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Top Interview Questions On Data Science
This blog post is a perfect guide for all the aspirants who are preparing for the Data Science Interview This post some of the most frequently asked questions on Data Science. Get ready to successfully face your Data Science interview.
- If You Had To Choose Between The Programming Languages R And Python, Which One Would You Use For Text Analytics?
In my opinion, I would choose Python for text analytics as it offers solid data analysis tools and simple data structures, cheers to its Panda library.
- Explain Resampling Methods
For estimating the precision of the sample statistics, exchanging label on data points and validating models resampling method is used.
- What Are Recommender Systems?
A recommender system is a subclass of information filtering system that seeks to predict the “rating” or “preference” that a user would give to an item. The best of recommender system can be seen in ecommerce websites
- How Do You Find The Correlation Between A Categorical Variable And A Continuous Variable?
It is conceivable to discover the connection between’s an unmitigated variable and a nonstop factor utilizing the examination of covariance system.
- Which Skills In Python Programming Are Essential For Data Analysis?
Skills in relation building built-in data types especially lists, dictionaries, tuples, and sets, knowledge of N-dimensional NumPy Arrays. Also possessing extensive knowledge to perform element-wise vector & matrix operations on NumPy array, skills to use Anaconda distribution and the conda package manager are also must.
- What Are The Different Deep Learning Frameworks?
Deep Learning frameworks are
- Microsoft Cognitive Toolkit
- What Is The Difference Between Data Science And Big Data?
Data Science can be interpreted as a field which can be applicable to data of any size. The term Big Data refers enormous levels of data that requires latest & advanced analytics models like Data Science to analyze.
Another major thing which you are required to do before attending the interview is to have a brief overview of the projects which you have so far worked on.
- What Is Gradient Descent?
The primary objective in the process of building a statistical model is to reduce the value of the cost function that is associated with the model. And the technique that is used for accurate determination of the minima of the cost function is Gradient Descent which is an iterative optimization technique.
- What Is Difference Between Supervised And Unsupervised Learning Algorithms?
Supervised learning are the class of algorithms in which model is trained by explicitly labelling the outcome. Ex. Regression, Classification
Unsupervised learning no output is given and the algorithm is made to learn the outcomes implicity Ex. Association, Clustering
- What Is A P-Value In Statistics?
P value is crucial towards reaching to the conclusion in the hypothesis testing. In the case where p-value is too small then null hypothesis is rejected and alternate is accepted & when p-value is large then null hypothesis is accepted.
- What Are The Different Types Of Missing Value Treatment?
- Deletion of values
- Guess the value
- Average Substitution
- Regression based substitution
- Multiple Imputation
- What Are Merge Two List And Get Only Unique Values?
List a = [1,2,3,4] List b= [1,2,5,6] A = list(set(a+b))
- Name Few Libraries That Is Used In Python For Data Analysis?
- Scikit learn
- Matplotlib\ seaborn
- What Are The Different Sampling Methods?
The different sample methods that are extensively in use are
- Random Sampling
- Systematic Sampling
- Stratified Sampling
- Quota Sampling
- How the root node is predicted in Decision Tree Algorithm?
Mathematical Formula “Entropy” is utilized for predicting the root node of the tree.
- What Are The Applications Of Machine Learning?
- Self Driving Cars
- Image Classification
- Text Classification
- Search Engine
- Banking, Healthcare Domain
- Where To Use R & Python?
When the data in use is structured then opting for R will be ideal & in the case where data in use is unstructured then using Python would be efficient.
Also, when it comes to handling large volumes of data then R becomes incapable Python backend working with Theano/tensor made it easy to perform it as fast comparing with R.
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