Top interview Questions That Data Science Graduates Should Know!

Introduction

Data Science is a multidisciplinary subject where raw data is mined to draw insights and make strategic decisions. Currently, there are ample job opportunities for students pursuing an online MBA in Data Science. Such skilled individuals are becoming rock stars of global organizations. As per IBM, demand for Data Scientists will rise by 28 percent by 2020 and more so in the coming future. You can leap on this successful train by enrolling in a data science MBA online. It will help you get a deep understanding of the subject and aid you in getting a job in a global organization.

Suppose you are an up-and-coming Data Science professional pursuing an MBA in Data Science online. In that case, you should be well-versed with the basic and advanced Data Science concepts to impress prospective employers. Employers are looking for data science professionals who are smart, confident, technically sound, and a great fit for them. And to help you with that, we have combined top interview questions that Data Science graduates should know before appearing for a job interview.

What does the term Data Science mean?

Data Science is a field that uses scientific and statistical methods, tools, systems, and algorithms to extract insights from structured and unstructured raw data. It is used for strategic decision-making with the help of available data and facts.

What are the qualities of a good Data Scientist in an organization?

Data Scientists should be innovative, enjoy drawing insights from raw data, be well-versed with tools and techniques, be solution-oriented, inquisitive, have an analytical mind, enjoy automating routine tasks, be creative, solve complex issues, and be good team workers.

Explain some of the sampling techniques.

Data Scientists use data sampling techniques to analyze large data sets. In the case of large datasets, it becomes important to gather data samples that represent the whole population. Data sampling can be categorized into the following broad categories:

• Probability sampling technique: Stratified sampling, cluster sampling, Simple random sampling
• Non-probability sampling technique: Quota sampling, Convenience sampling, Snowball sampling, Self-selection sampling

When is resampling performed?

Resampling means when a data sample is collected again to perform analysis. Resampling is performed in the following cases:

• When you collect the original sample inaccurately, it is not representative of the whole population
• To improve the accuracy of the data samples
• To ensure that model is apt by performing data testing on different datasets and to ensure variations are taken care of

Define feature vectors

Feature vector contains multiple elements of an object. To put it in simple language, it is a numerical list that forms and represents a picture.

What are the drawbacks of the linear model?

Some drawbacks of the linear model are-

• It cannot solve some overfitting problems
• Binary outcomes cannot be counted using the linear model
• It assumes linearity of errors

Define selection bias

Selection bias means bias due to the selection of data, samples, people, and groups so that it does not represent the qualities of the whole population. As a result, the randomness of data is compromised.

What are the different kinds of sampling biases?

A few different types of biases in sampling are-

• Observer bias – It occurs when the observer projects their expectation and understanding on the sample, and it is usually done subconsciously.
• Survivorship bias- This occurs when we only focus on the sample that has passed the selection process and ignore the population that did not. It results in exceedingly positive results that might not be the actual case.
• Recall bias- Recall bias occurs when a respondent does not recall things correctly. It usually occurs when data is collected through surveys or interviews.
• Under coverage- It happens when the sample does not represent the complete population and underrepresents some population groups.

What is the difference between Data Science and Data Analytics?

The two subjects- Data Science and data analytics are interrelated.

• Data Scientists usually aid data analysts in performing data analysis by providing insightful and transformed data.
• Data Science promotes innovation by providing solutions for future problems. Data analytics mainly focuses on present problems and provides a solution by analyzing past data trends.
• Data Scientists use more advanced tools for solving complex problems. On the other hand, data analysts focus more on data visualization and statistical tools.

What do you mean when p values are lower and when p values are higher?

A p-value is a probability of results being equal to or higher than the results of a specific hypothesis, and it assumes that the null hypothesis is correct.

• A low p-value means probability ≤ 0.05; therefore, the null hypothesis is incorrect.
• A high p-value means probability ≥ 0.05 and therefore null hypothesis is correct.
• P-value = 0.05 means there is an equal likelihood of the null hypothesis being true or false.

What is a decision tree?

A decision tree is a popular statistical model. It looks like a flowchart where each node represents a test or an outcome of an attribute. It is a tree-like structure that outlines all the possible decisions and prospective outcomes. It is used extensively in many real-life projects across engineering, law, civil planning, business, etc.

Define six sigma.

Six Sigma is a process where 99.99966 percent of results are free of defects. It is a tool for organizations to improve their existing business process. It helps boost employee morale, reduce defects, and improve the quality of goods and services.

In addition to these technical questions, you should also be ready for a few personality tests and HR questions. You should understand the mission and vision of the organizations and should be able to justify why you would be a good fit for them. You should also prepare a few common questions like future goals, strengths, weaknesses, hobbies, and work ethics. You should also research the organization you are interviewing for and understand what they are looking for. Getting an online MBA in Data Science will equip you to answer all the above questions and many more during an interview.