It’s no doubt that Data Science is getting bigger and better each day and there are plenty of opportunities for people who are interested in pursuing a career as a Data Scientist. As the leading and the most popular technologies in the world, Data Scientists are high in demand. Major companies or organizations are hiring professionals in the Data Science field with the highest pay. However, getting a Data Scientist job isn’t just luck or a matter of chance rather it is a matter of preparation and smartness. Candidates must be prepared to impress prospective employers with the Data Science knowledge and their technical proficiency with Big Data Concepts, machine learning, applications, and frameworks.

Here’s a list of popular, top 10 Data Scientist questions that you professionals expect in an interview irrespective of any company and business field. If you are aspiring to be a Data Scientist, get familiar with the below Data Science questions and answers.

1. What is the difference between Supervised and Unsupervised Machine Learning?

The fundamental difference between Supervised and Unsupervised Machine Learning is that the Supervised Learning requires training labeled data as input whereas the Unsupervised Machine Learning doesn’t require it. To know more differences, check out our blog. https://www.sulekha.com/data-science-and-business-analytics-training/know-the-key-differences-between-supervised-unsupervised-learning-5845-blog

2. What does logistic regression mean?

Logistic regression is a classification algorithm that measures the relationship between the dependent variable and independent variables using the logistic function.

3. What is selection Bias?

Selection Bias occurs in Data Science when the sample obtained doesn’t represent the population that is intended to be analyzed.

4. How do you make a decision tree?

Following are the steps in making a decision tree:

• The entire data set is taken as input
• The entropy of the target variable as well as predictor attributes calculated
• Information gain of all attributes calculated
• The attribute with the highest information gain chosen as the root node
• The same procedure is repeated on every branch until the decision node is finalized

5. What is the Random Forest model and how does it work?

 Random Forest is a versatile machine learning technique that is capable of performing both classification and regression tasks. It treats missing values and also used for dimensionality reduction and outlier values. Random Forest is a type of ensemble learning method where weak models are combined to form a powerful model.

6. What are the various types of Selection Bias?

The different types of Selection Bias include

• Sampling Bias
• Time Interval
• Data &
• Attrition

7. Which would you pick for text analytics between Python & R and why?

Python is better than R for text analytics for the following reasons.

• Python has a quicker performance for all types of text analytics
• The Pandas library in Python provides easy-to-use data structures as well as high-performance data analysis tools
• R is best suited for machine learning than trivial text analysis

8. What is the role of data cleaning in data analysis?

Due to the increase in the number of data resources, data cleaning can be a distressing job. Also, the time required for cleaning the data increases at an exponential rate. This is mostly because data cleaning takes up to 80% of the total time that is required for performing a data analysis task. Besides, there are several other reasons for using data cleaning in data analysis, and the two most important reasons include:

• Data cleaning accentuates the accuracy of a machine learning model
• Also, cleaning data from different sources aids in converting the data into a format that is easy to work with

9. How do you distinguish between deep learning and machine learning?

Machine Learning: Machine learning provides computers the ability to learn things without programming them explicitly. Machine learning can be classified in the following three categories.

• Reinforcement Learning
• Supervised Machine Learning &
• Unsupervised Machine Learning

Deep Learning:

Deep Learning is a part of machine learning involved with algorithms instigated by the function and structure of the brain called artificial neural networks.

10. What are the skills that a Data Scientist requires to help in using Python for data analysis purposes?

The skills required and could help a Data Scientist in using Python for data analysis purposes are mentioned below:

• Expertise in Scikit-learn, Pandas Dataframes, and N-dimensional NumPy Arrays
• Knowledge of Python script and bottlenecks optimization
• To be able to understand built-in data types, including sets, dictionaries, tuples, and others
• Skills to apply matrix operations on NumPy arrays and element-wise vector
• Equipped with Anaconda distribution and the Conda package manager
• Ability to write efficient list comprehensions, small, clean functions, and elude traditional for loops

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