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Data science interviews consist of questions from statistics and probability, Linear Algebra, Vector, Calculus, Machine Learning/Deep learning mathematics, Python, OOPs concepts, and Numpy/Tensor operations. Apart from these, an interviewer asks you about your projects and their objective. In short, interviewers focus on basic concepts and projects.

This article is part 1 of the data science interview series and will cover some basic data science interview questions. We will discuss the interview questions with their answers:

What is OLS? Why, and Where do we use it?

OLS (or Ordinary Least Square) is a linear regression technique that helps estimate the unknown parameters that can influence the output. This method relies on minimizing the loss function. The loss function is the sum of squares of residuals between the actual and predicted values. The residual is the difference between the target values and forecasted values. The error or residual is:

Minimize ∑(yi – ŷi)^2

Where ŷi is the predicted value, and yi is the actual value.

We use OLS when we have more than one input. This approach treats the data as a matrix and estimates the optimal coefficients using linear algebra operations.

What is Regularization? Where do we use it?

Regularization is a technique that reduces the overfitting of the trained model. This technique gets used where the model is overfitting the data.

Overfitting occurs when the model performs well with the training set but not with the test set. The model gives minimal error with the training set, but the error is high with the test set.

Hence, the regularization technique penalizes the loss function to acquire the perfect fit model.

What is the Difference between L1 AND L2 Regularization?

L1 Regularization is also known as Lasso(Least Absolute Shrinkage and Selection Operator) Regression. This method penalizes the loss function by adding the absolute value of coefficient magnitude as a penalty term.

Lasso works well when we have a lot of features. This technique works well for model selection since it reduces the features by shrinking the coefficients to zero for less significant variables.

Thus it removes some features that have less importance and selects some significant features.

L2 Regularization( or Ridge Regression) penalizes the model as the complexity of the model increases. The regularization parameter (lambda) penalizes all the parameters except intercept so that the model generalizes the data and does not overfit.

Ridge regression adds the squared magnitude of the coefficient as a penalty term to the loss function. When the lambda value is zero, it becomes analogous to OLS. While lambda is very large, the penalty will be too much and lead to under-fitting.

Moreover, Ridge regression pushes the coefficients towards smaller values while maintaining non-zero weights and a non-sparse solution. Since the square term in the loss function blows up the outliers residues that make the L2 sensitive to outliers, the penalty term endeavors to rectify it by penalizing the weights.

Ridge regression performs better when all the input features influence the output with weights roughly equal in size. Besides, Ridge regression can also learn complex data patterns.

What is R Square?

R Square is a statistical measure that shows the closeness of the data points to the fitted regression line. It calculates the percentage of the predicted variable variation calculated by a linear model.

The value of R-Square lies between 0% and 100%, where 0 means the model can not explain the variation of the predicted values around its mean. Besides, 100% indicates that the model can explain the whole variability of the output data around its mean.

In short, the higher the R-Square value, the better the model fits the data.

Adjusted R-Squared

The R-square measure has some drawbacks that we will address here too.

The problem is if we add junk independent variables or significant independent variables, or impactful independent variables to our model, the R-Squared value will always increase. It will never decrease with a newly independent variable addition, whether it could be an impactful, non-impactful, or insignificant variable. Hence we need another way to measure equivalent RSquare, which penalizes our model with any junk independent variable.

So, we calculate the Adjusted R-Square with a better adjustment in the formula of generic R-square.

What is Mean Square Error?

Mean square error tells us the closeness of the regression line to a set of data points. It calculates the distances from data points to the regression line and squares those distances. These distances are the errors of the model for predicted and actual values.

The line equation is given as y = MX+C

M is the slope, and C is the intercept coefficient. The objective is to find the values for M and C to best fit the data and minimize the error.

Why Support Vector Regression? Difference between SVR and a simple regression model?

The objective of the simple regression model is to minimize the error rate while SVR tries to fit the error into a certain threshold.

Main Concepts:



Support Vector


The best fit line is the line that has a maximum number of points on it. The SVR attempts to calculate a decision boundary at the distance of ‘e’ from the base hyper-plane such that data points are nearest to that hyper-plane and support vectors are within that boundary line.


The ordinary least squares technique estimates the unknown coefficients and relies on minimizing the residue.

L1 and L2 Regularization penalizes the loss function with absolute value and square of the value of the coefficient, respectively.

The R-square value indicates the variation of response around its mean.

R-square has some drawbacks, and to overcome these drawbacks, we use adjusted R-Square.

Mean square error calculates the distance between points on the regression line to the data points.

SVR fits the error within a certain threshold instead of minimizing it.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


You're reading Data Science Interview Series: Part

Data Science: The 10 Commandments For Performing A Data Science Project

Machine learning has the ultimate goal of creating a model that is generalizable. It is important to select the most accurate model by comparing and choosing it correctly. You will need a different holdout than the one you used to train your hyperparameters. You will also need to use statistical tests that are appropriate to evaluate the results.

It is crucial to understand the goals of the users or participants in a data science project. However, this does not guarantee success. Data science teams must adhere to best practices when executing a project in order to deliver on a clearly defined brief. These ten points can be used to help you understand what it means.

1. Understanding the Problem

Knowing the problem you are trying to solve is the most important part of solving it. You must understand the problem you are trying to predict, all constraints, and the end goal of this project.

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2. Know Your Data

Knowing what your data means will help you understand which models are most effective and which features to use. The data problem will determine which model is most successful. Also, the computational time will impact the project’s cost.

You can improve or mimic human decision-making by using and creating meaningful features. It is crucial to understand the meaning of each field, especially when it comes to regulated industries where data may be anonymized and not clear. If you’re unsure what something means, consult a domain expert.

3. Split your data

What will your model do with unseen data? If your model can’t adapt to new data, it doesn’t matter how good it does with the data it is given.

You can validate its performance on unknown data by not letting the model see any of it while training. This is essential in order to choose the right model architecture and tuning parameters for the best performance.

Splitting your data into multiple parts is necessary for supervised learning. The training data is the data the model uses to learn. It typically consists of 75-80% of the original data.

This data was chosen randomly. The remaining data is called the testing data. This data is used to evaluate your model. You may need another set of data, called the validation set.

This is used to compare different supervised learning models that were tuned using the test data, depending on what type of model you are creating.

You will need to separate the non-training data into the validation and testing data sets. It is possible to compare different iterations of the same model with the test data, and the final versions using the validation data.

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4. Don’t Leak Test Data

It is important to not feed any test data into your model. This could be as simple as training on the entire data set, or as subtle as performing transformations (such as scaling) before splitting.

If you normalize your data before splitting, the model will gain information about the test set, since the global minimum and maximum might be in the held-out data.

5. Use the Right Evaluation Metrics

Every problem is unique so the evaluation method must be based on that context. Accuracy is the most dangerous and naive classification method. Take the example of cancer detection.

We should always say “not cancer” if we want to build a reliable model. This will ensure that we are correct 99 percent of the time.

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6. Keep it simple

It is important to select the best solution for your problem and not the most complex. Management, customers, and even you might want to use the “latest-and-greatest.” You need to use the simplest model that meets your needs, a principle called Occam’s Razor.

This will not only make it easier to see and reduce training time but can also improve performance. You shouldn’t try to kill Godzilla or shoot a fly with your bazooka.

7. Do not overfit or underfit your model

Overfitting, also called variance, can lead to poor performance when the model doesn’t see certain data. The model simply remembers the training data.

Bias, also known as underfitting, is when the model has too few details to be able to accurately represent the problem. These two are often referred to as “bias-variance trading-off”, and each problem requires a different balance.

Let’s use a simple image classification tool as an example. It is responsible for identifying whether a dog is present in an image.

8. Try Different Model Architectures

It is often beneficial to look at different models for a particular problem. One model architecture may not work well for another.

You can mix simple and complex algorithms. If you are creating a classification model, for example, try as simple as random forests and as complex as neural networks.

Interestingly, extreme gradient boosting is often superior to a neural network classifier. Simple problems are often easier to solve with simple models.

9. Tune Your Hyperparameters

These are the values that are used in the model’s calculation. One example of a hyperparameter in a decision tree would be depth.

This is how many questions the tree will ask before it decides on an answer. The default parameters for a model’s hyperparameters are those that give the highest performance on average.

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10. Comparing Models Correctly

Machine learning has the ultimate goal of creating a model that is generalizable. It is important to select the most accurate model by comparing and choosing it correctly.

You will need a different holdout than the one you used to train your hyperparameters. You will also need to use statistical tests that are appropriate to evaluate the results.

Interview: Jeffrey Steefel On Lotro Mines Of Moria, Part Two

(This is Part Two — Part One.)

Game On: Lord of the Rings Online is essentially divided into “books,” where each book consists off a certain amount of content broken into chapters, and you’ve been adding these since the game launched back in April 2007. Prior to Mines of Moria, the game stands at 14 books total, is that right?

Jeffrey Steefel: Yep.

GO: And Mines of Moria extends that by adding another six books. About how much story is packed into each book, and about how long does each one take to complete?

JS: Even more so. We’ve definitely noticed, to your point, that as much as there’are people out that really like playing these gigantic 24 person raids, and everything that entails, there’s a lot of people, specifically in LotRO, who gravitate toward these smaller encounter instances of three people or six people that are very story based, that tend to be around the epic story or something that supports the epic story.

GO: Thank you. I get the whole nine hour thing, but I just can’t do it.

GO: I read something a while back about a fight with some creature in Final Fantasy XI that lasted over 18 hours before the group trying to beat it finally gave up.

JS: Yeah, well in fact the whole point of the new system we have in Moria, the Legendary Item system, the whole point of that really is to provide you with that kind of high level game that you get from raids in terms of being able to obtain powerful items. You know, if I can’t go find 23 of my best friends, coordinate, organize together, and stand around a boss waiting for the right thing to drop 10 times in a row, I’m not going to get that thing that I need. Here you have a whole system designed to help you create your own items and upgrade them over time.

GO: In the expansion you can now rise beyond level 50 to 60. I think a lot of people hear that, but if they’ve never come anywhere near 50 in one of these games, it’s not clear what it means. What happens in that level space in LotRO?

We’ve added new skills for all the existing classes, and two new classes with a host of new skills from levels one to 60. We’ve extended the trait system. We now have trait sets. In addition to collecting traits and equipping them on your character in your own special customized way, which you can of course still do if you want, we also now have trait sets. There are three for each class, where you basically collect them and it’s a predetermined path. There’s eight traits, and if you collect them all you get a significant bonus and a legendary trait. As you collect them along the way, you get bigger and bigger bonuses. This gives people who love the flexibility of traits something a little more predictable if they’re trying to figure out how to get their characters from one point to another.

So, you know, just every system, looking at it and saying, depending on how you’re playing the game and what kind of activities you participate in, there’s new stuff for you to aspire to. That’s really what raising a level cap’s all about.

GO: You mentioned the two new classes, the Runekeeper and Warden.

JS: What the new classes have in common is that they’re both very good for solo play, in addition to complementing a group. But they’re intentionally very good for solo. They both have a kind of new mechanic, even a new interface to use, so the way that you play them is different than the way you play any other class. They’re going to be really fun for experienced players that maybe want to experience the game in a notably different way.

They’re also different just to look at. When you’re standing back and walking through the world and you see a Runekeeper, you’re going to know it’s a Runekeeper because of the way they’re using effects and the way they’re animation works. If it’s a Warden, you’re going to know it because they’re going to be throwing spears and crouching down with a javelin and doing lots of things that other classes don’t. So we wanted to make sure that we distinguished them as well.

The Runekeeper has a really interesting mechanic where they’re tuning their capabilities during battle. It’s called attunement. And so they have a little meter that can go all the way to the left or all the way to the right. As you start using skills, depending on the types of skills you’re using, you can actually push that meter one direction or another. All the way to the left makes you a more and more powerful DPS [damage per second] nuker [a character who can deal massive amounts of damage at a distance]. All the way to the right makes you a more and more powerful healer. You can’t be both at the same time. So you start out with some basic, limited skills on either side, and you start using them, and the more you use them, the more they start to attune you in the direction you’re trying to go, and then more powerful skills become available.

So I could decide that…first of all, for solo, it’s great, because I can make myself heavy DPS, and then I can fairly quickly recharge and push myself over to the healing side to heal myself. In a group it’s great because I can say I’m going to go into an encounter and I know that the first MOB [a non-player character or monster] is really really tough, and we need as much firepower as possible, so I’m just going to immediately start using skills that’ll push me over to the DPS side so I can stand back and nuke the hell out of this MOB. But I also know that we’re going to get to a certain point where we just need to heal. The Runekeeper’s the only class that during battle, you can basically change its spec. It’s not like a switch, of course. You can’t keep jumping back and forth between DPS and healing, because that would be unfair. There’s a cost. It takes a little time, and the stronger you get at one thing, the weaker you get at the other.

And then we have monsters who will respond to the new class capabilities, and it’s part of the overall balance. When you add two new classes to a game, you have to make sure they’re balanced against all of the other classes, and that was a significant amount of work.

Mastering Exploratory Data Analysis(Eda) For Data Science Enthusiasts

This article was published as a part of the Data Science Blogathon


Step by Step approach to Perform EDA

Resources Like Blogs, MOOCS for getting familiar with EDA

Getting familiar with various Data Visualization techniques, charts, plots

Demonstration of some steps with Python Code Snippet

What is that one thing that differentiates one data science professional, from the other?

Not Machine Learning, Not Deep Learning, Not SQL, It’s Exploratory Data Analysis (EDA). How good one is with the identification of hidden patterns/trends of the data and how valuable the extracted insights are, is what differentiates Data Professionals.

1. What Is Exploratory Data Analysis

EDA assists Data science professionals in various ways:-

3 Getting a better understanding of the problem statement

[ Note: the dataset in this blog is being opted as iris dataset]

2. Checking Introductory Details About Data

The first and foremost step of any data analysis, after loading the data file, should be about checking few introductory details like, no. Of columns, no. of rows, types of features( categorical or Numerical), data types of column entries.

Python Code Snippet

Python Code:

data.head()              For displaying first five rows

data.tail()   For Displaying last Five Rows

3. Statistical Insight

This step should be performed for getting details about various statistical data like Mean, Standard Deviation, Median, Max Value, Min Value

Python Code Snippet


  4. Data cleaning

This is the most important step in EDA involving removing duplicate rows/columns, filling the void entries with values like mean/median of the data, dropping various values, removing null entries

Checking Null entries

data.IsNull().sum   gives the number of missing values for each variable

Removing Null Entries

data.dropna(axis=0,inplace=True)     If null entries are there

Filling values in place of Null Entries(If Numerical feature)

Values can either be mean, median or any integer

Python Code Snippet

Checking Duplicates

data.duplicated().sum()  returning total number of duplicates entries

Removing Duplicates


5. Data Visualization

Data visualization is the method of converting raw data into a visual form, such as a map or graph, to make data easier for us to understand and extract useful insights.

The main goal of data visualization is to put large datasets into a visual representation. It is one of the important steps and simple steps when it comes to data science

You Can refer to the blog below for getting more details about Data Visualization

Various Types of Visualization analysis is: a. Uni Variate analysis:

This shows every observation/distribution in data on a single data variable. It can be shown with the help of various plots like Scatter Plot, Line plot,  Histogram(summary)plot, box plots, violin plot, etc.

b. Bi-Variate analysis: c. Multi-Variate analysis:

Scatterplots, Histograms, box plots, violin plots can be used for Multivariate Analysis

Various Plots

Below are some of the plots that can be deployed for Univariate, Bivariate, Multivariate analysis

a. Scatter Plot Python Code Snippet

sns.scatterplot(data[‘sepal_length’],data[‘sepal_width’],hue =data[‘species’],s=50)

For multivariate analysis Python Code Snippet


b. Box Plot Python Code Snippet

c. Violin Plot

More informative, than box plot, and shows full distribution of data

Python Code Snippet

d. Histograms

It can be used for visualizing the Probability density function(PDF)

Python Code Snippet


Email: [email protected]

You can refer to the blog being, mentioned below for getting familiar with Exploratory Data Analysis

Exploratory Data Analysis: Iris Dataset

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 


Citizen Data Scientists: 4 Ways To Democratize Data Science

Analytics vendors and non-technical employees are democratizing data science. Organizations are looking at converting non-technical employees into data scientists so that they can combine their domain expertise with data science technology to solve business problems.

What does citizen data scientist mean?

In short, they are non-technical employees who can use data science tools to solve business problems.

Citizen data scientists can provide business and industry domain expertise that many data science experts lack. Their business experience and awareness of business priorities enable them to effectively integrate data science and machine learning output into business processes.

Why are citizen data scientists important now?

Interest in citizen data science is almost tripled between 2012-2024, as seen below.

Reasons for this growing interest are:

Though there is an increasing need for analytics due to increased popularity of data-driven decision making, data science talent is in short supply. As of 2023, there are three times more data science job postings than job searches.

As with any short supply product in the market, data science talent is expensive. According to the U.S. Bureau of Labor Statistics, the average data science salary is $101k.

Analytics tools are easier-to-use now, which reduces the reliance on data scientists.

Most industry analysts are also highlighting the increased role of citizen data scientists in organizations:

IDC big data analytics and AI research director Chwee Kan Chua mentions in an interview: “Lowering the barriers to allow even non-technical business users to be ‘data scientists’ is a great approach.”

Gartner defined the term and is heavily promoting it

Various solutions help businesses to democratize AI and analytics:

Citizen data scientists first need to understand business data and access it from various systems. Metadata management solutions like data catalogs or self-service data reporting tools can help citizen data scientists with this.

Automated Machine Learning (AutoML): AutoML solutions can automate manual and repetitive machine learning tasks to empower citizen data scientists. ML tasks AutoML tools can automate are

Data pre-processing

Feature engineering

Feature extraction

Feature selection

Algorithm selection & hyperparameter optimization

Augmented analytics /AI-driven analytics: ML-led analytics, where tools extract insights from data in two forms:

Search-driven: Software returns with results in various formats (reports, dashboards, etc.) to answer citizen data scientists’ queries.

Auto-generated: ML algorithms identify patterns to automate insight generation.

No/low-code and RPA solutions minimize coding with drag-and-drop interfaces which helps citizen developers place the models they prepare in production.


BotX’s no-code AI platform can empower citizen data scientists to build solutions faster while reducing development costs. BotX solutions allow developers and data scientists to launch apps and set infrastructure and IT systems through: 

What are best practices for citizen data science projects? Create a workspace where citizen data scientists and data science experts can work collaboratively

Most citizen data scientists are not trained in the foundations of data science. They rely on tools to generate reports, analyze data, create dashboards or models. To maximize citizen data scientists’ value, you should have teams that can support them which also includes data engineers and expert data scientists.

Train citizen data scientists

use of BI/autoML tools for maximum efficiency

data security training to maintain data compliance

detecting AI biases and creating standards for model trust and transparency so that citizen data scientists can establish explainable AI (XAI) systems.

Classify datasets based on accessibility

Due to data compliance issues, all data types should not be accessible to all employees. Classifying data sets that require limited access can help overcome this issue.

Create a sandbox for testing

Sandboxes, software testing environment, which include synthetic data and which are not connected to production environments help citizen data scientists quickly test their models before rolling them to production.

If you still have questions on citizen data science, don’t hesitate to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





How To Get Hired At A Top Seo Agency Part 3: Rocking The Interview

You did it! You got an interview at the SEO agency of your dreams! Since you’ve started reading this series you now know the main things they are looking for in a SEO , you’ve mastered the SEO skills that most are lacking, and now you just need to nail the interview. A big fear for most people being interviewed is getting stumped by the questions they ask.

Well fear no longer!  We asked our panel of SEO experts what questions they ask the most when interviewing people for SEO positions.

I like to ask SEO candidates to explain how search engines work and to get into as much detail as possible. How they describe servers, crawlers, bots, sites, content, links, etc. tells me a lot about their core knowledge and their perception of the industry.

Bonus follow-up question: why hasn’t anyone built that already?

People don’t have to be technical to answer this but it shows great critical thinking as well as a good knowledge of the industry as it is right now.

Define canonicalization- 90% of applicants say “Something that happens at the Vatican?” or “a meta tag?”

My favorite SEO question on an interview-probably this one: “When is last time you did something for the first time?”


I asked Rand of SEOmoz to participate in our SEO series, but he politely declined because they are a software company these days. Which means they don’t hire SEO’s much anymore, but I was not going to let that slow me down. I found this great video of Rand explaining what questions he would ask in an interview for a SEO position.

Your Thoughts?

What questions do you think are best to ask in an SEO interview?

Stay Tuned- Next time we’ll hear examples of what not to do during an interview.

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