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

- Length: 152 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2020-05-08
- ISBN-10: 9389845785
- ISBN-13: 9789389845785
- Sales Rank: #1076116 (See Top 100 Books)

**Know Data science with numpy, pandas, scipy, sklearn**

**Key Features**

- Questions related to core/basic Python, Excel, basic and advanced statistics are included
- Book will prove to be a companion whenever you want to go for an interview
- Simple to use words have been used in the answers for the questions to help ease of remembering

**Description**

“Data science and Machine learning interview questions using Python,” a book which is a true companion of people aspiring for data science and machine learning, and it provides answers to most asked questions in an easy to remember and presentable form.Book mainly intended to be used as last-minute revision, before the interview, as all the important concepts and various terminologies have been given in a very simple and understandable format. Many examples have been provided so that the same can be used while giving answers in an interview.The book is divided into six chapters, which starts with the Data Science Basic Questions and Terms then covers the questions related to Python Programming, Numpy, Pandas, Scipy, and its Applications, then at the last covers Matplotlib and Statistics with Excel Sheet.

**What will you learn**

- You can learn the basic concept and terms related to Data Science, python programming
- You will get to learn how to program in python, basics of Numpy
- You will get familiarity with the questions asked in an interview related to Pandas and learn the concepts of Scipy, Matplotib, and Statistics with Excel Sheet

**Who this book is for**

The book is mainly intended to help people represent their answer in a sensible way to the interviewer. The answers have been carefully rendered in a way to make things quite simple and yet represent the seriousness and complexity of the matter. Since data science is incomplete without mathematics, we have also included a part of the book dedicated to statistics.

**About the Author**

Vishwanathan has twenty years of hard code experience in the software industry spanning across many multinational companies and domains. Playing with data to derive meaningful insights has been his domain, and that is what took him towards data science and machine learning.

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.

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