markov Random Field, the factorization of the similarly structured graph, where we have the potential function of A/B edge and a potential function for A/C edge.

In case you have attended any Data Analytics interview in the recent past, do paste those interview questions in the comments section and we’ll answer them ASAP.

In simple words, SAS can process complex data and generate meaningful insights that would help organizations make better decisions or predict possible outcomes in the near future.

We can sort and then join the data sets on Age by writing the following query: The basic syntax style of writing code in SAS is as follows: To answer this question, you can first answer what exactly a Do loop is.

For the loops you can write the code as follows: Do Index The output would be: Do While The output would be: Do Until The output would be: The ANYDIGIT function is used to search for a character string.

For Example: Consider the following data set: Data Set: ID NAME X1 X2 Y1 X3 Then, X1 — X3 would return X1 X2 X3 and X1 — X3 would return X1 X2 Y1 X3 The trailing @ is commonly known as the column pointer.

So, when we use the trailing @, in the Input statement, it gives you the ability to read a part of the raw data line, test it and decide how can the additional data be read from the same record.

An Input statement ending with @@ instructs the program to release the current raw data line only when there are no data values left to be read from that line.

The following steps occur when a PROC SQL gets executed: We can read the last observation to a new dataset using end = dataset option.

The SUM function returns the sum of non-missing arguments whereas “+” operator returns a missing value if any of the arguments are missing.

Example: In the output, the value of y is missing for 4th, 5th, and 6th observation as we have used the “+” operator to calculate the value of y.

RDBMS is one of the most commonly used databases till date, and therefore SQL skills are indispensable in most of the job roles such as a Data Analyst.

Let us say if we want to display the employeeId, of even records, then you can use the mod function and simply write the following query: where ‘employee’ is the table name.

Similarly, if you want to display the employeeId of odd records, then you can write the following query You can write the below query: NVL(exp1, exp2) and NVL2(exp1, exp2, exp3) are functions which check whether the value of exp1 is null or not.

Similarly, if we use NVL2(exp1, exp2, exp3) function, then if exp1 is not null, exp2 will be returned, else the value of exp3 will be returned.

Websites such as Indeed.com make use of dual axis to show the comparison between two measures and the growth of these two measures in a septic set of years.

To create a calculated field in Tableau, you can follow the below steps: Take a look at the snapshot below: To view the underlying SQL Queries in Tableau, we mainly have two options: According to your question, you must have a country, state, profit and sales fields in your dataset.

Aggregation of data: Aggregation of data refers to the process of viewing numeric values or the measures at a higher and more summarized level of data.

Now, you can aggregate the Age field to determine the average age of participants, or you can disaggregate the data to determine the age at which the participants were most satisfied with their product.

Each story point can be based on a different view or dashboard, or the entire story can be based on the same visualization, just seen at different stages, with different marks filtered and annotations added.

To create a story in Tableau you can follow the below steps: You can embed interactive Tableau views and dashboards into web pages, blogs, wiki pages, web applications, and intranet portals.

This allows people in your organization to view and interact with Tableau views embedded in web pages without having to sign in to the server.

You can do the following to embed views and adjust their default appearance: Now, moving onto something more interesting, I have planned up a set of 5 puzzles, that are most commonly asked in the Data Analyst Interviews.

The analytics industry predominantly relies on professionals who not only excel in various Data Analyzing tools available in the market but also on those professionals who have excellent problem-solving skills.

Since there are 25 cars racing with 5 lanes, there would be initially 5 races conducted, with each group having 5 cars.

We cannot assume that Y1 and Z1 are 2 ndand 3 rdsince it may happen that the rest cars from the group of X1s’ cars could be faster than Y1 and Z1.

So, the cars that finish the 1 stand 2 ndis the 7 thrace are actually the 2 ndand the 3 rdfastest cars among all cars.

You just must pick 1 coin from the 1 ststack, 2 coins from the 2 ndstack, 3 coins from the 3 rdstack and so on till 10 coins from the 10 thstack.

Yet, if stack 1 turns out to be defective, then the total weight would be 1 less then 550 grams, that is 549 grams.

Similarly, if stack 2 was defective then the total weight would be equal to 2 less than 50 grams, that is 548 grams.

The questions that you learned in this article are the most sought-after questions that are asked in the interview which will help you crack your interviews.

11 Artificial Intelligence Interview Questions to Prep for Ahead of Time

Once the stuff of science fiction novels and futuristic movies, Artificial Intelligence (AI) is now very real to us.

Almost 60 percent (57.9) of organizations with Big Data solutions are using AI in some way, it’s predicted that AI and machine learning will impact all segments of our daily lives by 2025 with huge implications for industries ranging from transport and logistics to healthcare, home maintenance, and customer service.

Read More: Understanding Artificial Intelligence With that dramatic increase in reliance on AI, heavy investments are being made in both the technology and the skilled professionals needed to enable implementing and benefitting from the technology.

Meaning, the need for professionals skilled in Artificial Intelligence exists in just about every field imaginable which leads to a strong job outlook and high-paying salaries.

According to Indeed.com, the average salary for a professional with an AI certification is $110k a year in the U.S. Growing adoption, increased demand for certified professionals and solid salaries make a move into AI a wise choice for someone interested in this career field.

Whether you’re considering a career move into the AI domain, or you’re already there and want to move up the career ladder, the future looks bright.

To position yourself for success as a job candidate who stands out from the crowd, you should be pursuing certifications in AI, as well as, preparing ahead of time for crucial job AI interview questions.

Your answer here should show that you recognize the far-reaching and practical applications of AI, but your answer is up to you because your personal understanding of the AI field is what the interviewer is trying to ascertain.

Possibilities include contract analysis, object detection, and classification for avoidance and/or navigation, image recognition, content distribution, predictive maintenance, data processing, automation of manual tasks or data-driven reporting.

It refers to using multi-layered neural networks to process data in increasingly complex ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data, for continual improvement in the ability to recognize and process information.

Image recognition also helps machines to learn (as in machine learning) because the more images that are processed, the better the software gets at recognizing and processing those images.

Supervised learning is a machine learning process in which outputs are fed back into a computer for the software to learn from, for more accurate results the next time.

Artificial intelligence learns, in part, using “if-then” rules, so if you’re not sure your AI education is at the level it should be before you start job hunting, then consider pursuing certification in AI or even a masters program that can prepare you for a career as an Artificial Intelligence Engineer.

'text': '<p>Your answer here should show that you recognize the far-reaching and practical applications of AI, but your answer is up to you because your personal understanding of the AI field is what the interviewer is trying to ascertain.

Possibilities include contract analysis, object detection, and classification for avoidance and/or navigation, image recognition, content distribution, predictive maintenance, data processing, automation of manual tasks or data-driven reporting.</p>'

It refers to using multi-layered neural networks to process data in increasingly complex ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data, for continual improvement in the ability to recognize and process information.

Image recognition also helps machines to learn (as in machine learning) because the more images that are processed, the better the software gets at recognizing and processing those images.<p>'

Top 10 Machine Learning Interview Questions 2019

As per the findings of the latest industry report, jobs in emerging technologies like machine learning, artificial intelligence, and data science rank among the top emerging jobs.

K-means is an unsupervised algorithm that is used for the process of clustering problems and KNN or K nearest neighbors is a supervised algorithm that is used for the process of regression and classification.

You can attribute the missing values in many ways including assigning a unique category, row deletion, substituting with mean/median/mode, employing algorithms that support the support missing values, and forecasting the missing value to name a few.

When the model is provided a large amount of data during training, it starts to learn from the noise and other wrong data from the data set.

The first way is by keeping the model simple, the second way is by using cross-validation techniques and thirdly, by using regularization techniques, for example, LASSO.

Ensemble method refers to the learning algorithms that build classifier sets and then categorize new data points to make a choice of their forecasting.

Some of the critical steps that you should take for achieving a good working model are collecting data, preparing data, selecting a machine learning model, model training, evaluating the model, tuning the parameter, and lastly, prediction.

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