Crack Your Data Science Interview with These Top 15 Questions for 2023

Data Science Questions for Experienced Professionals

If you are already working in the data science field and preparing for an interview to get promoted to higher positions, here are some important questions and answers you must learn:

1. What is RMSE in a Linear Regression Model?

RMSE (Root Mean Square Error) in a linear regression model is used to test the performance of an ML model. It evaluates the data spread around the line of best fit to measure the deviation from the actual value. It is calculated by taking the square root of MSE (Mean Square Error). A model with zero RMSE value indicates the perfect fit.

2. What are the differences between Overfitting and Underfitting?

Overfitting: Overfitting or force-fitting in a model occurs when the model cannot analyze new data but gives accurate predictions for the training data. In other words, these models correspond to a particular data set. Overfitting is most likely to occur in decision trees.

Underfitting: Underfitting in a model occurs when the model performs poorly, even on the training data. These models are unable to find the relationship between the input variables. This generally happens due to low variance and high bias. Underfitting is more likely to occur in linear regression models.

3. Explain Neural Network Fundamentals.

Neural networks are computing systems that combine various nodes that work like human brain neurons. These neural networks identify the trends and patterns in data to use this knowledge for future data predictions.

One of the simplest neural networks, Perceptron, contains a single neuron performing two functions – estimating the weighted sum of two or more input variables and generating one output. The output can activate or deactivate a device, for example, turn a television on or off.

Some neural networks are more complicated and consist of three layers:

• Input Layer: The input layer in a neural network receives the input.
• Hidden Layer: Neural network consists of multiple hidden layers between the input and output layers. The initial layers are low-level patterns, and other layers combine the outputs from previous layers to identify new patterns.
• Output Layer: This layer shows the prediction as output.

4. What is Your Approach to Solving a Data Analytics Project?

Here are the steps we use to solve a data analysis project:

• Firstly, you need to understand the requirements of the business you are working for.
• The second step is to explore the data carefully. If you find any data points missing, get your requirements fulfilled by the organization.
• The next step is to perform data cleaning and preparation. Here, you must find out missing values and transform the data to prepare it for modeling.
• Use your model against the data, build visualization, and analyze it carefully to drive meaningful insights.
• Track the results and performance of the model over time.
• Make sure to perform cross-validation of the model.

5. How Data Cleaning Plays an Important Role in Data Science?

Data cleaning forms the bulk of the data science lifecycle. It identifies errors, duplicates, and irrelevant data from a raw data set and fixes them. Data cleaning is the process of cleaning data from multiple sources to transform it into a format workable for data scientists.

As the quantity of data increases, it becomes more time-consuming to clean this data. Data Cleaning might take up 80% of the total time to analyze a data set. This is why it is a critical part of data science.

6. How do you treat missing values during data analysis?

You first need to identify the variables with missing values. If you can figure out a pattern, you can move further to drive meaningful information out of it. On the other hand, if no patterns are identified, you can either replace the missing values with mean or ignore them. If more than 80% of values are missing, you can omit the variable instead of substituting the missing values.

7. How do you balance imbalanced data?

There are several techniques to correct an imbalanced data set. It can be done by resampling, using the right evaluation metrics, or other methods. The following are some best approaches to balancing data:

• Use the Correct Evaluation Metrics: Using the right evaluation metrics that give valuable information is very important to turn imbalanced data into balanced ones. You can apply evaluation metrics, such as specificity, sensitivity, F1 score, MCC, and AUC.
• Training Set Resampling: You can also balance data by working with different data sets by resampling the training data. You can use under-sampling when the data quantity is sufficient and over-sampling when the data quantity is not sufficient.
• Perform K-Fold Cross-Validation: You should apply cross-validation before over-sampling. If you use K-fold cross-validation after over-sampling, the overfitted model derives a result.

8. Explain the differences between univariate, bivariate, and multivariate analysis.

Statistical analysis is classified into univariate, bivariate, and multivariate analysis based on the number of variables to be processed. Here is how the three are different from each other:

• Univariate Analysis: In this, you deal with only one variable at a time, for example, a sales pie chart based on a particular region.
• Bivariate Analysis: In this, you study two variables simultaneously, for example, a graph of sales and spending volume by a business.
• Multivariate Analysis: In this, you deal with more than two variables at a time. For example, signs recorded by a newborn baby, such as blood pressure, temperature, respiratory rate, heart rate, etc.

How To Prepare Yourself for a Data Science Interview

Before you start applying for data science jobs and giving interviews, you must have a strong foundation in the subject. A good data science course will prepare you for the most entry-level questions and develop practical skills. Application-based questions are common for every data science interview.

There are lots of data science course options available online and offline today. You must check the curriculum, duration, learning style, fees, and resources before enrolling in the course.

• IBM Professional Certificate in Data Science: It Covers Business Analytics in R, Machine Learning in Python, and Data Analysis in SQL. You get a certification from IBM.
• Certified Data Science Specialist: This course also covers Business Analytics, data analysis, and machine learning.
• PG Program in Data Science: After completion, you get certified by UPES. The course is ranked among the top 5 courses in India.
• Executive Program in Data Science for Managers: For this course, you should have at least three years of experience in data science after graduation. It is an advanced-level course designed for experienced professionals.

FAQs

To clear a data science interview, you don’t only need theoretical knowledge, but you should also be aware of how to apply this knowledge in real life. So, you can work on real-world data science projects before you go for an interview.

The difficulty level of data science interview questions depends on the complexity and experience required for the job you have applied for. Data science interview questions for beginners usually bank on the basic understanding of data science concepts. So, if you have completed your data science certification you should not have a problem clearing the interview.

Yes, you must have a decent knowledge of programming languages, such as Python, R, SQL, etc., to crack a data science interview.

The field of data science is open to everyone with an interest in learning mathematics, statistics, programming, etc. Even non-IT students can become data scientists by developing the required skills.