Data scientists are big data ninjas. They tackle colossal amounts of messy data, and utilize their imposing skills in statistics, mathematics and programming to collect, manage and analyze data. Next, they combine all their analytic abilities – including, industry expertise, encompassing knowledge and skepticism to unravel integral business solutions of meaningful challenges.
But how do you think they become such competent data wranglers? Years of experience or substantial pool of knowledge, or both? In this blog, we have penned down the most important interview data questions on data science – it will only aid you crack tough job interviews but also will test your knowledge about this promising field of study.
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Data is a fine blend of statistics, technical expertise and business acumen. Together they are used to analyze datasets and predict the future trend.
Python includes a very versatile library, known as Pandas, which helps analysts use advanced level of data analysis tools and data structures. R doesn’t have such a feature. Therefore, Python is the one that’s highly suitable for text analytics.
Today, a recommender system is extensively deployed across multiple fields – be it music recommendations, movie preferences, search queries, social tags, research and analysis – the recommender system works on a person’s past to build a model to predict future buying or movie-viewing or reading pattern in the individual.
HDFS – It is the abbreviated form of Hadoop Distributed File System. It’s the distributed database that functions over Hadoop. It stores and retrieves vast amounts of data in no time.
YARN – Stands for Yet Another Resource Negotiator. It aims to allocate resources dynamically and manage workloads.
Logistic regression is nothing but a statistical technique that analyzes a dataset and forecasts significant binary outcomes. The outcome has to be in either zero or one or a yes or no.
Following are the real-life scenarios where machine learning is used extensively:
Power analysis is best defined as the process of determining sample size required for determining an impact of a given size from a cause coupled with a certain level of assurance. It helps you understand the sample size estimate and in the process aids you in making good statistical judgments.
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The blog has been sourced from — intellipaat.com/interview-question/data-science-interview-questions
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