Are you aspiring to become a data scientist, but struggling to crack the interviews? Getting a break in the data science field can be difficult. Doubly so, if you're coming from a non-data science background (which in all likelihood you are). The stories you hear from other aspiring data scientists can make interviews feel more intimidating and daunting. So you better be prepared before facing the interviews. What kind of questions can be asked? How can you prepare and what are the resources you should refer to? What is the structure of a typical data science interview? How should your body language be? These are just some of the questions you'll have in mind.
Data science is an attractive field. It's lucrative, you get opportunities to work on interesting projects, and you're always learning new things. Hence, breaking into the world of data science is extremely competitive. One of the best ways to start your data science career is through a data science internship. In this article, we'll look at the general level of knowledge that's required, the components of a typical interview process, and some example interview questions.
In recent months, Bangladesh has seen a surge in attacks claimed by the so-called Islamic State of Iraq and the Levant (ISIL, also known as ISIS). As authorities try to tackle the growing domestic threat, does the country have an ISIL problem? And, after several bloggers inside the country were hacked to death for expressing secular views, what is the future of democracy and human rights in the country? In a special interview, Mehdi Hasan speaks to Bangladesh's State Minister for Foreign Affairs Shahriar Alam, who defends the country's young democracy. Editor's note: This interview was conducted prior to the latest developments in Bangladesh.
Machine learning (ML) is a rising field. It offers many interesting and well-paid jobs and opportunities. Each of these and some other items might be touched in an ML interview. There is a large number of possible questions and topics. This article presents 12 general questions (with the brief answers) appropriate mainly for beginners and intermediates.
This paper covers two topics: first an introduction to Algorithmic Complexity Theory: how it defines probability, some of its characteristic properties and past successful applications. Second, we apply it to problems in A.I. - where it promises to give near optimum search procedures for two very broad classes of problems.