3 What are the popular libraries used in Data Science?
The popular libraries used in Data Science areÂ
6 How regularly must an algorithm be updated?
You will want to update an algorithm when:
7 Difference between Point Estimates and Confidence Interval
Confidence Interval: A range of values likely containing the population parameter is given by the confidence interval. Further, it even tells us how likely that particular interval can contain the population parameter. The Confidence Coefficient (or Confidence level) is denoted by 1-alpha, which gives the probability or likeness. The level of significance is given by alpha.Â
Point Estimates: An estimate of the population parameter is given by a particular value called the point estimate. Some popular methods used to derive Population Parametersâ Point estimators are – Maximum Likelihood estimator and the Method of Moments.
To conclude, the bias and variance are inversely proportional to each other, i.e., an increase in bias results in a decrease in the variance, and an increase in variance results in a decrease in bias.
To crack a data science interview is no walk in the park. It requires in-depth knowledge and expertise in various topics. Furthermore, the projects that you have worked on can significantly boost your potential in a lot of interviews. In order to help you with your interviews, we have compiled a set of questions for you to relate to. Since data science is an extensive field, there are no limitations on the type of questions that can be inquired. With that being said, you can answer each of these questions depending on the projects you have worked on and the industries you have been in. Try to answer each one of these sample questions and then share your answer with us through the comments.
Pro Tip: No matter how basic a question may seem, always try to view it from a technical perspective and use each question to demonstrate your unique technical skills and abilities.
76. Which is your favorite machine learning algorithm and why?
77. Which according to you is the most important skill that makes a good data scientist?
78. Why do you think data science is so popular today?
79. Explain the most challenging data science project that you worked on.
80. How do you usually prefer working on a project – individually, small team, or large team?
81. Based on your experience in the industry, tell me about your top 5 predictions for the next 10 years.
82. What are some unique skills that you can bring to the team as a data scientist?
83. Were you always in the data science field? If not, what made you change your career path and how did you upgrade your skills?Â
84. If we give you a random data set, how will you figure out whether it suits the business needs or not?
85. Given a chance, if you could pick a career other than being a data scientist, what would you choose?
86. Given the constant change in the data science field, how quickly can you adapt to new technologies?
87. Have you ever been in a conflict with your colleagues regarding different strategies to go about a project? How were you able to resolve it?
88. Can you break down an algorithm you have used on a recent project?
89. What tools did you use in your last project and why?
90. Think of the last technical problem that you solved. If you had no limitations with the projectâs budget, what would be the first thing you would do to solve the same problem?
91. When you are assigned multiple projects at the same time, how best do you organize your time?Â
92. Tell me about a time when your project didnât go according to plan and what you learned from it.
93. Have you ever created an original algorithm? How did you go about doing that and for what purpose?
94. What is your most favored strategy to clean a big data set and why?
95. Do you contribute to any open source projects?