50 Data Science Interview Questions and Answers for 2017

Data Science and Analytics jobs are in the great demand across the globe. The Harvard Business Review has named the DSA jobs as “the sexiest job of the 21st century”. According to IBM, the roles of Data Scientists and Analysts are projected to experience a demand spike by 28% by 2020. The following figure shows how important the DSA skills are today, and likely to be through 2020. After experiencing the vibrant demand for the Data Science and Analytics jobs, you obviously need more attention while preparing for the DSA interviews. To help you prepare for the next interview, our subject experts have come up with a guide of most tricky interview questions and answers that will certainly let you give a tough fight in the interview. 1. What is Data Science? Data Science is a mix of statistics, machine learning, and Artificial Intelligence that are used to extract knowledge or insights from the data available in different forms (structured or unstructured) for predicting the future trends. 2. How Data Science is different from Big Data & Data Analytics? This is the question where most of the newbies generally get confused. Data Science is a blend of techniques that should deal with both structured and unstructured data for preparation, cleansing, and analysis; whereas, Big Data refers to the massive volumes of data that cannot be processed using the traditional systems. Alternatively, Data Analytics is the method of applying algorithmic / mechanical processes for drawing future insights. 3. Python or R. Which one would you prefer for Data Science? Python and R, both are renowned programming languages for the data science and analytics. However, the best possible answer for this question would be Python. The reason is because, R is mostly found in the data science environment whereas Python is used across different platforms like data analytics, web development, etc. 4. Define the steps involved in an analytics project. An analytics project will include the following steps: Defining the problemrn Exploring the datarn Preparing the datarn Modellingrn Data validationrn Implementation and tracking 5. What is meant by Data Cleansing? Data Cleansing also known as Data Cleaning, is a technique used for identifying and removing the anomalies and inconsistencies from the data, to improve the quality of the data. Read More: https://kovidacademy.com/blog/top-data-science-interview-questions-2017/