Data Scientist Interview Question

If you want to Become data scientist you have must know these questions. A data scientist should not only be evaluated only on his/her knowledge on machine learning, but he/she should also have good expertise in statistics. Prepare from onlineitguru is
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Explain The Various Benefits Of R Language?

The R programming language includes a set of a software suite that is used for graphical representation, statistical computing, data manipulation, and calculation.

     what is data science?

Data science is a "concept to unify statistics, data analysis, machine learning, and their related methods" 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.

  

     What is selection Bias?

Selection bias occurs when the sample obtained is not representative of the population intended to be analyzed

    Which language is more suitable for text analytics? R or Python?

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.

   What is the difference between machine learning and deep learning?

A machine algorithm to parse data, learn from that data, and make informed decisions based on what it has learned. Deep Learning is used in layers to create an Artificial “Neural Network” that can learn and make intelligent decisions on its own.

  What is Star Schema?

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.

   What is the difference between data design and data model?

Data design is the process of designing a database. The main output of 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 hardcoded 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.

Data Scientist Interview Question

Some of the highlights of the R programming environment include the following:

An extensive collection of tools for data analysis Operators for performing calculations on matrix and array Data analysis technique for graphical representation

A highly developed yet simple and effective programming language It extensively supports machine learning applications

How did you get interested in working with data?

A - Like many data scientists, I have a classical pure science background and data are the lifeblood of the empirical scientific method. If you want to be a scientist, you have to care about data.

Where can data science create the most value in online retail?

A - There is the old cliché about the right thing at the right place at the right time. One Kings Lane was always trying to find the right balance between getting products that the data science team and OKL buying team believed individual customers would love versus serendipity, letting them explore and chancing upon something new, different and unexpected

What does the future of Data Science look like?

A - I think that it is very bright indeed. Computational power is only going to get bigger and faster, algorithms (such as deep learning) will become easier to train and use, and hopefully, tooling will be easier for the average non-technical user to harness machine learning.

What Is A Recommender System?

A recommender system is a today widely deployed in multiple fields like movie recommendations, music preferences, social tags, research articles, search queries and so on. The recommender systems work as per collaborative and content-based filtering or by deploying a personality-based approach. This type of system works based on a person’s past behavior to build a model for the future.

How Do Data Scientists Use Statistics?

Statistics help Data Scientists to look into the data for patterns, hidden insights and convert Big Data into Big insights. It helps to get a better idea of what the customers are expecting. Data Scientists can learn about consumer behavior, interest, engagement, retention and finally conversion all through the power of insightful statistics. It helps them to build powerful data models to validate certain inferences and predictions.

What Is Logistic Regression?

It is a statistical technique or a model to analyze a dataset and predict the binary outcome. The outcome has to be a binary outcome that is either zero or one or a yes or no.

Why Data Cleansing Is Important In Data Analysis?

With data coming in from multiple sources it is important to ensure that data is good enough for analysis. This is where data cleansing becomes extremely vital. Data cleansing extensively deals with the process of detecting and correcting data records, ensuring that data is complete and accurate and the components of data that are irrelevant are deleted or modified as per the needs. This process can be deployed in concurrence with data wrangling or batch processing.

What Is Power Analysis?

The power analysis is a vital part of the experimental design. It is involved with the process of determining the sample size needed for detecting an effect of a given size from a cause with a certain degree of assurance. It lets you deploy a specific probability in a sample size constraint.

How Is Data Modeling Different From Database Design? Answer:

Data Modeling: It can be considered as the first step towards the design of a database. Data modeling creates a conceptual model based on the relationship between various data models. The process involves moving from the conceptual stage to the logical model to the physical schema. It involves the systematic method of applying data modeling techniques.

Database Design: This is the process of designing the database. The database design creates an output which is a detailed data model of the database.

Strictly speaking, database design includes the detailed logical model of a database but it can also include physical design choices and storage parameters.

What is logistic regression? Or State an example when you have used logistic regression recently.

Logistic Regression often referred to as the logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent on election campaigning of a particular candidate, the amount of time spent in campaigning, etc.

What are Recommender Systems?

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

 Why data cleaning plays a vital role in the analysis?

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because - as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of the analysis task.

 Differentiate between univariate, bivariate and multivariate analysis.

These 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 be referred to as univariate analysis.

 Can you use machine learning for time series analysis?

Yes, it can be used but it depends on the applications.

Which Function In R Language Is Used To Find Out Whether The Means Of 2 Groups Are Equal To Each Other Or Not?

t.tests ()

What Is The Best Way To Communicate The Results Of Data Analysis Using R Language?

The best possible way to do this is to combine the data, code and analysis results in a single document using knitr for reproducible research. This helps others to verify the findings, add to them and engage in discussions. Reproducible research makes it easy to redo the experiments by inserting new data and applying it to a different problem.

How Many Data Structures Does R Language Have?

R language has Homogeneous and Heterogeneous data structures. Homogeneous data structures have the same type of objects – Vector, Matrix ad Array.

Heterogeneous data structures have a different type of objects – Data

frames and lists.

What Is The Process To Create A Table In R Language Without Using External Files?

MyTable= data.frame () edit (MyTable)

The above code will open an Excel Spreadsheet for entering data into

table.

Learn Data Science in R Programming to land a top gig as an Enterprise Data Scientist!

What Is The Difference Between Data Frame And A Matrix In R?

Data frame can contain heterogeneous inputs while a matrix cannot. In the matrix, only similar data types can be stored whereas in a data frame there can be different data types like characters, integers or other data frames.

How Can You Add Datasets In R?

bind () function can be used add datasets in R language provided the columns in the datasets should be same.

What Are Factor Variable In R Language?

A: Factor variables are categorical variables that hold either string or numeric values. Factor variables are used in various types of graphics and particularly for statistical modeling where the correct number of degrees of freedom is assigned to them.

What is an ensemble learning?

Ensemble learning is the art of combining more than one model to predict the outcome of an experiment. Commonly used ensemble techniques bagging, boosting and stacking

Name a few libraries that are used in python for data analysis?

Numpy Scipy Pandas Scikit learn

Matplotlib\ seaborn

What are the different types of data?

Data is broadly classified into two types 1) Numerical 2) Categorical Numerical variables is further classified into discrete and continuous data Categorical variables

Systematic Sampling Stratified Sampling

Quota Sampling are further classified into Binary, Nominal, and Ordinal data

What are the different ways of performing aggregation in python using pandas?

Group by function Pivot function Aggregate function

What are merge two lists and get unique values?

List a = [1,2,3,4] List b= [1,2,5,6] A = list(set(a+b))

What is a lambda function in python?

Lambda function is used to create the small, one-time anonymous function in python. It enables the programmer to create functions without a name and almost instantly.

How would you quantify the influence of a Twitter user?

like page rank with each user corresponding to the web pages and linking to the page equivalent to following.

How could you collect and analyze data to use social media to predict the weather?

We can collect social media data using twitter, Facebook, Instagram API’s. Then, for example, for twitter, we can construct features from each tweet, e.g. the tweeted date, number of favorites, retweets, and of course, the features created from the tweeted content itself. Then use a multivariate time series model to predict the weather.

What Is Descriptive Statistics?

We study in descriptive statistics the methods for organizing, displaying, and describing data.

What Is A Sample?

When data are collected in a statistical study for only a portion or subset of all elements of interest we are using a Sample.

Give An Example Of Inferential Statistics?

Example of Inferential Statistic :

You asked five of your classmates about their height. Based on this information, you stated that the average height of all students in your university or college is 67 inches.

A Normal Population Distribution Is Needed For The Which Of The Statistical Tests:

variance estimation. the standard error of the mean. Student's t-test.

What Is Covariance?

The measure of how much two variables change together. y2=[1 3 4 5 6 7 8]

cov(x,y2) %return 2*2 matrix, diagonal represents variance.

What Is One Sample T-test?

The t-test is any statistical hypothesis test in which the test statistic follows a Student's t distribution if the null hypothesis is supported.

[h,p,ci] = ttest(y2,0)% return 1 0.0018 ci =2.6280 7.0863

What Is Alternative Hypothesis?

The Alternative hypothesis (denoted by H1 ) is the statement that must be true if the null hypothesis is false.

What Is Significance Level?

The probability of rejecting the null hypothesis when it is called the significance level α , and very common choices are α = 0.05 and α = 0.01.

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