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Data science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data.
Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them.
[ Related Article – What is Data Science? ]
Selection bias occurs when sample obtained is not representative of the population intended to be analyzed
Python is the most prominent language used in Machine Learning as per my knowledge
However, R is also good. In one of the scenarios we were working, R was far better in time complexity when executing some recommendation based models.
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Linear regression is a statistical technique where the score of a variable Y is predicted from the score of a second variable X. X is referred to as the predictor variable and Y as the criterion variable.
Yes, it can be used but it depends on the applications.
A machine algorithm to parse data, learn from that data, and make informed decisions based on what it has learned. Basically, Deep Learning is used in layers to create an Artificial “Neural Network” that can learn and make intelligent decisions on its own.
Deep learning is one of the foundations of artificial intelligence (AI), and the current interest in deep learning is due in part to the buzz surrounding AI. At its simplest, deep learning can be thought of as a way to automate predictive analytics.
[ Related Article – Data Science with Deep learning ]
A recommendation system, or recommender system tries to make predictions on user preferences and make recommendations which should interest customers.
A recommendation system is any system that automatically suggests content for website readers and users. Recommender systems are one of the most common and easily understandable applications of big data.
R-Square can be calculated using the below formula –
1 - (Residual Sum of Squares/ Total Sum of Squares)
The R programming language includes a set of software suite that is used for graphical representation, statistical computing, data manipulation and calculation.
[ Related Article – Why does data scientist prefer R- language? ]
N-dimensional vector of numerical features that represent some object Term occurrences frequencies, pixels of an image etc. Feature space: vector space associated with these vectors
Corporate setups that require more hands on assistance & training choose SAS as an option. As per researchers & Statisticians choose R it helps in heavy calculations as they say R was meant for Job done & not to ease your computers. Python has been the best choice for startups today due to its lightweight nature & growing community.
SaaS is ruling the market now. So, from immediate job perspective: SaaS – R – Python, would be my rating. But I think with time, more structured data will be in place and then R & Python will be at par or may be more demanded.
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the star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries.
Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process.
The tools should also be used on a regular basis as inaccurate data levels can grow quickly, compromising database and decreasing business efficiency.
Linear regression is an important tool in analytics. The technique uses statistical calculations to plot a trend line in a set of data points. In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables.
Power is the probability of detecting an effect, given that the effect is really there. A power analysis involves estimating one of these four parameters given values for three other parameters. This is a powerful tool in both the design and in the analysis of experiments that we wish to interpret using statistical hypothesis tests.
Data design is the process of designing a database. The main output of a data design is a detailed logical data model of a database.
A data model gives you a conceptual understanding of how data is structured in a database it is hard coded into the DBMS software .So I can say it as a sort of facility given by the database.
Database Design- Database design is the system of producing a detailed data model of a database. The term database design can be used to describe many different parts of the design of an overall database system.
Classification methods are used to predict binary or multi class target variable. You could use conventional parametric models like logistic , multinomial regression, Linear discriminate analysis etc.
To be able to do data analysis in Python , you should be good with basics of Python and the below packages: Along with basics of python you should be comfortable in working in data frames and series also with multi-dimensional arrays and visualization.
Pandas: is most important in data analysis. You can load csv, excel, tables and variety of data formats and can view the in tabular format and work on rows and columns. – Python Pandas Tutorial
Numpy: is used for working with multi dimensional arrays.
Matplotlib: is used for visualization.
Univariate and multivariate represent two approaches to statistical analysis. Univariate involves the analysis of a single variable while multivariate analysis examines two or more variables. Most multivariate analysis involves a dependent variable and multiple independent variables.
Univariate analysis is the simplest form of data analysis where the data being analyzed contains only one variable. Since it’s a single variable it doesn’t deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it.
Bivariate analysis is used to find out if there is a relationship between two different variables. Something as simple as creating a scatterplot by plotting one variable against another on a Cartesian plane (think X and Y axis) can sometimes give you a picture of what the data is trying to tell you. If the data seems to fit a line or curve then there is a relationship or correlation between the two variables. For example, one might choose to plot caloric intake versus weight.
Multivariate analysis is the analysis of three or more variables. There are many ways to perform multivariate analysis depending on your goals. Some of these methods include Additive Tree, Canonical Correlation Analysis, Cluster Analysis, Correspondence Analysis / Multiple Correspondence Analysis, Factor Analysis, Generalized Procrustean Analysis, MANOVA, Multidimensional Scaling, Multiple Regression Analysis, Partial Least Square Regression, Principal Component Analysis / Regression / PARAFAC, and Redundancy Analysis.
The logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.
Logistic regression predicts the probability of an outcome that can only have two values (i.e. a dichotomy). The prediction is based on the use of one or several predictors (numerical and categorical). A linear regression is not appropriate for predicting the value of a binary variable for two reasons:
K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity. The results of the K-means clustering algorithm are:
No, they do not because in some cases it reaches a local minima or a local
optima point. You don’t reach the global optima point. It depends on the data and
starting conditions
A/B testing is a method of comparing two versions of a webpage or app against each other to determine which one performs better.
The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest. It enables you to analyze which one of them performs better and generate better conversion rates.
In an A/B test, you take a webpage or app screen and modify it to create a second version of the same page.
R Commander is used to import data in R language. To start the R commander GUI, the user must type in the command Rcmdr into the console. There are 3 different ways in which data can be imported in R language-
R programming supports five basic types of data structure namely vector, matrix, list, data frame and factor. This chapter will discuss these data structures and the way to write these in R Programming. Vector – This data structures contain similar types of data, i.e., integer, double, logical, complex, etc.
save (x, file=”x.Rdata”)
The function save() can be used to save one or more R objects to a specified file (in .RData or .rda file formats). The function can be read back from the file using the function load().
Interpolation is guessing data points that fall within the range of the data you have, i.e. between your existing data points.
Extrapolation is guessing data points from beyond the range of your data set. Interpolation is the estimation of a point between endpoints that have been sampled. Thus, the estimate is constrained by the sample values at the endpoints of the line segment over which you are estimating (and the estimated/expected function over the interval between the end points).
Extrapolation is an estimate of the value of a point in a range beyond that spanned by existing sample points. Because it is an extension of a trend, it is less constrained and as you move away from the last sampling point you have, the uncertainty in your estimate increases.
Root cause analysis (RCA) is a systematic process for finding and identifying the root cause of a problem or event.
Root cause analysis helps identify what, how and why something happened, thus preventing recurrence.
RCA has a wide range of advantages, but it is dramatically beneficial in the continuous atmosphere of software development and information technology.
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