Data Science and Machine Learning Interview Questions Using Python: A Complete Question Bank to Crack Your Interview

Cover Page
Title Page
Copyright Page
Dedication
About the Author
Preface
Foreword
Table of Contents
1. Data Science Basic Questions and Terms
    Q1: Explain the steps involved in data science?
    Q2: Explain variable and different types of variables?
    Q3: Explain Categorical measurement?
    Q4: Explain Binary variables?
    Q5: Explain Nominal measurement?
    Q6: Explain Ordinal variable?
    Q7: Explain Continuous variables?
    Q8: Explain Discrete variables?
    Q9: Is it possible to convert continuous values to discrete and vice versa?
    Q10: What are interval variables?
    Q11: What are ratio variables?
    Q12: What are Univariate and Bivariate variables?
    Q13: What is measurement error?
    Q14: Explain Validity?
    Q15: Explain Reliability?
    Q16: What are the different ways to test hypotheses?
    Q17: Explain the different types of variation?
    Q18: Explain repeated-measures design?
    Q19: What is independent design?
    Q20: Explain the role of randomization w.r.t variation?
    Q21: Explain various summary measures.
    Q22: Explain alternate hypotheses and null hypotheses.
    Q23: What is p value?
    Q24: What happens when null hypotheses is rejected?
    Q25: Explain directional and non-directional hypotheses.
    Q26: Explain fit of model?
    Q27: What is relation between sample and population?
    Q28: What is estimation?
    Q29: Explain deviation score?
    Q30: Explain variance?
    Q31: Explain Standard deviation.
    Q32: Explain standard error.
    Q33: What is precision?
    Q34: Explain confidence intervals.
    Q35: Explain confidence level.
    Q36: Explain alpha.
    Q37: Explain Beta.
    Q38: Explain Accuracy.
    Q39: Explain Bias.
    Q40: What is central limit theorem?
    Q41: Explain Absolute value?
    Q42: What is degree of freedom?
    Q43: Explain cluster sampling.
    Q44: Explain Correlation coefficients?
    Q45: Explain sample space.
    Q46: What is non parametric algorithm?
    Q47: How can learning be classified?
    Q48: What is classification?
    Q49: Explain the steps involved in classification.
    Q50: What is regression?
    Q51: Explain the similarities and differences between Classification and Regression.
    Q52: Explain various terms encountered during classification algorithm.
    Q53: Explain multi class classification?
    Q54: Explain multi label classification?
    Q55: Explain how multi label problem can be solved?
    Q56: Explain some important metrics with respect to testing a model?
    Q57: What is logistic regression?
    Q58: Explain Naïve Bayes.
    Q59: What is Stochastic Gradient Descent?
    Q60: Explain decision tree algorithm.
    Q61: What is Gini index?
    Q62: Is Gini index the only means which can be used in decision tree?
    Q63: What is Pruning w.r.t. decision tree?
    Q64: What is random forest?
    Q65: Explain the difference between Random forest and decision tree.
    Q66: What is overfitting and underfitting?
    Q67: What are the reasons for under fitting occurrences?
    Q68: Does over fitting get affected by noise?
    Q69: Explain KNN (K Nearest Neighbour) steps involved, advantage and disadvantage.
    Q70: Explain selection bias.
    Q71: What does selection bias indicate w.r.t. algorithm?
    Q72: What is Bootstrap sample?
    Q73: What is Resampling?
    Q74: Explain tail.
    Q75: Explain the difference between one way test and two way test.
    Q76: Explain degree of freedom.
    Q77: What is predictive modeling?
    Q78: What is time series analysis?
    Q79: What is deep learning?
    Q80: What is Convolutional Neural Network?
    Q81: What are different ways to determine optimal value of clusters?.
    Q82: What are various distance related functions for similarity measures?
2. Python Programming Questions
    Q1: Is Python Object oriented?
    Q2: Is Python case sensitive?
    Q3: What kind of language is Python?
    Q4: What are different versions of Python?
    Q5: Explain different implementations of Python?
    Q6: Is Python loosely typed?
    Q7: How to start a new block in Python?
    Q8: How to get data type of a particular variable?
    Q9: How many ways can Python program be run?
    Q10: Explain the importance of Pylint and Pychecker.
    Q11: Explain Zen of Python.
    Q12: How to print Zen in Python?
    Q13: Explain Python data types.
    Q14: How can we switch variables in Python?
    Q15: What is the use of pass statement in Python?
    Q16: Is Python pass by value or pass by reference?
    Q17: Does Python supports chained operations?
    Q18: Explain ALL and ANY.
    Q19: Explain the difference between IS and ==.
    Q20: Explain supported collection of data type w.r.t. Python?
    Q21: Create a simple number list?
    Q22: Can you create nested list?
    Q23: Explain CRUD (Create, Update, and Delete) operations from list.
    Q24: Explain operations in dictionary.
    Q25: Explain operation with tuples.
    Q26: Explain del?
    Q27: If del can remove variable can it remove tuple variable?
    Q28: Delete last element in a list.
    Q29: Predict the output of following code.
    Q30: What do you mean by list comprehension?
    Q31: Explain the preferred way for looping through list?
    Q32: Find the reverse of the dictionary?
    Q33: How to sort dictionary by value?
    Q34: What is the use of shuffle function?
    Q35: What is the preferred way to get a value based on key in Python?
    Q36: Explain alternate way of merging 2 or more dictionaries without using update method?
    Q37: What is the preferred way of fetching last element/second last and so on from a list?
    Q38: What is the preferred way for reversing a list?
    Q39: Explain various string utility functions in Python.
    Q40: How to check whether two strings are equal.
    Q41: Can string use single quote or double quote?
    Q42: Explain type conversions on collection types.
    Q43: Explain set theory operations supported by set data type.
    Q44: Explain frozenset?
    Q45: Explain functions in Python?
    Q46: What is a Boolean function?
    Q47: Can we specify data type for arguments as well as return types in Python?
    Q48: Explain variable arguments?
    Q49: Write a program to find occurrences or count of characters in given word.
    Q50: What is **kwargs?
    Q51: Write a simple Lambda expression?
    Q52: Lambda forms in Python contain statements? True or False?
    Q53: Explain filter function?
    Q54: Explain steps involved in reading and writing a file?
    Q55: Explain the term “withstatement”?
    Q56: Explain the preferred way of reading a big file?
    Q57: Explain modules in Python.
    Q58: Explain different ways of importing modules.
    Q59: Can we create our own module?
    Q60: Explain in brief about os module and its corresponding functions.
    Q61: Using os module print the directory structure.
    Q62: Explain dir function.
    Q63: Explain exception handling in Python.
    Q64: How to create user defined exception?
    Q65: What is the use of raise statement?
    Q66: How to create own class in Python? Explain constructors.
    Q67: Is it necessary to have the first argument of class function as self? Can’t we rename it to any other variable?
    Q68: Explain inheritance in Python.
    Q69: How to determine whether a particular class is sub class?
    Q70: Does Python support multiple inheritance?
    Q71: How is diamond problem resolved in case of Python?
    Q72: Does Python support private method and variables?
    Q73: Can __ be used for other purpose than creating private variables or functions?
    Q74: Does Python support abstract classes?
    Q75: Differentiate between static methods and class methods in Python.
    Q76: What are named tuple?
    Q77: How to sort using lamdas?
    Q78: Explain Generators?
    Q79: What is generator expression?
    Q80: When Python program exits, all the memory is released? Say true or false?
    Q81: Can a function be passed as parameter to another function?
    Q82: Can a function be retuned as result from another function?
    Q83: Explain decorator function.
    Q84: How can we represent big text in Python?
    Q85: What is PEP 8?
    Q86: What is anaconda?
    Q87: How to install external modules?
    Q88: What is Jupyter notebook?
    Q89: What is pickling and unpickling?
    Q90: Explain the importance of setup.py?
    Q91: Is it possible to make connections to database using Python?
    Q92: Explain meta programming?
    Q93: Explain Python memory model.
3. Numpy Interview Questions
    Q1: What is numpy?
    Q2: How to install numpy?
    Q3: How to create single dimension numpy array?
    Q4: Explain different attributes provided by numpy?
    Q5: Explain some utility methods provided by numpy for creating different elements?
    Q6: How can we change shape of an object?
    Q7: Which all data types are supported in Python?
    Q8: Explain various simple mathematical operations which can be done on numpy?
    Q9: Explain slicing operation in numpy?
    Q10: Explain Boolean indexing?
    Q11: Perform matrix multiplication using numpy?
    Q12: Explain various functions available with numpy?
    Q13: What is broadcast?
    Q14: Explain rules of broadcasting.
    Q15: Explain some statistical measures supported by numpy.
    Q16: Explain functions available in numpy.linalg.
    Q17: How to save numpy data from memory to flat file?
    Q18: What is the use of where and extract?
    Q19: What is the use of ndenumerate?
    Q20: Explain how can we draw a histogram using numpy?
4. Pandas Interview Questions
    Q1: What is Pandas?
    Q2: How does Pandas represent data?
    Q3: How to create Series?
    Q4: How to create Data frame?
    Q5: How are missing values represented in data frame?
    Q6: Explain the process of creating indexes w.r.t. pandas?
    Q7: Explain various attributes associated with series.
    Q8: Explain various statistical measures supported by pandas.
    Q9: Explain reindexing.
    Q10: Explain bfill and ffill.
    Q11: What all type of iterations are provided in pandas data frame?
    Q12: Explain how sorting is supported in pandas?
    Q13: How to override default reload option in pandas?
    Q14: Explain various slicing options available with pandas?
    Q15: Explain advanced statistics with pandas.
    Q16: Explain rolling function.
    Q17: How can we handle NA in pandas?
    Q18: Explain group by function.
    Q19: Explain merge functions w.r.t data frame.
    Q20: Explain concat method.
    Q21: Explain how time related range can be generated in pandas.
    Q22: Explain which all data sources can pandas retrieve values.
    Q23: Can you compare some of the functions of R and Python?
    Q24: How to print a histogram using pandas?
5. Scipy and its Applications
    Q1: Explain Scipy library.
    Q2: Explain how can we perform Normality Tests.
    Q3: Explain how can we perform correlation test?
    Q4: Explain tests pertaining to Parametric Statistical Hypothesis Tests.
    Q5: Explain how to test Nonparametric Statistical Hypothesis Tests.
    Q6: Implement logistic regression in Python?
    Q7: Explain how to implement decision tree in Python.
    Q8: How to implement Random forest in Python?
    Q9: How to implement support vector machine in Python?
    Q10: Which all kernels are supported by svm in Python?
    Q11: Implement KNN algorithm using Python.
    Q12: How to select k in KNN algorithm?
    Q13: How to implement K means in Python?
    Q14: How can accuracy of any model be calculated?
    Q15: Explain regression metrics.
    Q16: Explain how we can print a decision tree or see the rules of the decision tree?
    Q17: What is the use of boosting techniques?
    Q18: Explain some of the advantages and disadvantages of boosting techniques?
    Q19: What is AdaBoost?
    Q20: Explain Gradient boosting?
    Q21: Explain XGBoost?
    Q22: Explain the differences/similarities between bagging and Boosting?
    Q23: Write a small snippet to perform operation with neural networks using tensorflow and keras?
6. Matplotlib Samples to Remember
    Q1: Explain how to draw bar plot.
    Q2: How to draw histogram?
    Q3: How to draw line chart?
    Q4: Draw Pie chart.
    Q5: How to get the equation of the line printed line plot?
    Q6: Draw scatter plot.
7. Statistics with Excel Sheet
    Q1: Does Excel has any support for statistics?
    Q2: Find correlation using Excel.
    Q3: How to get Histogram in excel?
    Q4: Explain how to get Descriptive Statistics using Excel.
    Q5: Explain how to perform Anova in excel?
    Q6: Explain how to perform Rank and Percentile in excel.