## What Is Time Series Analysis And Where Can You Use It

With this question, the interviewer wants to gauge your background knowledge of some basic methods used for analysis.

Example:âTSA is a type of statistical analysis that deals with trend analysis and time-series data. It is useful for understanding how an asset or variable changes over time. TSA involves data at particular intervals of time or set period. You can use TSA for astronomy, weather forecasting, signal processing, earthquake prediction and applied science.â

## How Would You Describe A P

P-value in statistics is calculated during hypothesis testing, and it is a number that indicates the likelihood of data occurring by a random chance. If a p-value is 0.5 and is less than alpha, we can conclude that there is a probability of 5% that the experiment results occurred by chance, or you can say, 5% of the time, we can observe these results by chance.

## How Do You Work Towards A Random Forest

The following algorithm is used to construct a random forest:

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## How Do You Explain The Law Of Large Numbers In Statistics

Inference from statistical data can be said to follow the law of large numbers, which purports that, as the number of trials increases, the average result will increase in proportion to it. The percentage of heads obtained by repeatedly flipping a fair coin is lower the more times it is flipped, 100,000 times in this example.

## Write A Query To Return Data To Support Or Disprove This Hypothesis

Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric

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## How Is The Statistical Significance Of An Insight Assessed

Hypothesis testing is used to find out the statistical significance of the insight. To elaborate, the null hypothesis and the alternate hypothesis are stated, and the p-value is calculated.

After calculating the p-value, the null hypothesis is assumed true, and the values are determined. To fine-tune the result, the alpha value, which denotes the significance, is tweaked. If the p-value turns out to be less than the alpha, then the null hypothesis is rejected. This ensures that the result obtained is statistically significant.

## Interview Questions On Statistics For Data Scientists

We frequently come out with resources for aspirants and job seekers in data science to help them make a career in this vibrant field. Cracking interviews especially where understating of statistics is needed can be tricky. Here are 40 most commonly asked interview questions for data scientists, broken into basic and advanced.

Here are some other interview questions resourcesfor data scientists.

#### THE BELAMY

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## Q: Theres One Box Has 12 Black And 12 Red Cards 2nd Box Has 24 Black And 24 Red If You Want To Draw 2 Cards At Random From One Of The 2 Boxes Which Box Has The Higher Probability Of Getting The Same Color Can You Tell Intuitively Why The 2nd Box Has A Higher Probability

The box with 24 red cards and 24 black cards has a higher probability of getting two cards of the same color. Lets walk through each step.

Lets say the first card you draw from each deck is a red Ace.

This means that in the deck with 12 reds and 12 blacks, theres now 11 reds and 12 blacks. Therefore your odds of drawing another red are equal to 11/ or 11/23.

In the deck with 24 reds and 24 blacks, there would then be 23 reds and 24 blacks. Therefore your odds of drawing another red are equal to 23/ or 23/47.

Since 23/47 > 11/23, the second deck with more cards has a higher probability of getting the same two cards.

## Take A Few Minutes To Explain How You Would Estimate How Many Shoes Could Potentially Be Sold In New York City Each June

Many interviewers pose questions that let them see an analysts thought process without the aid of computers and data sets. After all, technology is only as good and reliable as the people behind it. What to look for in an answer:

- Ability to identify variables/data segments
- Ability to communicate thought process

**Example:**

First, I would gather data on how many people live in New York City, how many tourists visit in June and the average length of stay. Id break down the numbers by age, gender and income, and find the numbers on how many shoes they may already have. Id also figure out why they might need new shoes and what would motivate them to buy.

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## What Is An Rnn

A recurrent neural network is a kind of artificial neural network where the connections between nodes are based on a time series. RNNs are the only form of neural networks with internal memory and are often used for speech recognition applications.

#### Get To Know Other Data Science Students

Sam Fisher

## What Are Descriptive Statistics

Descriptive statistics are used to summarize the basic characteristics of a data set in a study or experiment. It has three main types

- Distribution refers to the frequencies of responses.
- Central Tendency gives a measure or the average of each response.
- Variability shows the dispersion of a data set.

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## What Are The Biggest Challenges Youve Encountered In Data Analytics And How Did You Address Them

This is an opportunity to reveal what youve learned as a data analyst at a personal level. Its a great question to have a meaningful discussion about the challenges in data analytics. Be open and tell your story. The quality of data is a huge problem for analysts. Incomplete, inconsistent, error-prone or badly formatted data sucks a lot of the data analysts time and energy. Give examples from your own personal projects to support this point.

Also, remember to mention how you solved them. Whether you spent extra time in data cleaning, or wrote scripts to automate it, or re-structured data collection processes, talk about it. Dont just highlight the issues, also present possible solutions.

## What Are The Major Industries In Chicago

One of the main reasons for the development of Chicago in several industries is its central location. Major industries in Chicago include technology, the food industry, business, and professional services, among others. With the increase in tech companies settling in the city, there is a high demand for professionals with Data Analytics training in Chicago.

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## Why Must You Update An Algorithm Regularly How Frequently Should You Update It

It is important to keep tweaking your machine learning algorithms regularly. The frequency with which you update them will depend on the business use case. For example, fraud detection algorithms need to be updated regularly. But if you need to study manufacturing data using machine learning, then those models need to be updated much less regularly.

## What Do You Understand By An Inlier In Statistics

An inlier is a data point within a data set that lies at the same level as the rest of the data set. It isn’t easy to find an inlier in the dataset compared to an outlier as it requires external data.

Similar to outliers, inliers also reduce the model accuracy. Unlike outliers, inlier is hard to find and often requires external data for accurate identification. So, it is usually an error, and we have to remove it to improve the model accuracy. This is mainly done to maintain the model accuracy at all times.

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## Q7 What Is The Difference Between 1

You can answer this question, by first explaining, what exactly T-tests are. Refer below for an explanation of T-Test.

T-Tests are a type of hypothesis tests, by which you can compare means. Each test that you perform on your sample data, brings down your sample data to a single value i.e. T-value. Refer below for the formula.

**Fig 7:** Formula to calculate t-value Data Analyst Interview Questions

Now, to explain this formula, you can use the analogy of the signal-to-noise ratio, since the formula is in a ratio format.

Here, the numerator would be a signal and the denominator would be the noise.

So, to calculate 1-Sample T-test, you have to subtract the null hypothesis value from the sample mean. If your sample mean is equal to 7 and the null hypothesis value is 2, then the signal would be equal to 5.

So, we can say that the difference between the sample mean and the null hypothesis is directly proportional to the strength of the signal.

Now, if you observe the denominator which is the noise, in our case it is the measure of variability known as the standard error of the mean. So, this basically indicates how accurately your sample estimates the mean of the population or your complete dataset.

So, you can consider that noise is indirectly proportional to the precision of the sample.

Now, the ratio between the signal-to-noise is how you can calculate the T-Test 1. So, you can see how distinguishable your signal is from the noise.

## What Are The Criteria That Binomial Distributions Must Meet

Here are the three main criteria that Binomial distributions must meet

- The number of observation trials must be fixed. It means that one can only find the probability of something when done only a certain number of times.
- Each trial needs to be independent. It means that none of the trials should impact the probability of other trials.
- The probability of success remains the same across all trials.

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## What Is Root Cause Analysis How To Identify A Cause Vs A Correlation Give Examples

**Root cause analysis: **a method of problem-solving used for identifying the root cause of a problem

**Correlation **measures the relationship between two variables, range from -1 to 1. **Causation **is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.

Example: a higher crime rate is associated with higher sales in ice cream in Canada, aka they are positively correlated. However, this doesnt mean that one causes another. Instead, its because both occur more when its warmer outside.

You can test for causation using hypothesis testing or A/B testing.

## What Does A Degree Of Freedom Represent In Statistics

The t-distribution is used to calculate degrees of freedom and not the z-distribution. When speaking about degrees of freedom, we are referring to the number of options at our disposal when conducting an analysis.

The t-distribution will shift closer to a normal distribution as DF increases. If DF is greater than 30, this means that the t-distribution at hand has all of the characteristics of a normal distribution.

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## What Is Your Greatest Achievement

My greatest achievement was being featured in the Top 30 under 30 best professionals of the year, owing to the amazing insights I obtained for my company. It opened several doors and gave me a chance to see how rewarding my hard work was. This was my first post-college award and brought with it lots of nostalgia. My manager was pleased and, together with the company, awarded me a paid two-week vacation to Bali. Even though I have won several awards afterward, this remains my best. It evokes fond memories.

## Describe A Time When You Solved A Conflict At Work

This question assesses your ability to remain objective at work, that you communicate effectively in challenging situations, and that you remain calm under fire. Heres an example response:

In my previous job, I was the project manager on a dashboard project. One of the BI engineers wasnt meeting the deadlines I had laid out, and I brought that up with him. At first, he was defensive and angry with me. But I listened to his concerns about the deadlines and asked what I could do to help. From our conversation, I learned he had a full workload in addition to this project. I talked with the engineering manager, and we were able to reduce some of his workload. He caught up quickly and we were able to finish the project on time.

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## What Is A Decision Tree

are a tool used to classify data and determine the possibility of defined outcomes in a system. The base of the tree is known as the root node. The root node branches out into decision nodes based on the various decisions that can be made at each stage. Decision nodes flow into lead nodes, which represent the consequence of each decision.

## What Is A Normal Distribution

A normal distribution, also called Gaussian distribution, is one that is symmetric about the mean. This means that half the data is on one side of the mean and half the data on the other. Normal distributions are seen to occur in many natural situations, like in the height of a population, which is why it has gained prominence in the world of data analysis.

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## Post Graduate Program In Data Analytics Chicago

In 2019, Chicago was the most populated city in Illinois and the third most populous in the United States, after New York and Los Angeles, with an estimated population of 2,693,976. The two branches of the Chicago River- the north and the south branch, divides the city roughly in thirds. Chicago has a different image in every direction. One of its most attractive aspects is the city’s well-used parks and other public amenities along the coast. Often Chicago has a calm and pleasant breeze.

Chicago’s gross domestic product as of 2019 amounted to $618,62 billion, with a GDP per capita amounting to $65,407.

Chicago is undoubtedly one of the best tourist destinations in the US. There are some of the world’s biggest museums and parks in Chicago. Here are a few places that you should definitely visit while in Chicago:

## Why Did You Opt For A Data Science Career

Tell them how you got passionate about data science. You can share a quick story or talk about a specific area that served as your gateway to data science, such as statistical analysis or Python programming.

Then, talk about your backgroundyour college degree, previous companies youve worked at, and data science courses that youve completed.

Finally, relate your interests to the organizations needs, and explain how your expertise in data science can help the company solve its challenges.

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## What Do You Understand By Covariance

Covariance is a measure that specifies how much two random variables vary together. It indicates how two variables move in sync with each other. It also specifies the direction of the relationship between two variables. There are two types of Covariance: positive and negative Covariance. The positive Covariance specifies that both variables tend to be high or low simultaneously. On the other hand, the negative Covariance specifies that the other tends to be below when one variable is high.

## Write The Python Code To Create An Employees Data Frame From The Empcsv File And Display The Head And Summary

To create a DataFrame in Python, you need to import the Pandas library and use the read_csv function to load the .csv file. Give the right location where the file name and its extension follow the dataset.

To display the head of the dataset, use the head function.

The describe method is used to return the summary statistics in Python.

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## Q5 What Is The Difference Between Univariate Bivariate And Multivariate Analysis

The differences between univariate, bivariate and multivariate analysis are as follows:

**Univariate:**A descriptive statistical technique that can be differentiated based on the count of variables involved at a given instance of time.**Bivariate:**This analysis is used to find the difference between two variables at a time.**Multivariate:**The study of more than two variables is nothing but multivariate analysis. This analysis is used to understand the effect of variables on the responses.

## 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. If you think you are not the greatest data storyteller, make sure youre making efforts in that direction, e.g. through additional training.

#### Example Answer

Over time, Ive had plenty of opportunities to enhance my writing skills, be it through email communication with coworkers, or through writing analytical project summaries for the upper management. I believe I can interpret data in a clear and succinct manner. However, Im constantly looking for ways to improve my writing skills even further.

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