Data Science Interview: Top Data Science Interview Questions and Answers

Applying for a data science job? Preparing for the interview can be nerve-wracking. Luckily, a data science job interview is not as bad as you may think. While it may seem intimidating, proper preparation will help eliminate your pre-interview jitters.

Not sure where to start? In this data science interview guide, we’ll teach you which topics you need to study and what some of the most common types of questions are during the interview process

How to Prepare for a Data Science Interview

The best way you can prepare for a data science interview is by prepping answers to potential questions. The more detailed your answers are, the better. One good way to prepare answers is by conducting mock interviews.

Another good way to prepare for these types of interviews is to study the job requirements. What areas of expertise are most important? How can you tackle the job responsibilities? What does the company focus on? Does your portfolio include a recent data science project or analytics project similar to the work that the company does? Make sure to check these boxes before the interview. 

Data Science Interview Prep

Prior to your interview, you should prepare for technical questions by learning about programming languages, software development practices, data science tools, and the data collection and cleaning methods you’ll be expected to use. Below is a more in-depth description of what topics you need to study beforehand.

Programming Languages

Knowing several programming languages is a good way to get hired in the data science industry. With so many programming languages out there, deciding where to start is hard. According to Flatiron School, these are some of the best you can learn for data science careers.

• Python
• JavaScript
• Java
• R
• C/C++
• SQL

You can learn these programming languages on your own. It is completely possible to teach yourself or you can enroll in an online course or bootcamp. Proficiency in Python and SQL are essential skills for data scientists. Be sure to master at least a few programming languages prior to your interview.

Software Tools

Data scientists use a variety of software tools in their daily tasks. According to BrainStation, a well-known data science bootcamp, data scientists use software tools for programming, machine learning, and data visualization.

Some of the most important machine learning tools include H2O.ai, TensorFlow, and Apache Mahout. When it comes to data visualization, data scientists tend to use software programs such as Tableau, Bokeh, and Plotly. There are many other data science software tools out there as well, but learning these will make you a more appealing job candidate.

Data Collection and Data Cleaning

You will also need to know about data collection and cleaning, as both data science principles are central to most of the best data science jobs. There are various methods you can use to collect data and it is important to know the difference between quantitative and qualitative data. 

Whether you are collecting data for univariate or multivariate analysis, you will use both dependent variables and independent variables. Dependent variables are the output of a data process and depend on the value of the independent variable, which is the input of a data set. The values of independent variables are unaffected by other variables. 

These are different from random variables, which depend on the outcome of a random situation, and the target variable, which is the value you deduce through research.

You can collect data via surveys, online forums, and face-to-face interviews. Once you’re sure you don’t have a biased sample, the data needs to be cleaned. Data cleaning is the process of removing corrupt or inaccurate data from the data set. The process includes removing unwanted data values, determining if any typos have been made, and removing outlier variables. 

To do this, you will also need a strong understanding of the different types of variables listed above.

Top Data Science Interview Questions and Answers

Your data science interview will likely contain a mix of general questions and technical questions. In this section, we focus on the technical types of questions, as these are a bit trickier to prepare for. The following questions are designed to test your expertise in data science. 

How Would You Handle a Situation with Missing Data?

There are a few different techniques you can use to handle missing data. One of the most common methods is simply deleting rows in your spreadsheet that are missing data. Other methods include assigning a unique value to the missing data, using means, medians, and modes for value imputation, and trying to predict the missing values. 

Another great way to handle missing data is to use an algorithm designed to support missing values, such as a random forest model. Random forest models calculate the predicted regression of a data set using a decision tree algorithm. By determining the regression, a random forest model can help determine the actual values of a data set. For a less complex model, you could choose a logistic regression model instead.

What Is the Central Limit Theorem, and Why Is It Important?

The Central Limit Theorem states that for any data set with unknown distribution, the sample mean will become closer to a normal distribution as the sample size grows larger. Essentially, the Central Limit Theorem suggests that the larger the sample size, the closer it will be to normal distribution. This is commonly used in machine learning.

The primary reason the Central Limit Theorem is important is that it is used for hypothesis testing. This theorem is also used for the calculation of confidence intervals and to reduce the significance of outlier values. The Central Limit Theorem is also considered a statistical technique, so it can be applied to a wide variety of data science jobs.

What Is a Confusion Matrix?

A confusion matrix is a table used to help describe the performance of a classification model used to test a specific set of data. For a confusion matrix to work, you will need to know all of the true values of a data set prior to using the matrix. When using a confusion matrix, you will work with true positives, true negatives, false positives, and false negatives. 

The great thing about a confusion matrix is its ability to help distinguish extreme values. It uses an F-score to accurately sort them. You will likely use a confusion matrix in machine learning, as it is considered to be a deep learning model. Using a confusion matrix is a fantastic way to understand more about your data set than the human brain often can without the matrix.

What Are Some Common Machine Learning Models and Algorithms?

There are quite a few types of models and algorithms used in machine learning. Artificial neural networks, often referred to as simply neural networks, are one of the most common. An artificial neural network is a type of computing system modeled by connecting nodes similar to those in a human brain.

Logistic regression models are similar to artificial neural networks. The primary difference between logistic regression models and neural networks is that logistic regression models are simpler and consist of only one layer. In either case, model performance should be tested with cross-validation, where data outside the sample is recruited to see how well the model holds up. 

Algorithms include the decision tree algorithm, the clustering algorithm, and the binary classification algorithm. Decision tree algorithms split nodes into multiple nodes like trees, while clustering algorithms create groupings of data points. Two common clustering techniques are DBSCAN clustering and k-means clustering, each of which has its own algorithm. 

Finally, binary classification algorithms separate data into two groups depending on a classification definition. It is important to determine the performance of your chosen models and algorithms in order to weed out weak models for your specific data sets.

What Are Recommender Systems?

A recommender system is an algorithm designed to help predict the rating a real person would give to an object. These systems help determine if an object, item, or service will receive positive feedback. Recommender systems are most commonly used in machine learning and deep learning work environments.

Recommender systems are typically based on a statistical model. These models are created from data sets with many missing values. Many recommender systems suffer from sampling bias, which occurs when some items are prioritized over others. When sampling bias occurs, the outcome will be biased as well.

Describe a Few Different Types of Functions in Machine Learning

The three most common functions you will use in data science and machine learning are loss functions, activation functions, and cost functions. A loss function is used to measure a true value and how far off the estimated value is from it. Loss functions are used in non-linear algorithms. They are also key in measuring the learning rate of a machine learning model. 

An activation function is used in an artificial neural network. These are used to help determine whether a neuron should fire to another neuron, allowing the neural network to learn complex patterns.

A cost function can be found in a linear regression model. A cost function is used to determine the effectiveness of models. In a less effective linear model, cost functions help identify errors between predicted outcomes and actual outcomes.

How to Succeed in a Data Science Interview

The best way to succeed in your data science interview is to arrive well-prepared. Ask a friend or family member to conduct mock interviews with you, study up on your technical skills, and ensure you know the job requirements.Becoming a data scientist is a fantastic idea in 2021. According to PayScale, the average data scientist earns about $96,491 per year. You don’t need to have a post-graduate program in data science on your resume to qualify for most jobs. If you prepare well for your data science job interview, you will likely become a well-paid data scientist in no time.