What Is A Bias
Bias: Due to an oversimplification of a Machine Learning Algorithm, an error occurs in our model, which is known as Bias. This can lead to an issue of underfitting and might lead to oversimplified assumptions at the model training time to make target functions easier and simpler to understand.
Some of the popular machine learning algorithms which are low on the bias scale are –
Support Vector Machines , K-Nearest Neighbors , and Decision Trees.
Algorithms that are high on the bias scale –
Logistic Regression and Linear Regression.
Variance: Because of a complex machine learning algorithm, a model performs really badly on a test data set as the model learns even noise from the training data set. This error that occurs in the Machine Learning model is called Variance and can generate overfitting and hyper-sensitivity in Machine Learning models.
While trying to get over bias in our model, we try to increase the complexity of the machine learning algorithm. Though it helps in reducing the bias, after a certain point, it generates an overfitting effect on the model hence resulting in hyper-sensitivity and high variance.
Bias-Variance trade-off: To achieve the best performance, the main target of a supervised machine learning algorithm is to have low variance and bias.
The following things are observed regarding some of the popular machine learning algorithms –
Q78 During Analysis How Do You Treat Missing Values
The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights.
If there are no patterns identified, then the missing values can be substituted with mean or median values or they can simply be ignored. Assigning a default value which can be mean, minimum or maximum value. Getting into the data is important.
If it is a categorical variable, the default value is assigned. The missing value is assigned a default value. If you have a distribution of data coming, for normal distribution give the mean value.
If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.
Faqs On Data Scientist Interview Questions
1. What type of questions are asked in a data scientist interview?
Data science interview questions are usually based on statistics, coding, probability, quantitative aptitude, and data science fundamentals.
2. Are coding questions asked at data scientist interviews?
Yes. In addition to core data science questions, you can also expect easy to medium Leetcode problems or Python-based data manipulation problems. Your knowledge of SQL will also be tested through coding questions.
3. Are behavioral questions asked at data scientist interviews?
Yes. Behavioral questions help hiring managers understand if you are a good fit for the role and company culture. You can expect a few behavioral questions during the data scientist interview.
4. What topics should I prepare to answer data scientist interview questions?
Some domain-specific topics that you must prepare include SQL, probability and statistics, distributions, hypothesis testing, p-value, statistical significance, A/B testing, causal impact and inference, and metrics. These will prepare you for data scientist interview questions.
5. Is having a masterâs degree essential to work as a Data Scientist at FAANG?
Based on our research, you can work as a data scientist even though you only have a bachelorâs degree. You can always upgrade your skills via a data science boot camp. But for better career prospects, having an advanced degree may be useful.
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Q: What Is A Random Forest Why Is It Good
Random forests are an ensemble learning technique that builds off of decision trees. Random forests involve creating multiple decision trees using bootstrapped datasets of the original data and randomly selecting a subset of variables at each step of the decision tree. The model then selects the mode of all of the predictions of each decision tree. By relying on a majority wins model, it reduces the risk of error from an individual tree.
For example, if we created one decision tree, the third one, it would predict 0. But if we relied on the mode of all 4 decision trees, the predicted value would be 1. This is the power of random forests.
Random forests offer several other benefits including strong performance, can model non-linear boundaries, no cross-validation needed, and gives feature importance.
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Common Technical Data Science Interviews Questions
Technical skills questions in a data science interview are used to assess your data science knowledge, skills, and abilities. These questions will be related to the specific job responsibilities of the Data Scientist position.
Technical data science interview questions may have one correct answer or several possible solutions.
Remember to show your thought process when solving problems and clearly explain how you arrived at an answer!
Examples of technical data science skill interview questions include:
Question: What are the top tools and technical skills for a Data Scientist?
Answer: Data science is a highly technical field and you will want to show the hiring manager that youre adept with all of the latest industry-standard tools, software, and programming languages.
Out of the various statistical programming languages used in data science, R and Python are most commonly used by Data Scientists. Both can be used for statistical applications such as creating a nonlinear or linear model, regression analysis, statistical tests, data mining, and more. Jupyter Notebook is often used for statistical modeling, data visualizations, machine learning functions, etc.
Of course, there are a number of dedicated data visualization tools used extensively by Data Scientists, including Tableau, PowerBI, and plotting packages in Python such as matplotlib, seaborn, Bokeh, and Plotly. Data Scientists also need plenty of experience using SQL and Excel.
Top Data Science Tools and Skills
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Q102 What Are Recurrent Neural Networks
RNNs are a type of artificial neural networks designed to recognise the pattern from the sequence of data such as Time series, stock market and government agencies etc. To understand recurrent nets, first, you have to understand the basics of feedforward nets.
Both these networks RNN and feed-forward named after the way they channel information through a series of mathematical orations performed at the nodes of the network. One feeds information through straight, while the other cycles it through a loop, and the latter are called recurrent.
Recurrent networks, on the other hand, take as their input, not just the current input example they see, but also the what they have perceived previously in time.
The decision a recurrent neural network reached at time t-1 affects the decision that it will reach one moment later at time t. So recurrent networks have two sources of input, the present and the recent past, which combine to determine how they respond to new data, much as we do in life.
The error they generate will return via backpropagation and be used to adjust their weights until error cant go any lower. Remember, the purpose of recurrent nets is to accurately classify sequential input. We rely on the backpropagation of error and gradient descent to do so.
How Do You Explain Technical Concepts To A Non
What theyâre really asking: How are your communication skills?
While the ability to draw insights from data is a critical skill for a data analyst, being able to communicate those insights to stakeholders, management, and non-technical co-workers is just as important.
Your answer should include the types of audiences youâve presented to in the past . If you donât have a lot of experience presenting, you can still talk about how youâd present data findings differently depending on the audience.
Interviewer might also ask:
What is your experience conducting presentations?
Why are communication skills important to a data analyst?
How do you present your findings to management?
Tip: In some cases, your interviewer might not be involved in data analysis. The entire interview, then, is an opportunity to demonstrate your ability to communicate clearly. Consider practicing your answers on a non-technical friend or family member.
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Data Scientist Interview Question Examples
Hiring managers may categorize their data science interview questions into three groups that allow them to:
The following sets of questions are examples of what data science job interviewers may ask. They are fairly detailed and may be helpful for preparation.
Are Data Science Interviews Difficult
In general, data scientist interviews can be challenging with respect to the technical questions you might expect. This is why it is important to prepare in advance by first researching the company to understand how it uses data analysis and statistics in its daily operations. Understanding what the company expects of the data scientist position can help you get an idea of the types of questions to anticipate.
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How Would You Approach A Dataset Thats Missing More Than 30 Percent Of Its Values
The approach will depend on the size of the dataset. If it is a large dataset, then the quickest method would be to simply remove the rows containing the missing values. Since the dataset is large, this wont affect the ability of the model to produce results.
If the dataset is small, then it is not practical to simply eliminate the values. In that case, it is better to calculate the mean or mode of that particular feature and input that value where there are missing entries.
Another approach would be to use a machine learning algorithm to predict the missing values. This can yield accurate results unless there are entries with a very high variance from the rest of the dataset.
Q: How Do You Prove That Males Are On Average Taller Than Females By Knowing Just Gender Height
You can use hypothesis testing to prove that males are taller on average than females.
The null hypothesis would state that males and females are the same height on average, while the alternative hypothesis would state that the average height of males is greater than the average height of females.
Then you would collect a random sample of heights of males and females and use a t-test to determine if you reject the null or not.
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What Are The Assumptions Required For Linear Regression
There are several assumptions required for linear regression. They are as follows:
- The data, which is a sample drawn from a population, used to train the model should be representative of the population.
- The relationship between independent variables and the mean of dependent variables is linear.
- The variance of the residual is going to be the same for any value of an independent variable. It is also represented as X.
- Each observation is independent of all other observations.
- For any value of an independent variable, the independent variable is normally distributed.
Technical Data Scientist Interview Questions
Technical interview questions often focus on how you approach the challenges Data Scientists face every day. In some cases, the question itself may not specifically allude to any technical tools, but you should still view your answer as an opportunity to show you know which tools and techniques best solve key problems.
Before the interview, it’s a good idea to mentally list the tools you’ve used to solve problems, ranging from data science-specific tools like SQL and Python to general tools like Excel and PowerPoint.
What is selection bias, and why is it important to avoid it?
Selection bias refers to data samples that aren’t randomly selected. Avoiding selection bias is important because insights derived from biased datasets aren’t useful. While answering this question, discuss the tools and methods you’ve used to avoid selection bias, such as weighting, boosting, and resampling data.
Are large datasets always the best choice?
The best way to answer a question about the ideal size of a dataset is to explain how it depends on the context of the situation. Then, provide examples of how different circumstances require different sizes.
For example, large datasets aren’t always cost-effective because they require vast amounts of computational power, human resources, and time to maintain. Plus, there might even be redundancies in your data. In many situations, you can use a smaller dataset without sacrificing the validity of your results.
What is meant by root cause analysis?
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Q121 What Is A Generative Adversarial Network
Suppose there is a wine shop purchasing wine from dealers, which they resell later. But some dealers sell fake wine. In this case, the shop owner should be able to distinguish between fake and authentic wine.
The forger will try different techniques to sell fake wine and make sure specific techniques go past the shop owners check. The shop owner would probably get some feedback from wine experts that some of the wine is not original. The owner would have to improve how he determines whether a wine is fake or authentic.
The forgers goal is to create wines that are indistinguishable from the authentic ones while the shop owner intends to tell if the wine is real or not accurately
Let us understand this example with the help of an image.
There is a noise vector coming into the forger who is generating fake wine.
Here the forger acts as a Generator.
The shop owner acts as a Discriminator.
The Discriminator gets two inputs one is the fake wine, while the other is the real authentic wine. The shop owner has to figure out whether it is real or fake.
So, there are two primary components of Generative Adversarial Network named:
The generator is a CNN that keeps keys producing images and is closer in appearance to the real images while the discriminator tries to determine the difference between real and fake images The ultimate aim is to make the discriminator learn to identify real and fake images.
Implement Logistic Regression On This Heart Dataset In R Where The Dependent Variable Is Target And The Independent Variable Is Age
For loading the dataset, we will use the read.csv function:
In the structure of this dataframe, most of the values are integers. However, since we are building a logistic regression model on top of this dataset, the final target column is supposed to be categorical. It cannot be an integer. So, we will go ahead and convert them into a factor.Thus, we will use the as.factor function and convert these integer values into categorical data.We will pass on heart$target column over here and store the result in heart$target as follows:
Now, we will build a logistic regression model and see the different probability values for the person to have heart disease on the basis of different age values.
To build a logistic regression model, we will use the glm function:
Here, target~age indicates that the target is the dependent variable and the age is the independent variable, and we are building this model on top of the dataframe.
family=binomial means we are basically telling R that this is the logistic regression model, and we will store the result in log_mod1.
We will have a glance at the summary of the model that we have just built:
Now, we have other parameters like null deviance and residual deviance. Lower the deviance value, the better the model.
This basically means that there is a strong relationship between the age column and the target column and that is why the deviance is reduced.
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Q: How To Check If The Regression Model Fits The Data Well
There are a couple of metrics that you can use:
R-squared/Adjusted R-squared: Relative measure of fit. This was explained in a previous answer
F1 Score: Evaluates the null hypothesis that all regression coefficients are equal to zero vs the alternative hypothesis that at least one doesnt equal zero
RMSE: Absolute measure of fit.
How Can You Avoid Overfitting Your Model
Overfitting refers to a model that is only set for a very small amount of data and ignores the bigger picture. There are three main methods to avoid overfitting:
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What Tools And Devices Do You Plan To Use In Your Role As A Data Scientist
The purpose of this question is to determine what programming languages and tools you have experience with. In your answer, you can list the tools you frequently use in addition to describing how you use them to successfully and efficiently complete tasks. Consider discussing a recent project you completed, focusing on a single or set of languages or tools you used to overcome a challenge.
Example:I recently completed an important research project that provided insight into what product design would be more attractive to customers. I had previous experience with SQL and Tableau but was new to FUSE and Python. For this project, I was responsible for gathering and sorting large amounts of data using the FUSE and Tableau platforms for data mining and drawing references. I then used Python to implement algorithms and SQL to update my database when new data was collected. After three months on the project, I expanded my knowledge and application of SQL and Tableau and become proficient in Python, though I am eager to practice with it more.
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What Do You Do To Avoid Selection Bias
The interviewer is likely to ask about selection bias, as this question can help them assess how efficiently you select random data sets to ensure the most effective insights. Use your answer to demonstrate your ability to select methods that keep samples random and allow you to avoid selection bias.
Example:âI avoid selection bias by ensuring the random selection of sample sets with respect to the data, rather than the variables. This makes each sample set I select from a larger population result in more variables that are representative of the population, reducing the risk of selection bias when gaining statistical insights.â
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