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Describe The Principal Characteristics Of Environment? The programme was organised by UNESCO and UNEP, and here it was noted that “the environment includes a complex of natural, built and social components in the life of humanity”. In 1977, the first international conference on Environmental Education was held in Tiflis (capital city of Georgia Republic). But, for practical reasons, environmental study deals only with the system of interaction between non-living and living components of the globe. Since everything is interrelated with every other thing, logic demands-that the entire universe is our environment. There are about 100 billion such galaxies floating in the vast universe, each containing about 100 billion stars!! In this universe, not a single event can be said to be isolated, and no object is independent of the rest. Today, environment means a single system of the interacting living and non-living natural components of the earth.īut the earth is not the only planet of the solar family, which again belongs to our home Galaxy - the Milky Way. With time, the concept has undergone modifications. Whatever we see around us is confined to the living component of the human eco-system. You'll have to figure out what the nature of the problem is yourself.Įxample question: "Tell me about a time when your model gave results that your customer disagreed with.If we do not want to go into the intricacies of the term, then we can define environment as everything that surrounds us. More importantly: unless your interviewer is really throwing you a bone, they probably won't ask these questions so explicitly.
#BASIC DATA SCIENCE INTERVIEW QUESTIONS HOW TO#
Given a description of _ model behaving unexpectedly, (assuming you can recognize that it is behaving strangely at all) how would you diagnose the root cause of the unusual performance? Do you know how to recognize data leakage? Overfit? Heteroskedasticity? Autocorrelation? Outliers? Missing data? Given that you've diagnosed _ in your model/data, how will you change your approach to address this issue? Given a description of _ business problem, can you identify when the stakeholder is asking the wrong questions (type III error) and tease out of them what they actually need done?
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How would you measure/trigger that _ production model is performing poorly and should be re-evaluated or replaced? How would you go about deploying _ model into production? How would you turn _ business problem into an experiment? The kinds of questions you need to be able to answer to get a (good) job are along the lines of:
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These are enough to get you past a trivial technical screen. What is the difference between regression and classification of ML techniques? What is the difference between Machine Learning and Deep Learning?Įxplain what regularisation is and why is it useful What are Recurrent Neural Networks(RNNs)? How will you define the number of clusters in a clustering algorithm?
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What do you understand by the term Normal Distribution? What is logistic regression? Or State an example when you have used logistic regression recently.
#BASIC DATA SCIENCE INTERVIEW QUESTIONS SERIES#
What cross-validation technique would you use on a time series data set? What is Entropy and Information gain in a Decision tree algorithm? What are the different kernel functions in SVM?Įxplain decision tree algorithm in detail. What is the difference between supervised and unsupervised Machine LearningĮxplain SVM machine learning algorithm in detail.