‘Machines can teach themselves.’ This phrase had captured our imagination the day it was coined, and it continues to do so. What kind of algorithms encompasses such a phenomenon? What are the Machine Learning basics? The answers to such questions have been revealed in various capacities over the years, but the curiosity around the subject strives for more.
One of the few misconceptions around Machine learning is that it doesn’t involve human intervention.
Machine learning algorithms are based on python programming and other such languages. These algorithms are not self-sufficient, at least in the initial stages. They are supervised and trained using data sets, to obtain primary outputs in the beginning. Once the algorithms based on python programming mature, they start recognizing complex relationships within data.
To get optimum results, the quality of Data used matters. The training data should be free from misclassifications. Otherwise, it may hamper the learning process of algorithms.  Only a few algorithms can overcome such misclassifications.
The quantity of Data should be calibrated as well. Exposing the algorithms to humongous sets of test-data may make them responsive to a specific information-niches. They may provide inaccurate results when fed with something other than test-data. So, it’s about maintaining a balance between under-training and over-training an algorithm.
Now that we are done with the revelations, let’s understand Machine learning basics. It comprises three essential components:

• Model: This component is responsible for identifying relationships and making predictions.
• Parameters: These are the factors which Model takes into consideration while making decisions.
• Learner: This component is responsible for comparing the predictions made and the actual outcome. Based on the dissimilarities found between the two, it adjusts the parameters.

Let’s understand the Machine Learning basics via a real-world scenario.
Let’s assume that there is a teacher, who wants her students to attain the best grade in a test. She wants to calculate the time her students should devote to their studies, to obtain the desired results. Let’s see how machine learning can help her find the solution.
Firstly, the teacher will set the parameters for the Model. In this case, parameters will be ‘Hours spent on studying’ and ‘Resulting scores’.
Suppose the teacher gives the following relationship between the parameters:

0 hours	50% Score
1 Hour	60% Score
2 Hours	70% Score
3 Hours	80% Score
4 Hours	90% Score
5 Hours	100% Score

Based on the relationship as mentioned earlier, the machine learning algorithms will form a predictive line of results for different inputs.
Once the machine learning model is established, the actual test results are entered by the teacher. Let’s assume that she enters the scores of four students along with their study-hours.
The above results or scores will act as the training data, through which the learner will refine the Model. It will assess the difference between the predictive results given by the original Model and the actual results. The parameters will be adjusted accordingly by the learner, to improve the accuracy of the Model.
For example, the relationship mentioned above between the parameters may be modified into the following.

0 hours	44% Score
1 Hour	54% Score
2 Hours	64% Score
3 Hours	74% Score
4 Hours	84% Score
5 Hours	94% Score
6 Hours	100% Score

As you can see, the predictions have been reworked, to get closer to the actual results. It must be noted that the Learner makes very minute adjustments for refining the Model. The training cycle can be repeated again and again until the perfect Model is created. A Model that can predict the correct scores based on study-hours.
Similar training cycles can be conducted for creating Models that can identify events and objects. There is so much to learn and reveal about Machine Learning that one write-up cannot suffice. Still, we hope that this write-up gave you a good insight into the mysteries of Machine Learning.

Before this article gets on to the details of machine learning, it will enlighten the reader about the definition and concepts of machine learning. Some of the researchers and studies suggest that machine learning is a technique of artificial intelligence where the system automatically learns from information and data.
IT professionals define machine learning as a process by which the machine automatically improves their performance and algorithms without getting programmed manually.  Thus it can be said that machine learning history has several benefits like publishing sports reports, driving cars automatically and improved interaction with humans with the help of algorithms.
Recent surveys have suggested that examining the machine learning history will give the industries overview that how machine learning works and how it is important to the evolution on a daily basis. The collection from the data presented below will make the reader clearly understand the origins of machine learning and how it has evolved with the present date.

History

Turning test (1950)

This is a test which named after Alan Turing. According to this test, he was in the constant search of stating the machine has its own intelligence. Thus in order to pass this test, the computer must able to convince that it is also a human operating system and operates with its own set of intelligence.

Computer learning program (1957)

Computer learning program is basically the first ever computer program written by Arthur Samuel. The program was designed in the form of a game known as checkers. The system would play this game and through its course would develop winning strategies of its own to win the game. This would prove that just like humans the machine also had artificial intelligence to make changes according to its own convenience.

Neutral network (1950)

The neutral network was first designed by Frank Rosenblatt. The neutral network came to be known as a perceptron. Now basically this was a network which works on human brains, meaning using this network stimulation of human thought can be analysed and processed.

Nearest Neighbor (1967)

Nearest Neighbor is basically a creative algorithm which was designed in order for the system to evaluate or recognize the pattern. For example, the pattern could involve a salesman’s route to a particular city ensuring that the salesman covers all the major cities that fall within the route

Stanford Cart (1979)

Stanford cart is basically a program which was designed by the students of Ford University to find out the obstacles in a living room. With the help of this program, the system would automatically find out the obstacles in the living room using its own artificial intelligence.

EBL (1981)

Earlier till the 70’s, machine learning was all about programming but 1981 saw a major change when Gerald Dejong introduced the world to explanation-based learning. Now explanation based learning or better known as EBL was training data analyses which would allow the system to capture the important documents and exclude the unimportant ones.

Net Talk (1985)

In 1985 Terry Sejnowski developed a program known as the Net talk. With the help of this program, the system would be able to pronounce words just as a baby does. The system will take note of the pronunciation of the baby and will automatically generate sound and words that would be an exact copy of the baby.

Data-driven approach (1990’s)

During the times of the 90’s, there came a time when machine learning moved from a knowledge-based approach to the data-based approach. With the help of this approach, the IT professionals could evaluate large chunks of data and draw conclusions from it.

ERA of 2000

The era of 2000 includes some major changes that took place in 2006. 2011, 2012, 2014. 2015 and 2016. As early as 2006 developed a program known as deep learning which would allow the system to analyze the videos, images, and text on its own. Come 2010, Microsoft was able to track human features giving the humans the ability to interact with the system in the form of movements and gestures.
By 2011 Google was able to identify the objects just as the cat does and by 2012 Google developed a program which shows specific results when a particular search was made in YouTube. For example, if the user searched for the dog then with the help of these programs only videos of dog will be shown. This program provided as a blessing to précised searching. In 2015 machine came in the form of machine learning online. Both Amazon and Microsoft were able to launch their own machine learning platform. Now with the help of machine learning online data could be available on multiple machines and now better prediction could be made future made which was possible due to the batch learning process.

Conclusion

Hence to conclude it can be said machine learning has faced various transformations over the years. Now it can be said that the computer basically have their own artificial intelligence where it can think and act on their own. This statement is contradicted by various researchers and scientist who say that a computer will think the same as a human brain does. This is like comparing apples with oranges. Though it cannot be unnoticed that transformation of machine learning growing at random space and there is no limitation to it. The main question arising here is that will the computers be able to grow continually as the data keeps getting larger and larger.

What are the top 15 Data Analyst Interview Questions and Answers?

Data analytics has emerged as the latest hotshot for organisations, with tremendous opportunities arising daily in the industry. 

Here are some of the most asked data analyst interview questions one may encounter while sitting for a data analytics job.

• What are the key skills required for becoming a data analyst?

To become a data analyst, you must possess strong Microsoft Excel skills. 

A typical data analyst’s job responsibilities involve gathering and organising the data.

• What qualifications are necessary to become a data analyst?

A data analyst must have a thorough understanding of business-related tools, statistics, mathematics, and computer languages like Java, SQL, C++, etc. 

For the profession, one also needs solid analytics training, data mining knowledge, pattern identification skills, and problem-solving aptitude.

• What does “data cleansing” mean?

Data cleansing refers to the process of detecting and removing any inconsistency or errors from the data to improve its quality. 

• What are some of the best tools for data analysis?

Some of the most useful tools for data analysis are Google Search Operators, KNIME, Tableau, Solver and RapidMiner.

• What is the KNN imputation method?

KNN imputation method refers to the attribution of the values of missing attributes by using the attribute values nearest to the missing ones. 

• Mention some best techniques for data cleansing.

Some of the best techniques for data cleansing are –

• Sorting of the data, which organises them based on their categories.
• Focusing attention on the summary statistics for each column 
• Getting mastery of regular expression
• Creating a set of utility functions, tools, and scripts 

• How is data mining different from data profiling?

Data mining focuses on identifying essential records, analysing data collections, discovering sequences, etc. 

Data profiling, on the other hand, is concerned with analysing individual attributes of the data and providing valuable information on those attributes such as data type, length etc.

• What are data validation methods?

There are two ways to validate data:

• Data verification – once the data has been gathered, a verification is done to check its accuracy and remove any inconsistency from it.
• Data screening – inspection or screening of data is done to identify and remove errors from it (if any) before commencing the analysis of the data.

• Name some common issues associated with a data analyst career.

Some common issues which data analysts face are Missing values, Miss-spelt words, Duplicate values and Illegal values.

• What is an Outlier?

The term outlier refers to a value which appears far away and diverging from an overall pattern in a sample. 

• What is logistic regression?

Logistic regression or logit regression is a statistical method of data examination where one or more independent values define an outcome.

• Mention the various steps in an analytics project.

Various steps in an analytics project –

• Definition of problem
• Exploration of data
• Preparation of data
• Modelling
• Validation of data
• Implementation and tracking

• What are the missing patterns generally observed in data analysis?

Some of the commonly observed missing patterns are –

• Missing completely at random
• Missing at random
• Missing that depends on the unobserved input value
• Missing that depends on the missing value itself

• How can multi-source problems be dealt with?

One can deal with multi-source problems by –

• Restructuring schemas for attaining schema integration
• Identifying similar records and merging them together 

• What are the ways to detect outliers? 

Outliers are detected using two methods. 

Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range).

Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).

Use this salary calculator to calculate your potential salary

10 Ingenious Practical Applications Of Data Analytics 

Data Science is changing industries, and from healthcare to disaster management, all sectors are waking up to the significance of data analytics. But what is data analytics, exactly? And how does it help the tech giants? Data Analytics refers to collecting and examining data, which the data scientists then process to extract information. Right now, the success of companies like Facebook, Amazon and Google depends on predictive analytics and are investing more and more money to recruit data engineers and scientists.

When we broach the question, ‘what is data analytics?’ we must also talk about its practical approach and how it helps us and makes our lives easier. Tech professionals who acquire a data science certification are headed for a very lucrative career path, but how does data science help us who are not pros at analytics?

So, what are some of the practical applications of data analytics?

Your recommendations

All those helpful suggestions Amazon offers whenever looking for alternate options during online shopping are mainly thanks to data science. Data analytics gives the site access to your preferences, needs, location, and purchase history and offers you items you are likely to go for. This happens when a range of data is analysed and processed to arrive at a shortlisted conclusion. This doesn’t just improve the user’s experience but also helps them make an informed decision, and this area primarily uses predictive analytics.

How does picture recognition work?

You might have noticed when you upload an image on Facebook that you get a suggestion to tag your friends on it. This automatic feature mainly uses an algorithm based on recognising physical characteristics. The same algorithm also applies to other types of recognition of inanimate features, perhaps. For instance, when you log into WhatsApp on your web, the barcode you need to scan also uses the same algorithm. Google also uses this algorithm to look for an image from other sources if you command it.

Data science has transformed gaming

From single-player games to interactive ones, data science has brought about incredible breakthroughs in gaming. The algorithms used by gaming companies mainly study user behaviour and history and enhance the player experience, depending on the game’s purpose. For example, in single-player games, the computer, which is often your virtual opponent, examines your moves and technique to interact with you accordingly.

What impact does it have on education?

How can you work in the education field after you acquire a data analytics certification? Data science helps schools and educational organisations equip themselves with the strengths of data science by studying student preferences and assessing their needs. Data science is, of course, also convenient for evaluating their students’ talents and scores.

Tracking locations

The beneficial delivery tracking systems you check when you place an order online are mainly the work of data science. The tracking of the item from dispatch to its entire transit to its delivery time and status depends on the assimilation of data about real-time information about schedules, weather, traffic etc. Alternatively, it also helps retail companies improve their user experience as it can tell you exactly when your product will reach you.

Advertising makes use of analytics

The advertising world also uses data analytics by studying user behaviour and after studying whatever data they have. Your location, for instance, your search history, previous orders or downloads. Then, it uses algorithms to determine what ads you might like to see. This would explain the ads you stumble upon on Facebook, which show you precisely what you were searching for and other options from the same site.

Your media needs are controlled by it

Whenever you browse through Netflix, you will see it recommends movies or shows based on your previous searches. This is one of the most practical data analysis uses you’ll see now. The streaming portal studies what you are watching and clicking on to recommend content that matches your interests. For instance, Netflix tells you that it recommends shows X, Y and Z because you watched a specific show in the same genre or language.

In what ways does it benefit sports?

Sports have a history of data analytics, as teams have often used analytics to prepare for major league tournaments. With help from data analytics, data is assimilated by the sports teams to prepare their players better and to equip the team against a specific team by studying the opponent’s behaviour.

It has improved healthcare

Data analytics has revolutionised the world of healthcare, not just for the medical industry but also for patients. Just like hospitals and doctors have access to their patients’ data and preferences and the history of their patients, people seeking medical services too have access to a sea of information when making decisions about their healthcare or their medical policies.

Banking is made easier with it

Banks collect and study the data about their customers, from their credit scores to their economic preferences. They also use data science tools to find ways to benefit their clients so that they can use more innovative policies and faster loans. It also uses the data to improve its personalised marketing services.

On the landscape of technological advancements, Machine Learning is taking giant strides. Every sector is getting infused with artificial intelligence be it social networks, retail stores, automobiles, home appliances etc. It is no more the ‘Next’ big thing, and it is the ‘Thing’ today.
‘What is machine learning?’ is something most of us have read about, but the famous question is, Why it has grabbed so many eyeballs over the years?  Primarily because it can predict events and spot patterns, that most humans are not efficient at. A developer cannot write code for every possible scenario. He or she can work around a specific data set, but can’t make generalised conclusions, which Machine learning can achieve.
Also Read: What is The Easiest Way To Learn Machine Learning?
Machine learning is a complex subject, and its education can never be complete. There are so many exciting aspects to understand about ML. You can go for a Machine learning course in India to realise its nitty-gritty. We have discussed some of the issues below:

Machine Learning comprises of three stages, namely, Representation, Evaluation, and Optimization

• Representation: In this stage, a classifier is converted to a language that a machine can understand. Moreover, a set of classifiers, also known as hypothesis space, is dedicated for a learner.
• Evaluation: Once the classifiers are chosen, it’s important to segregate good classifiers from the bad ones. The internal function used for evaluating the classifier is different from the external feature of the algorithm. The classifier itself optimises the outer function.
• Optimization: Finally, the predictions made by the Model and the actual outcome are compared. Based on this comparison the parameters are optimised so that that perfect outcome can be obtained.

Generalization is the soul of Machine Learning

The essence of Machine Learning lies in generalising so that it can go beyond the scope of specific data-sets, and predict never-seen-before events. An efficient ML model is the one which can quickly adapt to new or unseen data. It’s like how humans learn to drive. They don’t learn to drive on specific roads, but they learn the skill of driving to traverse all kinds of routes and paths.
The ML Model will generalise better if the data is reliable and contains a broad spectrum of observations. It will be easier for the Model to discover the underlying mapping if data are more representative than others. You can understand this concept further by opting for the best Machine learning course in India.

Feature engineering is critical for Machine Learning

Feature engineering enhances Machine Learning Algorithms by utilising core domain knowledge of the data. It develops features using the raw data to improve the predictive ability of algorithms. Such features make the process of Machine Learning a lot easier, as they seamlessly correlate with the class. 

Machine Learning Models also give ‘Too good to be true’ results

To predict an outcome, the Machine learning Model receives the training-data first, and the testing-data afterwards. If the accuracy of the consequences is satisfactory, then the complexity of data is increased to improve the prediction-ability of algorithms. At times, this approach backfires, as the Model becomes too complicated and starts giving poor results. In other words, too much of data stops the learning of algorithms, and instead, they start memorising. Such a model produces a graph where the prediction-line covers the noise-points as well. It produces results that are too good to be true. The best way to deal with Overfitting is to generalise the Model.

Machine learning is not insulated from human errors.

Machine learning won’t take over Humanity, as most of us believe. It is, in fact, vulnerable to human errors. Whenever there is a glitch in the Machine Learning models, the algorithms are rarely responsible for that. Mostly, a human error leads to inappropriate training data, which in turn leads to other systematic errors.
So we may know the answer to ‘What is Machine Learning?’ But by believing that it will surpass us, we are evading our responsibility. We will always be in the driver seat, and it’s our discipline which will decide its future course.
Related Articles: 

• 10 Machine Learning Use Cases You Should Know About
• 7 Key Skills Required For Machine Learning Jobs

Before machine learning was acceptable globally, multinational tech companies like Google, Facebook and Amazon used machine learning secretly. Google with the help of machine learning took care of ad placement whereas Facebook with the help of machine learning showed post feeds according to their convenience. Amazon showed recommendations in the e-commerce website which impacted the clicks of the user. For example, if the user purchased a shoe recently then Amazon used this information to show recommendations for other shoes of the same shape and structure to entice the user for further exploration.
Though machine learning has undergone modifications from very recently, but is now also the centre of an important topic for debate – Is machine learning the end of privacy for the human race? What else can machine learning do other than driving cars automatically and communication? Is machine learning harmful to mankind?  These are some questions that researchers and IT professionals debate when the topic of machine learning training comes up.  Since machine learning training happens to be the future of evolution there are certain misconceptions that surround it. This article will provide a quick view of several misconceptions that have taken a hold over the time.

Machine learning only means generating compact data

Most of the people have a common misconception that machine learning only means summarising data. What they fail to realize is that the main function of machine learning is to predict the future. For example, if a user has watched some movies in the past then with the help of machine learning the system would able to tell the user what are the types of movies that the user wants to watch next.

Algorithms in machine learning are there just to discover the relationship between events

The media has presented just one side of AI to the minds of the people. What people watch is what they believe in. The media has played an important role in making people believe machine learning algorithms is only used for discovering relationship but in reality, they are used to discover knowledge.

Machine learning is useless when it comes to relationships

Most of the people believe that machine learning is only helpful in identifying correlations but when it comes to a causal relationship, it serves as a complete failure. This view is completely faulty as the machine can look at the entire data and derive relationship from the past data to the current one.

Machine learning is not helpful when it comes to unseen events

Most of the people think that machine learning is unable to predict unseen events which are commonly known as “Black Swan” events.  In reality, machine learning is designed to predict events with high accuracy.

More data in machine learning leads to hallucinating patterns

For example, consider an example where NASA checks their records and accidentally matches an innocent to their terrorist identification rule. These “hallucinating patterns” are made because more mining is done with people sharing same entities and attributes. In reality, machine learning keeps hallucinating at a very low rate.

There is no space for pre-existing knowledge in machine learning

Many IT professionals feel that machine learning has a blank state meaning that it only derives knowledge from the algorithms. According to these professionals, real reasoning power can only be derived from past experimentation and reasoning. In reality, all algorithms in machine learning do not have blank state and some user data is required to modify the systems knowledge.

Complex models are not accurate

This means that many people think that simple infrastructure of machine learning tends to be more accurate. This view is completely untrue because a simple infrastructure can come with a complex data and the complex model can come with the simplest of data which is very easy to understand.

Face value determines the patterns

Consider a user making the search for a mole in their skin, then there can be results saying that a tumor which can lead to cancer. This view is completely wrong as the slight change in data could modify the patterns, for example, if the user now searches for mole with a red patch which would only tell that it is just a simple mole and there is no reason to worry about.

Machine learning will be as same as human intelligence

Yes, machine learning can modify from day to day basis but it can never arise in level with human intelligence because humans are the ones who have designed machine language. Scientist and the IT professionals who deal with them on regular basis know their Importance.

Models are incompressible

This means that some of the models which have machine learning implemented in them may not have a trustworthy recommendation and some may have a trustworthy recommendation.  This misconception can be better be understood by people who have studied machine learning in India like Balaram Ravindran and Bidyut Baran Chaudhuri.  Machine learning in India is not a popular concept but one of the major cities that famous for it is Bengaluru.

Are you planning to take a machine learning course? If yes, then you are the right place. Machine learning is an excellent skill to have, especially in a time where most of the world is depending on technology to get the majority of the work done. Before you jump on to the bandwagon and start your course, there are a few preparations which you should make in order to smooth sail your way through the course.
Model building is one of the most important aspects of a machine learning course. There are a lot of algorithms and data which needs to be understood to be able to create an accurate model, which performs the job perfectly. So, what we basically mean is, machine learning includes lots and lots of data, you will have to manage it and not be intimidated by it.

What is machine learning in India?

The prospects of machine learning are excellent in this country. It is an all new world of data science, and you will most certainly have to have an understanding of data. You must also have knowledge of data tools like Python, in order to excel in this field. Plus, you will have to self-learn, before stepping into a course, so go through books, online study materials and videos, in order to prepare yourself for what is coming. If you want to know what is machine learning in India and how can it help you, then you ought to join a course. Go through online studies, and theories, practice them before setting your foot into the world of machine learning.

Getting together all the data

As mentioned before, data is king when it comes to the deep learning of this subject. This happens to be a crucial step because the quantity, as well as the quality of the data, will determine the accuracy of your model. So be thorough with your data and compile it with a lot of care and attention.

Prepare the data

There is nothing a little bit of prep cannot solve. Data preparation includes loading the data carefully, segregating it and then using it in machine learning. Keep an eye on things like the relationship between different variables, (if any), or look out for data imbalances as well. The data has to be divided into two parts, the first one will be used to train the model and the second will be required to evaluate the accuracy of the trained model. Chances are, that you might have to manipulate the data or adjust it, so keep going through it and try to make it as error-free as you can.

Choosing the right model

It is one of the most crucial jobs. You need to have a proper workflow model! Just go through the different research models created by other data scientists, which are similar in nature and get a good model to make your work cohesive and accurate.

Training

This is, hand’s down one of the most important steps in the course of the preparation. There are many features, in the training process, you have to initialize the experiments and attempt to predict the outcome. In the first instance, the model will definitely perform inaccurately. So you will have to train your model and keep on adjusting your values to have better and correct predictions. There is a lot of trial and error which goes into it. Go on repeating the process, and with each step you will notice the progress. With training the output will become more and more accurate.

Evaluate, evaluate, evaluate

The second set of data, which is saved for the evaluation stage comes into play now. This way, you will be able to test your model with new data, and this metric helps you to perfectly determine how accurately the model can perform. This will give you a good idea of how it would perform in real life situation and how much tweaking does it need to become perfect.

Tune the parameters

The evaluation step is a tough one, so once you get past that, you will be charged up to improve your model and make it perfect. Parameter tuning is imperative, so go back the assumptions you made in the previous steps and try other values. Go through the training data set multiple times to get a more accurate result. The “hyperparameters” can be easily adjusted and tuned. By all means, all the tweaking is very experimental in nature and depends on your model, training process and dataset.

Prediction

As you must have realized this is one of the final steps in this series. Now you will be able to know, whether the model you have built with so much effort is being able to provide with accurate results or not. You can rely on your model to derive an inference with regards to the reason why it has been designed.
A deep learning of machine learning will require you to understand data and use it in the best way possible to derive the results you want from your model. There are several steps which will follow, but the aforementioned steps will help you build a strong foundation and delve deeper into a machine learning course.

R is a programming language and the environment which is used in the case of statistical computing, data analytics, and the research. It is termed as one of the most popular languages that are commonly used by the statisticians, researchers, data analysts, and the marketers to retrieve, clean, analyse, visualise and to present the data. Because of its communicative syntax and easy-to-use interface method, it has fully grown in popularity in the recent years. Codes in R are far more compact as compared to the SAS. However, it makes the language tougher to retain all the syntax. You may probably need plenty of practice to get the hang of it. This makes some of the interview questions on R tricky and thus handling them becomes overwhelming for a few candidates. And, I strongly feel a desire for a common thread which should have all the tricky R queries asked in interviews. So that is why this article has come up with some of the Tricky R interview questions that are inevitably going to guide you towards the success.
Also Read: Advantages Of R Programming Language

1) Explain the data import in R language.

R provides an option to import data in R language. To start with the R commander user interface, the user should type the commands within the command R commander into the console. Then, data is imported in R language in the three ways such as –
Select the data set within the window or enter the name of the data set as required.
Data can be easily entered directly by using the editor of R Commander via data -> New Data Set. This works well only if the data set is not significant.
Data can also be imported from a URL or the open document (ASCII), or from any statistical package or the clipboard.

2) In R how will you be able to import Data?

You can easily import data by using R commander to import data in R, and there are three ways through that you can use to enter data into it.

• New dataset. – You can enter data directly via data
• Import data from plain text (ASCII) or the alternative files (SPSS, Minitab, etc.)
• Read a dataset either by writing the name of the data set or by choosing the data set within the window.

3) What are the data structures in R that’s used to perform statistical analyses and build graphs?

R has data structures like the –

• Vectors – A vector can easily be defined as a set of sequence of data elements of the same basic type. Here is an example of the vector containing three numeric values 2, 3 and 5. (2, 3, 5)
• Matrices – A matrix is an assortment of data elements organised in a two-dimensional rectangular layout.
• Arrays – Arrays are the type of R data objects or say elements of the same data types which can easily store the data in more than two dimensions. Arrays can store only data type. An array is created using the array () function.
• Data frames – A data frame can be defined in the form of a table or the two-dimensional array-like structure in which every column contains values of 1 variable {and every} row contains one set of values from each of the column.

4) Mention what doesn’t ‘R’ language do?

• Though R programming can easily connect to DBMS isn’t a database, we can not claim R as a graphical user interface as it doesn’t consist any
• Though it relates to Excel/Microsoft Office easily, R language does not offer any spreadsheet view of the data.

Related Articles:

• Why R Programming is better than SAS?
• Importance of R Programming As a Tool in the Machine Learning Spaces