Job interviews are stressful and can make you freeze up or forget answers to otherwise simple interview questions. So what can you do to reduce that stress? For one thing, you can extensively prepare for the interview. Secondly, you can remove some of that fear of the unknown by familiarizing yourself with the kinds of data scientist interview questions you will most likely get. That’s why we’re here today.
This article collects two dozen of the most popular data science interview questions and answers you may get asked this year. We will cover some basic and advanced data science interview questions, briefly explore the difference between data analytics and data science, and share an online data science course you can take to gain the necessary skills to become a data scientist.
Let’s get the ball rolling by defining data science.
What is Data Science?
Data science, not to be confused with data analysis, is Data science is the process of studying raw data to extract valuable, meaningful insights for businesses. Data science is a multidisciplinary approach combining computer engineering, mathematics, statistics, and artificial intelligence principles and practices to analyze huge amounts of data. This analysis aids data scientists and other related professionals in asking and answering significant questions like what happened, why it happened, what can happen, and what we can do with the results.
Also Read: Big Data and Analytics: Unlocking the Future
What’s The Difference Between Data Analytics and Data Science?
Although the terms sometimes get used interchangeably, data analytics is actually a subset of data science. Data science is an overall term that encompasses all aspects of data processing, from data mining and collection to data modeling to creating insights. On the other hand, data analytics is mainly concerned with mathematics, statistics, and statistical analysis. Data analytics focuses solely on data analysis, while data science deals with the bigger picture around organizational data. In most organizations, data scientists and data analysts team up to meet expected business goals. For example, the company’s data analysts may spend most of their time conducting routine analyses and providing regular reports. Meanwhile, data scientists design how data is stored, analyzed, and manipulated. In summary, a data analyst makes sense of the existing data, and the data scientist develops new methods and tools for the analysts to use to process data.
So, while all data analytics are part of data science, not all parts of data science are analytics. Furthermore, data scientists need considerably more skills and training than data analysts.
Now that we understand the differences between data science and data analytics let’s get into the interview questions.
24 Basic and Advanced Data Science Interview Questions
We begin with a group of basic data science interview questions and answers, then move on to the tougher ones.
Basic/Beginner Data Scientist Interview Questions
Why does data science matter?
A: Data science is important because it combines the tools, methods, and technology needed to generate meaning from the vast data inundating modern organizations. This data can create low-risk, actionable strategies for keeping a business going.
What’s the difference between data science and business analytics?
A: Although these two fields overlap, business analysts bridge the gap between business and IT, defining business cases, collecting stakeholder information, or validating solutions. Data scientists use technology to work with business data, write programs, apply machine learning techniques for creating models, and develop new algorithms. Business analysts take the data output generated by data scientists and use it to tell a narrative that stakeholders can understand.
What’s the difference between the fields of data science and machine learning?
A: Machine learning is the study of training machines to analyze and learn from datasets as people do. Machine learning is just one of the methods data scientists use in their projects to gather automated insights from data.
Also Read: Five Outstanding Data Visualization Examples for Marketing
What’s the difference between supervised learning and unsupervised learning?
A: Supervised and unsupervised learning differ by the nature of the training data they’re given. Supervised learning needs known and labeled training data, whereas unsupervised learning works with unlabeled data and uses it to discover trends. Supervised learning uses a feedback mechanism, while unsupervised learning doesn’t. The most popular supervised learning algorithms include decision trees, logistic regression, and support vector machines, and the most popular unsupervised algorithms include k-means clustering, hierarchical clustering, and apriori algorithms.
What’s the data science lifecycle?
A: The data science lifecycle includes:
- Business understanding. Asking the right questions and defining objects needed to track the problem.
- Data mining. Gathering and scraping data the needed data.
- Data cleaning. Fixing any inconsistencies in the data and addressing any missing values.
- Data exploration. Developing hypotheses regarding the problem by visually analyzing the data.
- Feature engineering. Choosing the most essential features and creating additional useful ones using the raw data.
- Predictive modeling. Training machine learning models, evaluating their performances, and using them to make predictions.
- Data visualization. Using plots and interactive visualization to communicate findings with the appropriate stakeholders.
What are the four primary ways of using data science to study data?
A: Here are the four ways:
- Descriptive analysis. This method examines data to gather insights into what has happened or is happening within the data environment, characterized by data visualizations like bar charts, pie charts, tables, line graphs, or generated narratives.
- Diagnostic analysis. This method is a detailed or deep-dive data examination to figure out why something happened, characterized by techniques like data mining, drill-down, data discovery, and correlations.
- Predictive analysis. This method uses historical data to generate accurate forecasts about possible future data patterns, characterized by techniques like forecasting, machine learning, pattern matching, and predictive modeling.
- Prescriptive analysis. This method predicts what is likely to happen and suggests an optimum response to the outcome. Prescriptive analysis analyzes the possible implications of different choices and recommends the best action. This analysis uses complex event processing, graph analysis, neural networks, simulation, and recommendation engines from machine learning.
What are the top techniques used by data scientists?
A: The following techniques are used to teach machines to sort data based on a known dataset, give unknown data to a machine, let it sort the data independently, and take result inaccuracies into account and handle resulting probability factors. They include:
- Classification. Sorting data into specific categories.
- Regression. Finding a relationship between two apparently unrelated data points.
- Clustering. Grouping closely related data together and looking for patterns and anomalies.
Also Read: Data Science Bootcamps vs. Traditional Degrees: Which Learning Path to Choose?
Discuss some of the common technologies used by data scientists.
A: Here are four commonly used technologies.
- Artificial intelligence. This technology covers machine learning models and associated predictive and prescriptive analysis software.
- Cloud computing. Virtual data systems provide greater flexibility and processing power for advanced data analytics.
- The Internet of Things (IoT). IoT describes the network of devices that independently and automatically connect to the Internet and provide vast amounts of data for mining and extraction.
- Quantum computing. Quantum computers rapidly perform complex calculations. Experienced data scientists use them to create complex quantitative algorithms.
What are some of the biggest challenges facing data scientists?
A: Obstacles include multiple data sources, eliminating biases, and getting stakeholders to define the initial problem correctly.
What’s the difference between univariate, bivariate, and multivariate analysis?
A: Univariate data contains only one variable, describes the data, and finds its patterns. Bivariate data uses two different variables and deals with causes and relationships, performing the analysis to determine the relationship between both variables. Multivariate data does the same but involves three or more variables.
Explain the difference between long-format data and wide-format data.
A: Long format data contains values that repeat in the first column, and each row is a one-time point for each subject. With wide-format data, the data’s repeated responses are in a single row, and each response may be recorded in separate columns.
What’s a confusion matrix?
A: A confusion matrix is a table that estimates a model’s performance. It uses a 2×2 matrix to tabulate the actual and predicted values.
Also Read: Career Roundup: Data Scientist vs. Machine Learning Engineer
Advanced Data Scientist Interview Questions
Explain true and false positive rates.
A: True Positive Rate (TPR) represents the probability that an actual positive will be positive, calculated by using True Positives (TP) and False Negatives (FN) in this formula: TPR=TP/TP+FN. The False Positive Rate (FPR) measures the likelihood that an actual negative result will be revealed as positive. In other words, the chance a model will generate a false alarm. It uses the False Positives (FP) and True Positives (TP) values in this formula: FPR=FP/TN+FP.
Discuss the linear regression model.
A: Linear regression is an analysis technique where the score of the Y variable is predicted using the score of a predictor, the X variable. Y is known as the criterion variable.
Discuss the random forest model.
A: Random forests combine multiple models to get a final output. More precisely, it combines multiple decision trees to get a final output. Since forests consist of many trees, decision trees make up a random forest model.
What’s a p-value, and what is it used for?
A: The p-value measures the statistical importance of an observation. P-value is the probability that shows the output’s significance to the data. Data scientists compute the p-value to discover a model’s test statistics. The p-value commonly helps data scientists choose whether to accept or reject the null hypothesis.
Explain the difference between a correlation and a covariance matrix.
A: Both terms are used to establish a dependency and relationship between two random variables. Here’s where they differ:
- Correlation measures and estimates the quantitative relationship between two variables, measured by how strongly the variables are related.
- Covariance represents the extent the variables change together in a cycle. Covariance explains the systematic relationship between variables when changes in one variable affect changes in another.
Define cross-validation.
A: Cross-validation assesses the generalizability of statistical analysis results to other data sets and is often applied when forecasting is the primary objective and the data scientist wants to gauge how effective a model will function in real-world applications.
What is variance in the context of data science, and how does it occur?
A: Variance is an error when the model becomes too complex and consequently learns features from data and accompanying noise. This error typically occurs if the model-training algorithm is highly complex, although the data, underlying patterns, and trends are easy to spot.
List some feature selection methods used to choose the correct variables.
A: These are the most common feature selection methods in data analysis:
- Backward Elimination
- Chi-Square
- Pearson’s Correlation
- Recursive Feature Elimination
- Lasso Regression
- Ridge Regression
What is dimensionality reduction, and why is it useful?
A: Dimensionality reduction removes redundant features or variables being studied in machine learning environments. The benefits of dimensionality reduction include:
- It reduces machine learning projects’ storage requirements
- It makes it easier to interpret machine learning model results
- It’s easier to visualize results when dimensionality is reduced to a few parameters, thus making 2D and 3D visualizations possible
What’s an outlier, and how do you handle outlier values?
A: Outliers are data values that differ significantly from the dataset’s other values. An outlier is typically the result of an experimental error or a valid value demonstrating a high degree of variance from the mean. The best way to handle outliers is to set up a filter that automatically eliminates any outliers that don’t fit the criteria.
What’s a normal distribution?
A: Normal distribution is a probability distribution with values symmetric on either side of the data’s mean, implying that the values closer to the mean are more common than values further away from it.
How do you handle missing values when performing data analysis?
A: The impact of missing values can be found after the data scientist identifies which variables are missing. Here’s how to handle these missing values.
- If the data analyst finds any patterns in these missing values, this increases the chances of uncovering meaningful insights.
- If no patterns are found, these missing values can be ignored or replaced with default values like minimum, maximum, mean, or median values.
- If the missing values belong to the categorical variables, they are assigned default values. If the data has a normal distribution, assign mean values to the missing values.
- If over 80 percent of the values are missing, it depends on the analyst’s discretion to either replace them with default values or drop the variables outright.
Also Read: What is A/B Testing in Data Science?
How to Get Better Prepared for a Data Science Interview and Career
It’s great to have the answers to commonly asked interview data science interview questions. Still, nothing beats having that knowledge and associated skills locked up in your head in the final analysis. That’s why a data science bootcamp is an excellent choice for aspiring or current data scientists to upskill.
This six-month bootcamp will boost your data science expertise and teach you to make better data-driven decisions via a high-engagement learning experience that includes over two dozen hands-on projects and integrated labs.
According to the Indeed.com job site, data scientists in the United States can earn an annual average of $123,641, with a high limit of over $186,000. Today’s businesses and organizations are looking for data scientists to help them make sense of the tidal waves of new data that inundate our everyday lives. Could you be one of those data scientists that can help make sense of it all while enjoying a rewarding career? Check out the bootcamp today, and get involved in the fascinating world of data science.
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