Question 1. What do you know about cross validation model technique?

Answer: Cross validation is a model validation technique. It is a technique for evaluating how generalized the analysis of statistics is to an independent data set. This technique is mostly used to evaluate machine learning models.

Question 2. Data science is a stressful job, how do you deal with stress?

Answer: Learning from my previous work experiences, I am aware that you have to work in a very stressful environment and superiors always place high expectations on your performance. So, to avoid getting tired or stressed, I still take 5–10-minutes breaks after completing a task to stay productive throughout the day.
 

Question 3. How is machine learning different from data science?

Answer: Machine learning is the set of techniques used by data scientists that allow modern machines such as computers to learn from data, while data science aims to use a scientific approach to extract information. and develop ideas from them.
 

Question 4. How can you avoid overfitting your model?

Answer: When a model is established only for a small amount of data and ignores the big picture, it is called overfitting, and to avoid it, I will keep the model not very complex considering few variables, so that the complexity of the data is reduced. and the use of cross-validation techniques can also help to avoid overfitting.

Question 5. What is logistic regression?

Answer: It can be defined as a technique to predict the binary outcome from a linear combination of predictor variables. Also called the logit model. The result of the prediction is binary, that is, 0 or 1. An example of this concept could be the possibility that a leader wins the elections.

Question 6. What factors do you check to ensure data quality?

Answer: For checking the data quality, Every time I check its

• Precision
• Integrity
• Consistency
• Integrity
• Accordance
• Uniqueness

 

Question 7. What do you think, how much statistics important in data science?

Answer: Statistics plays a very important role in data science. It is essential to help data scientists get a better idea of customer and consumer expectations. A data scientist can gain insights into several important things like consumer interest and behavior, trends and engagement, retention, etc. In short, it helps build robust data models to validate predictions and inferences.
 

Question 8. What is the RDBMS? Do you have knowledge about it?

Answer: RDBMS stands for Relational Database Management System, which is based on the relational model to create a database for the purpose of storing data. Yes, I have used MySQL (or other that you have used) which is itself a relational database software to store data in the form of tables and databases using queries to add, update, delete and modify the data
 

Question 9. Why do you want to work at this company as a data scientist?

Answer: I've been in the field of technology since high school and have degrees in computer science (your degree) and I'm passionate about working as a data scientist as I love working with data and numbers as well as all the coding and programming. I have always wanted to work in a data-driven company like yours and that is why I am looking forward to working as a data scientist for your company.
 

Question 10. Do you have any previous work experience that is relevant to this position?

Answer: Yes, I worked as a data scientist trainee for a tech company where my role was to collect customer feedback and engage more customers from multiple platforms both online and offline. My primary role was to gather information about what most customers consider to be a problem with their company-provided device. I learned a lot of skills in that job and I'm sure those skills will carry over to this role as well.