Top 10 data science interview questions and answers

1. Top 10 data science interview questions and answers The Data Science field has emerged in prominence with the mass adoption across industries and their swelling effort to leverage the power of Data. A master of science in data science is one of the most relevant credentials that employers are hunting for. Today let us briefly explore the top data science interview questions and answers that may help you in your preparation for your next professional step with an MS in Data Science. Some institutions offering a master of science in data science may require you to sit through an interview to check on your potential. Below are the most relevant questions along with brief and concise answers curated for you. Let's get started. 1. What is Data Science? Data Science is an interdisciplinary field encompassing varied subjects and scientific processes including machine learning, statistics, computer science, mathematics, etc. The field of data science is concerned with gathering data from various sources, processing and computing the data and generating valuable

2. insights from it. It involves an established cycle from data collection up to information production. 2. How is data science different from data analytics? Data analytics employ the insights generated by data science. While data science involves data transformation from raw nature to valuable insights employing various technical methods, data analytics is concerned with checking existing information and hypotheses and offers solutions for better and more effective decision-making processes. 3. advantageous? Why is Sampling used and considered Sampling becomes crucial as data analysis cannot be carried out in a whole volume or large data sets. Taking a sample to represent the whole and to analyze is made possible by sampling. This process requires careful selection of data that will represent the complete or the entire dataset. 4. What are the types of sampling techniques? Sampling techniques can be majorly classified into two categories: i)Probability sampling sampling, Stratified sampling, and Random Sampling techniques which are further classified into clustered ii) Non-probability Sampling Techniques, further classified into Quota sampling, Snowball sampling, Convenience sampling, etc. 5. and Underfitting? What are the conditions involved in Overfitting Overfitting: In overfitting, the models only perform for the sample training data. New data additions as input fails to generate any result. This is caused by the

3. low bias and high variance in the model. An instance of overfitting is Decision trees. Underfitting: In underfitting the presence of low variance and high bias results in the model being unable to identify the accurate relationship in the data, hence it fails to perform well even on test data. An instance of Underfitting is Linear regression. 6. What is the difference between Long-format data and Wide format data? In the long format data, the subject's one-time information is represented by each row of the data. Hence every subject will have its data in multiple rows and the subject's responses will be part of separate columns. The rows here are considered as groups and hence can be identified accordingly. In the Wide format data, in contrast, the subject's repeated responses are part of separate columns. Here columns are considered as groups. 7. Can you explain what is Imbalanced Data? Imbalanced data means unequal distribution of data across various categories. Such datasets are prone to errors in model performance and give inaccurate results. 8. Explain Confusion Matrix. A confusion matrix usually has 2 columns and 2 rows with 4 outputs that a binary classifier is known to provide to it. Confusion matrix is used for deriving various measures such as specificity, accuracy, precision, error rate, sensitivity, and recall. 9. Define Logistic Regression? Also called the logit model, Logic regression is a technique employed for predicting the binary outcome from the predictor variables. Prediction variables are essentially linear combinations of variables.

4. 10. Define Supervised Learning It is a Machine learning approach wherein an algorithm is trained on labelled data and learns to make predictions, forecast news or detect unseen data. The algorithm learns patterns and relationships from the input data and output labels. The goal of supervised learning is to generalize the learned patterns and generate accurate outputs for new input data based on the trained patterns. Those are the most relevant interview questions that are often asked during interviews for admissions to a master of science in data science or job positions after your MS in Data science. This set of questions will give you a broad idea about how you may prepare for your interview.