Data Science Interview Questions

1. Python: Python is a very interactive language and mostly preferred for the domain of Data Science because of its flexibility and the availability of packages and modules crucial for Data Science and Machine Learning. With Data Science inclining towards machine learning and then further into Deep Learning python offers the compatibility a lot of software scientists look for. With the popular open source machine learning platform TensorFlow based on Python, it makes the framework of Python more powerful for Data Science and Machine Learning. The sub-modules of Python like Scikit learn, Scipy, Numpy, pandas, matplotlib offer a lot for helping you out with analyzing, cleaning, visualizing, testing and deploying datasets on to your model or algorithm. Python also is a very fast language compared to other traditional language approach to Data Science and is one of the easiest and near-written English language. Being nearly complicated syntax free, it focuses more on the perspective use of the programmer and thus becoming the most favored language for Data Science and Machine Learning.
2. R: R is a very popular alternative to Python for the domain of data science. Though R is a tool more inclined towards data visualization rather than towards the aspect of deployment of datasets for machine learning models, R is still one of the most actively used powerful language and it offers powerful model interpretability and a reliable community support. But for a fact, R has a steeper learning curve because of its complex paradigm and programmers without prior coding experience may find it very difficult to join all the pieces in R. Since the IDE of R called RStudio offers four monitoring window panels with a lot of processes running on the background to give you a detailed picture of the architecture of what's running under the hood in compared to Python's most popular interactive IDE called Jupyter Notebook, R has been seen to be a syntax based and complex language than Python but it offers extensive tools in Data Visualization and is preferred over Python for the domain of Data Science for those who exclusively work in Analytics. 

Python is the answer as it has libraries like pandas which provide easy data structures and high-performance for data analysis. 

• If the pie chart of a particular sales which is based on a territory has only one variable involved then it is referred to as univariate analysis. 
  
• If the analysis is a difference between two variables at a particular time on a scatter plot then it is referred to as bivariate analysis. 
  
• If the analysis deals with more than two variables then the variable effect on the response is called as multivariate analysis. 

• Using the classification matrix in order to look at the true negatives and false positives. 
  
• Concordance that helps in identifying the ability of the model if the event is happening or not. 
  
• The lift which helps in accessing the model by comparing it with random selection. 
  

• One is to use the Add-ins which is provided on a number of websites that one can use.
• Two is to use the fundamentals of the logistic regression and use it in Excel's power to be able to build up a logistic regression.

• Time Interval - When the trial can be terminated at the earliest at an extreme value but at the extreme value, it is likely to reach the variable which has the largest variance, even if the variables have similar mean.
• Data - When the subsets of the data are chosen in order to support a particular conclusion or the rejection of a bad data instead of previously stated data on general criteria.
• Sampling bias - It is a systematic error that is due to a non-random sample of an entire population that causes few members to be included rather than resulting it to a biased sample.
• Attrition - It is a kind of selection bias which is caused by attrition that is the loss of participants, that discounts the trial subjects and the tests which did not make it to completion. 

Question 28 - What is the advantage of performing dimensionality reduction before fitting an SVM?
Answer -  Support Vector Machine (SVM) algorithm actually performs better in a reduced space. It is beneficial as it performs dimensionality reduction before you fit an SVM as the number of features are large when you compare it to the number of observations. 

Question 29 - Explain cross-validation.
Answer - Cross-validation is a model validation technique which helps in evaluating how the outcomes of a statistical analysis can generalize the independent data set. It is mostly used in the backgrounds where one can forecast the objective and estimate how the models accurately behaving to accomplish the results. The ultimate goal of the cross-validation is to terminate the data-set to test the model in the entire training phase. In order to limit the problems like overfitting and to gain insight into how the model is going to generalize to an independent data set.

Question 30 - What are the important skills to have in Python with regard to data analysis?
Answer - The important skills that one needs to possess while performing data analysis using Python are:

• One should know about the built-in data types which are lists, tuples, sets and dictionaries. 
  
• One needs to master N-Dimensional NumPy arrays.
  
• To be a master of pandas and data frames.
  
• The ability to perform the element-wise vector and the matrix operations on the Numpy arrays. It requires having a mindset who comes from a traditional software background which is used for loops. 
  
• You should know how to use Anaconda distribution and the conda package.
  
• Ability to be able to write list comprehension in an efficient manner. 
  
• To know the scikit-learn package. 
  
• Knowing how to perform the Python script and to optimize the bottlenecks.  
  

Question 31 -  What are the differences between overfitting and underfitting?
Answer - In statistical machine learning, the most common task is to either fir the model to a training data set in order to make predictions on a general untrained data. 

• Overfitting - The statistical model actually describes the random error. It occurs when the model is very complex that is having a lot of parameters which is relative to the number of observations. The model is overfitting means the predictive is a poor performance as it reacts with minor fluctuations in the training data.
• Underfitting - This occurs when a statistical machine learning model or the machine learning algorithm captures the underlying trend of the data. It occurs if the model is to fit either a linear model to a non-linear data. This model too would have a poor predictive performance. 

Question 32 - How does data cleaning plays a vital role in the analysis?
Answer - Data cleaning plays a vital role in the analysis because:

• Cleaning the data from multiple resources will help in transforming it into a format that the data scientists or the data analysts can work with.
• Data cleaning helps in increasing the accuracy of the model in the machine learning algorithm
• If the number of data sources increases then the time that is taken to clean the data also increases in an exponential way due to the increase in sources and a huge amount of data that is generated by these sources.
• It takes up to 80% of the time to just clean the data in order to make it efficient and plausible for analysis. 

Question 33 - What are essential skills and training needed in Data Science?
Answer - The essential skills and training that one needs in Data science is to learn to Communicate Storytelling, Learning Optimization in Statistical Machine Learning, Computing with Big Data cloud, Having the knowledge about the business domain, to be able to visualize with the help of tools and to have a programming idea about the fundamentals of computing. 

Question 34 - Explain Data Science Vs Machine Learning. 
Answer - Machine Learning and statistics are a part of data science. Machine learning explains that the algorithms depend on some data. Which is used further to train the set, or to fine-tune some of the models or the parameters of the algorithm. Data Science covers various topics which are listed below:

• Data integration.
  
• Distributed architecture.
  
• Automating machine learning.
  
• Data Visualization.
  
• Dashboards and BI.
  
• Data engineering.
  
• Deployment in production mode.
  
• Automated, data-driven decisions.

Question 35 - What does Machine Learning mean for the Future of Data Science?
Answer - Data Science includes Machine Learning. Machine learning is the ability of a machine to gather the knowledge from the data that is provided which it learns from. But there are few machines which can also learn without the data. The data science is pushing forward to increase the importance of machine learning. This is being used in various industries and soon will be used by all. Machine learning is efficient as it stores the data which has the ability to take the algorithms. It is a standard need for data scientists in all their fields that they are working on. 

Question 36 - What is meant by logistic regression?
Answer - Logistic regression is a method to fit the regression curve that is y = f(x) where the y is a categorical variable. It is a classification of the algorithm. It is just used for predicting the binary outcome which is either 1/0, Yes/No, True/False with the given set of independent variables. It helps in representing either the binary or the categorical outcome. It is a regression model in which the response of the variable is categorical such as True or False or 0/1. This essentially means that it measures that probability of the binary response.

To perform logistic regression, we use this command in R: glm( response ~ explanantory_variables , family=binomial)

Question 37 - What is meant by Poisson regression?
Answer - Poisson Regression is the data that is collected in counts. The variables are discreet and have possible outcomes. The binomial counts are the number of successors that are fixed in the number of trials. Poisson counts are actually the number of occurrences of the events which occurs in the certain interval of time. It has no upper bound and the binomial counts are only between two values which are 0 and n. 

To perform logistic regression in R, we use the command:  glm( response ~ explanantory_variables , family=poisson)

Question 38 - What are distance measures in R statistics?
Answer - Distance measures - Similarity, dissimilarity, and correlation. It is a mathematical approach which helps us in measuring the distance between two objects which is further used as a commuting distance in order to compare the objects. This can be concluded in three different points which are based on the comparison which are:

• Similarity - It is a measure that ranges from 0 to 1. 
  
• Dissimilarity - It is a measure that ranges from 0 to Infinity. 
  
• Correlation - It is a measure that ranges from +1 to -1. 

Question 39 - What is a correlation in R?
Answer - It is a type of technique which is used for investigating the relationship which is between two quantitative and continuous variables. 

• Position correlation - In this case, both the variables either increase or decrease together. 
  
• Negative correlation - In this case, only one variable increases and the other decreases. 
  

Question 40 - What is term Pearson’s Correlation Coefficient?
Answer - This is actually a statistical technique which provides with a number that tells us how a strong and weak relationship is between the objects. Basically, it is not a measure that describes the distance. The measure actually describes the bound between the two objects. It is represented by "r" and it ranges from -1 and +1. 

• If r is close to 0, that means there is no relationship between the two objects. 
  
• If r is positive, that means if one of the objects that gets larger than the other also gets larger. 
  
• If r is negative, that means that if one of them gets larger than the other gets smaller.