No wonder, technologies like big data, machine learning, and artificial intelligence are increasingly gaining popularity. Looking at various applications and capabilities of data, undoubtedly, organizations are searching for skilled professionals. Demand for data science professionals is soaring high, which is why we have prepared a list of questions that you must know for your next interview.
Let’s see what are these questions:
1. Explain Decision Tree
The decision tree is a popular method used for predictive modeling. Taking a real-life example – deciding to go out on the weekend can be formulated through a decisions tree. Whether an individual wants to go out on Saturday or just crash on the couch. This decision is impacted by factors such as if it is a sunny day or it is raining. If it is raining, your friends might not show up. If it is sunny, you may go out on a shopping spree. If you don’t have to go out at all, then the weather won’t impact your decision.
Similarly, the decision tree in machine learning helps in finding a path for highly complex situations.
2. Explain the Feature Vector
A feature vector can be defined as an n-dimensional vector which has numerical features. These features signify some object. Talking about machine learning specifically, feature vectors are utilized to denote symbolic or numeric characteristics.
3. How Would You Define Logistic Regression?
If you have a linear combination corresponding to predictor variables, logistic regression helps you forecast its relevant binary outcome.
4. What Is Cross-Validation?
Cross-validation is a validation technique or statistical model, which is utilized to compare various models and select the one appropriate for a predictive modeling problem. This is decided based on how easy the model is to understand, implement, and the level of bias it has.
5. Explain the Importance Of A/B Texting
A/B testing is a statistical model, in which two variables are used for randomized trials. The object is to the test and finds out the changes in order to maximize the results or outcome of the given strategy.
6. Explain the Linear Model and Its Drawbacks
Linear regression is a method utilized for finding out the relationship between one or multiple predictors and the target. Its drawbacks include assuming that the errors are linear, binary outcomes are not solved, and overfitting problems.
7. What Do You Mean by Law of Large Numbers?
Law of large numbers is a theorem which believes in performing one experiment multiple times to obtain a feasible outcome. The basis of this theorem is Frequency-style execution. The theorem says that after performing an experiment for a large number of times, its average should be near to the expected value. The outcome, in fact, draws closer if trails are increased.
8. What Star Schema?
Star schema is certainly the simplest schema architecture. It is called star as the structure appears like a star protruding from the center. Generally, it has denormalized dimension tables and 3NF fact tables. Even though it is the simplest, it is still widely used because of its efficiency.
9. How Regularly Would You Update an Algorithm?
You are required to update or change any algorithm when:
- You observe a changing data source
- At the time of non-stationarity
- The model is required to evolve to data streams with the help of infrastructure
10. Explain Eigenvector and Eigenvalue?
While eigenvectors are used for knowing linear transformation, eigenvalues correspond to the direction of a linear transformation. For covariance matric or correlation, eigenvectors are used.
Conclusion
It is not easy to become a data scientist. You need to constantly brush up your knowledge and get acquainted with new concepts of the stream to stay ahead of the industry. When you enter the interview, ensure that you have full knowledge of the above basic concepts as well as new trending concepts of data science.