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In this Data Science Interview Questions blog, I will introduce you to the most frequently asked questions on Data Science, Analytics and Machine Learning interviews. This blog is the perfect guide for you to learn all the concepts required to clear a Data Science interview.
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years?
The answer lies in the difference between explaining and predicting.
The following are some of the important skills to possess which will come handy when performing data analysis using Python.
Good understanding of the built-in data types especially lists, dictionaries, tuples, and sets.
Mastery of N-dimensional NumPy Arrays.
Mastery of Pandas dataframes.
Ability to perform element-wise vector and matrix operations on NumPy arrays.
Knowing that you should use the Anaconda distribution and the conda package manager.
Familiarity with Scikit-learn. Scikit-Learn Cheat Sheet
Ability to write efficient list comprehensions instead of traditional for loops.
Ability to write small, clean functions (important for any developer), preferably pure functions that don’t alter objects.
Knowing how to profile the performance of a Python script and how to optimize bottlenecks.
The following will help to tackle any problem in data analytics and machine learning.
Selection bias is a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn’t random. It is sometimes referred to as the selection effect. It is the distortion of statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate.
The types of selection bias include:
Sampling bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.
Time interval: *A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
*Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants) discounting trial subjects/tests that did not run to completion.
Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up.
However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve.
The random variables are distributed in the form of a symmetrical bell-shaped curve.
Properties of Nornal Distribution:
Unimodal -one mode
Symmetrical -left and right halves are mirror images
Bell-shaped -maximum height (mode) at the mean
Mean, Mode, and Median are all located in the center
Asymptotic
It is a statistical hypothesis testing for a randomized experiment with two variables A and B.
The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest. A/B testing is a fantastic method for figuring out the best online promotional and marketing strategies for your business. It can be used to test everything from website copy to sales emails to search ads
An example of this could be identifying the click-through rate for a banner ad.
Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.).
Sensitivity is nothing but “Predicted True events/ Total events”. True events here are the events which were true and model also predicted them as true.
Calculation of seasonality is pretty straightforward.
Seasonality = ( True Positives ) / ( Positives in Actual Dependent Variable )
In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data.
In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.
Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model too would have poor predictive performance.
We will prefer Python because of the following reasons:
Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools.
R is more suitable for machine learning than just text analysis.
Python performs faster for all types of text analytics.
Data cleaning can help in analysis because:
Cleaning data from multiple sources helps to transform it into a format that data analysts or data scientists can work with.
Data Cleaning helps to increase the accuracy of the model in machine learning.
It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources.
It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.
Univariate analyses are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis.
The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis.
Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.
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