### What Is A Recommender System?

A recommender system is 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 filte

ring or by deploying a personality-based approach. This type of system works

based on a person’s past behavior in order to build a model for the future. This

will predict the future product buying, movie viewing or book reading by

people. It also creates a filtering approach using the discrete characteristics of

items while recommending additional items.

### Compare SAS, R And Python Programming?

**SAS**: it is one of the most widely used analytics tools used by some of the

biggest companies on earth. It has some of the best statistical functions,

graphical user interface, but can come with a price tag and hence it cannot be

readily adopted by smaller enterprises

**R**: The best part about R is that it is an Open Source tool and hence used

generously by academia and the research community. It is a robust tool for

statistical computation, graphical representation and reporting. Due to its open

source nature it is always being updated with the latest features and then

readily available to everybody.

**Python**: Python is a powerful open source programming language that is easy

to learn, works well with most other tools and technologies. The best part

about Python is that it has innumerable libraries and community created

modules making it very robust. It has functions for statistical operation, model

building and more.

### Explain The Various Benefits Of R Language?

The R programming language includes a set of software suite that is used for

graphical representation, statistical computing, data manipulation and

calculation.

Some of the highlights of 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

– It acts as a connecting link between various software, tools and datasets

– Create high quality reproducible analysis that is flexible and powerful

– Provides a robust package ecosystem for diverse needs

– It is useful when you have to solve a data-oriented problem

### How Do Data Scientists Use Statistics?

Statistics helps 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 the consumer behavior, interest, engagement, retention and finally conversion all through the power of insightful statistics. It helps them to build powerful data models in order to validate certain inferences and predictions. All this can be converted into a powerful business proposition by giving users what they want at precisely when they want it.

### What Is Logistic Regression?

It is a statistical technique or a model in order 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 of 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.

Once the data is cleaned it confirms with the rules of the data sets in the

system. Data cleansing is an essential part of the data science because the data

can be prone to error due to human negligence, corruption during

transmission or storage among other things. Data cleansing takes a huge

chunk of time and effort of a Data Scientist because of the multiple sources

from which data emanates and the speed at which it comes.

### Describe Univariate, Bivariate And Multivariate Analysis.?

As the name suggests these are analysis methodologies having a single,

double or multiple variables.

So a univariate analysis will have one variable and due to this there are no

relationships, causes. The major aspect of the univariate analysis is to

summarize the data and find the patterns within it to make actionable

decisions.

A Bivariate analysis deals with the relationship between two sets of data.

These sets of paired data come from related sources, or samples. There are

various tools to analyze such data including the chi-squared tests and t-tests

when the data are having a correlation.

If the data can be quantified then it can analyzed using a graph plot or a

scatterplot. The strength of the correlation between the two data sets will be

tested in a Bivariate analysis.

### How Machine Learning Is Deployed In Real World Scenarios?

Here are some of the scenarios in which machine learning finds

applications in real world:

Ecommerce: Understanding the customer churn, deploying targeted

advertising, remarketing.

Search engine: Ranking pages depending on the personal preferences of the

searcher

Finance: Evaluating investment opportunities & risks, detecting fraudulent

transactions

Medicare: Designing drugs depending on the patient’s history and needs

Robotics: Machine learning for handling situations that are out of the

ordinary

Social media: Understanding relationships and recommending connections

Extraction of information: framing questions for getting answers from

databases over the web.

### What Are The Various Aspects Of A Machine Learning

Process?

In this post I will discuss the components involved in solving a problem using

machine learning.

Domain knowledge:

This is the first step wherein we need to understand how to extract the various

features from the data and learn more about the data that we are dealing with.

It has got more to do with the type of domain that we are dealing with and

familiarizing the system to learn more about it.

Feature Selection:

This step has got more to do with the feature that we are selecting from the set

of features that we have. Sometimes it happens that there are a lot of features

and we have to make an intelligent decision regarding the type of feature that

we want to select to go ahead with our machine learning endeavor.

Algorithm:

This is a vital step since the algorithms that we choose will have a very major

impact on the entire process of machine learning. You can choose between the

linear and nonlinear algorithm. Some of the algorithms used are Support

Vector Machines, Decision Trees, Naïve Bayes, K-Means Clustering, etc.

Training:

This is the most important part of the machine learning technique and this is

where it differs from the traditional programming. The training is done based

on the data that we have and providing more real world experiences. With

each consequent training step the machine gets better and smarter and able to

take improved decisions.

Evaluation:

In this step we actually evaluate the decisions taken by the machine in order

to decide whether it is up to the mark or not. There are various metrics that are

involved in this process and we have to closed deploy each of these to decide

on the efficacy of the whole machine learning endeavor.

Optimization:

This process involves improving the performance of the machine learning

process using various optimization techniques. Optimization of machine

learning is one of the most vital components wherein the performance of the

algorithm is vastly improved. The best part of optimization techniques is that

machine learning is not just a consumer of optimization techniques but it also

provides new ideas for optimization too.

Testing:

Here various tests are carried out and some these are unseen set of test cases.

The data is partitioned into test and training set. There are various testing

techniques like cross-validation in order to deal with multiple situations.

### What Do You Understand By The Term Normal

Distribution?

It is a set of continuous variable spread across a normal curve or in the shape

of a bell curve. It can be considered as a continuous probability distribution

and is useful in statistics. It is the most common distribution curve and it

becomes very useful to analyze the variables and their relationships when we

have the normal distribution curve.

The normal distribution curve is symmetrical. The non-normal distribution

approaches the normal distribution as the size of the samples increases. It is

also very easy to deploy the Central Limit Theorem. This method helps to

make sense of data that is random by creating an order and interpreting the

results using a bell-shaped graph.

### What Is Linear Regression?

It is the most commonly used method for predictive analytics. The Linear

Regression method is used to describe relationship between a dependent

variable and one or independent variable. The main task in the Linear

Regression is the method of fitting a single line within a scatter plot.

The Linear Regression consists of the following three methods:

Determining and analyzing the correlation and direction of the data

Deploying the estimation of the model

Ensuring the usefulness and validity of the model

It is extensively used in scenarios where the cause effect model comes into

play. For example you want to know the effect of a certain action in order to

determine the various outcomes and extent of effect the cause has in

determining the final outcome.

### What Is Interpolation And Extrapolation?

The terms of interpolation and extrapolation are extremely important in any

statistical analysis. Extrapolation is the determination or estimation using a

known set of values or facts by extending it and taking it to an area or region

that is unknown. It is the technique of inferring something using data that is

available.

Interpolation on the other hand is the method of determining a certain value

which falls between a certain set of values or the sequence of values.

This is especially useful when you have data at the two extremities of a

certain region but you don’t have enough data points at the specific point.

This is when you deploy interpolation to determine the value that you need.

### 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 specific probability in a sample size constraint.

The various techniques of statistical power analysis and sample size

estimation are widely deployed for making statistical judgment that are

accurate and evaluate the size needed for experimental effects in practice.

Power analysis lets you understand the sample size estimate so that they are

neither high nor low. A low sample size there will be no authentication to

provide reliable answers and if it is large there will be wastage of resources.

### What Is K-means? How Can You Select K For K-means?

K-means clustering can be termed as the basic unsupervised learning

algorithm. It is the method of classifying data using a certain set of clusters

called as K clusters. It is deployed for grouping data in order to find similarity

in the data.

It includes defining the K centers, one each in a cluster. The clusters are

defined into K groups with K being predefined. The K points are selected at

random as cluster centers. The objects are assigned to their nearest cluster

center. The objects within a cluster are as closely related to one another as

possible and differ as much as possible to the objects in other clusters. Kmeans

clustering works very well for large sets of data.

### How Is Data Modeling Different From Database Design?

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 the 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 Are Feature Vectors?

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

### Explain The Steps In Making A Decision Tree.?

Take the entire data set as input

Look for a split that maximizes the separation of the classes. A split is

any test that divides the data in two sets

Apply the split to the input data (divide step)

Re-apply steps 1 to 2 to the divided data

Stop when you meet some stopping criteria

This step is called pruning. Clean up the tree when you went too far

doing splits.

### What Is Root Cause Analysis?

Root cause analysis was initially developed to analyze industrial accidents,

but is now widely used in other areas. It is basically a technique of problem

solving used for isolating the root causes of faults or problems. A factor is

called a root cause if its deduction from the problem-fault-sequence averts the

final undesirable event from reoccurring.

### Explain Cross-validation.?

It is a model validation technique for evaluating how the outcomes of a

statistical analysis will generalize to an independent data set. Mainly used in

backgrounds where the objective is forecast and one wants to estimate how

accurately a model will accomplish in practice.

The goal of cross-validation is to term a data set to test the model in the

training phase (i.e. validation data set) in order to limit problems like over

fitting, and get an insight on how the model will generalize to an independent

data set.

### What Is Collaborative Filtering?

The process of filtering used by most of the recommender systems to find

patterns or information by collaborating perspectives, numerous data sources

and several agents.