Q1:What is Data Science?

Data science is the study of data using statistical techniques and computer programming to predict what will happen,  recommend what is good, find association, understand pattern, etc. t is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, and computer engineering to analyze large amounts of data.

Data science is used to study data in four main ways:

  • Predictive Analysis: Predictive analysis is used to study historical data to make accurate forecasts about data patterns that may occur in the future.
  • Prescriptive Analysis: Prescriptive analysis is used to recommend the best course of action based on given data input. 
  • Descriptive Analysis: Descriptive analysis examines data to gain insights into what happened or what is happening  in the data environment.
  • Diagnostic Analysis: Diagnosis analysis used for detailed data examination to understand why something happened. 


Q2: What is the difference between Data Analytics and Data Science?

Q3: Explain Feature Engineering

Q4: Explain outliers

Q5: What do you understand by Linear Regression?

Q6: What is the of a Linear Regression?

Q7: Explain Intercept and slope

Q8: What is residual error?

Q9: Explain bias and variance

Q10: What is bias variance tradeoff?

Q11: What is goodness of fit?

Q12: How many significant variables you had in your model? 

Q13: What is R-squared?

 Q14: Explain skewness

Q15: Explain Standard deviation

Q16: What is p-value?

Q17: What is pearson correlation?

Q18: What is the difference between covariance and correlation?

Q19: Explain probability

Q20: What is joint probability?

Q21: What is conditional probability?

Q22: What is kurtosis?

Q23: How do we check the presence of outliers in the data?

Q24: Which python libraries have you used in your projects so far?

Q25: Explain EDA

Q26: What is Logistic Regression?

Q27: What is the difference between Linear and Logistic Regression?

Q28: What is confusion matrix?

Q29: What is True Positive, True Negative, False Positive, and False Negative?

Q30: Explain Null and Alternate hypothesis.

Q31: Explain type-I and type-II error?

Q32: What is precision?

Q33: What is recall?

Q34: Why do we use precision and recall?

Q35: Explain Decision Tree.

Q36: What is pruning in a decision tree algorithm?

Q37: What is entropy in a decision tree algorithm?

Q38: What is information gain in a decision tree algorithm?

Q39: Explain selection bias.

Q37: Explain Random sampling.

Q38: What is normal distribution?

Q39: What is F1 score?

Q40: What is supervised and unsupervised learning?

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