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What Is Data Science(with Examples), It's Lifecycle and Who exactly is a Data Scientist

Oh yes, Science is everywhere. A while ago, when children embarked on the journey of learning everyday science in school, the statement that always had a mention was “Science is everywhere”. The situation is more or less the same even in present times. Science has now added a few feathers to its cap. Yes, the general masses sing the mantra “Data Science” is everywhere. What does it mean when I say Data Science is everywhere? Let us take a look at the Science of Data. What are those aspects that make this Science unique from everyday Science?The Big Data Age as you may call it has in it Data as the object of study.Data Science for a person who has set up a firm could be a money spinnerData Science for an architect working at an IT consulting company could be a bread earnerData Science could be the knack behind the answers that come out from the juggler’s hatData Science could be a machine imported from the future, which deals with the Math and Statistics involved in your lifeData science is a platter full of data inference, algorithm development, and technology. This helps the users find recipes to solve analytically complex problems.With data as the core, we have raw information that streams in and is stored in enterprise data warehouses acting as the condiments to your complex problems. To extract the best from the data generated, Data Science calls upon Data Mining. At the end of the tunnel, Data Science is about unleashing different ways to use data and generate value for various organizations.Let us dig deeper into the tunnel and see how various domains make use of Data Science.Example 1Think of a day without Data Science, Google would not have generated results the way it does today.Example 2Suppose you manage an eatery that churns out the best for different taste buds. To model a product in the pipeline, you are keen on knowing what the requirements of your customers are. Now, you know they like more cheese on the pizza than jalapeno toppings. That is the existing data that you have along with their browsing history, purchase history, age and income. Now, add more variety to this existing data. With the vast amount of data that is generated, your strategies to bank upon the customers’ requirements can be more effective. One customer will recommend your product to another outside the circle; this will further bring more business to the organization.Consider this image to understand how an analysis of the customers’ requirements helps:Example 3Data Science plays its role in predictive analytics too.I have an organization that is into building devices that will send a trigger if a natural calamity is soon to occur. Data from ships, aircraft, and satellites can be accumulated and analyzed to build models that will not only help with weather forecasting but also predict the occurrence of natural calamities. The model device that I build will send triggers and save lives too.Consider the image shown below to understand how predictive analytics works:Example 4A lot many of us who are active on social media would have come across this situation while posting images that show you indulging in all fun and frolic with your friends. You might miss tagging your friends in the images you post but the tag suggestion feature available on most platforms will remind you of the tagging that is pending.The automatic tag suggestion feature uses the face recognition algorithm.Lifecycle of Data ScienceCapsulizing the main phases of the Data Science Lifecycle will help us understand how the Data Science process works. The various phases in the Data Science Lifecycle are:DiscoveryData PreparationModel PlanningModel BuildingOperationalizingCommunicating ResultsPhase 1Discovery marks the first phase of the lifecycle. When you set sail with your new endeavor,it is important to catch hold of the various requirements and priorities. The ideation involved in this phase needs to have all the specifications along with an outline of the required budget. You need to have an inquisitive mind to make the assessments – in terms of resources, if you have the required manpower, technology, infrastructure and above all time to support your project. In this phase, you need to have a business problem laid out and build an initial hypotheses (IH) to test your plan. Phase 2Data preparation is done in this phase. An analytical sandbox is used in this to perform analytics for the entire duration of the project. While you explore, preprocess and condition data, modeling follows suit. To get the data into the sandbox, you will perform ETLT (extract, transform, load and transform).We make use of R for data cleaning, transformation, and visualization and further spot the outliers and establish a relationship between the variables. Once the data is prepared after cleaning, you can play your cards with exploratory analytics.Phase 3In this phase of Model planning, you determine the methods and techniques to pick on the relationships between variables. These relationships set the base for the algorithms that will be implemented in the next phase.  Exploratory Data Analytics (EDA) is applied in this phase using various statistical formulas and visualization tools.Subsequently, we will look into the various models that are required to work out with the Data Science process.RR is the most commonly used tool. The tool comes with a complete set of modeling capabilities. This proves a good environment for building interpretive models.SQL Analysis Services SQL Analysis services has the ability to perform in-database analytics using basic predictive models and common data mining functions.SAS/ACCESS  SAS/ACCESS helps you access data from Hadoop. This can be used for creating repeatable and reusable model flow diagrams.You have now got an overview of the nature of your data and have zeroed in on the algorithms to be used. In the next stage, the algorithm is applied to further build up a model.Phase 4This is the Model building phase as you may call it. Here, you will develop datasets for training and testing purposes. You need to understand whether your existing tools will suffice for running the models that you build or if a more robust environment (like fast and parallel processing) is required. The various tools for model building are SAS Enterprise Miner, WEKA, SPCS Modeler, Matlab, Alpine Miner and Statistica.Phase 5In the Operationalize phase, you deliver final reports, briefings, code and technical documents. Moreover, a pilot project may also be implemented in a real-time production environment on a small scale. This helps users get a clear picture of the performance and other related constraints before full deployment.Phase 6The Communicate results phase is the conclusion. Here, we evaluate if you have been able to meet your goal the way you had planned in the initial phase. It is in this phase that the key findings pop their heads out. You communicate to the stakeholders in this phase. This phase brings you the result of your project whether it is a success or a failure.Why Do We Need Data Science?Data Science to be precise is an amalgamation of Infrastructure, Software, Statistics and the various data sources.To really understand big data, it would help us if we bridge back to the historical background. Gartner’s definition circa 2001, which is still the go-to definition says,Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs.When we break the definition into simple terms, all that it means is, big data is humongous. This involves the multiplication of complex data sets with the addition of new data sources. When the data sets are in such high volumes, our traditional data processing software fails to manage them. It is just like how you cannot expect your humble typewriter to do the job of a computer. You cannot expect a typewriter to even do the ctrl c + ctrl v job for you. The amount of data that comes with the solutions to all your business problems is massive. To help you with the processing of this data, you have Data Science playing the key role.The concept of big data itself may sound relatively new; however, the origins of large data sets can be traced back to the 1960s and the '70s. This is when the world of data was just getting started. The world witnessed the set up of the first data centers and the development of the relational database.Around 2005, Facebook, YouTube, and other online services started gaining immense popularity. The more people indulged in the use of these platforms, the more data they generated. The processing of this data involved a lot of Data Science. The masses had to store the amassed data and analyse it at a later point. As a platform that answers to the storage and analysis of the amassed data, Hadoop was developed. Hadoop is an open-source framework that helps in the storage and analysis of big data sets. And as we say, the rest will follow suit; we had NoSQL gaining popularity during this time.With the advent of big data, the need for its storage also grew. The storage of data became a major issue for enterprise industries until 2010. We have had Hadoop, Spark and other frameworks mitigating the challenge to a very large extent. Though the volume of big data is skyrocketing, the focus remains on the processing of the data, all thanks to these efficient frameworks. And, Data Science once again hogs the limelight.Can we say it is only the users leading to huge amounts of data? No, we cannot. It is not only humans generating the data but also the work they indulge in.Delving into the iota of the Internet of Things (IoT) will get us some clarity on the question that we just raised. As we have more objects and devices connected to the Internet, data gathers not just by use but also by the pattern of your usage and the performance of the various products.The Three Vs of Big DataData Science helps in the extraction of knowledge from the accumulated data. While big data has come far with the accumulation of users’ data, its usefulness is only just beginning.Following are the Three Properties that define Big Data:VolumeVelocityVarietyVolumeThe amount of data is a crucial factor here. Big data stands as a pillar when you have to process a multitude of low-density, unstructured data. The data may contain unknown value – such as clickstreams on a webpage or a mobile app and Twitter data feeds. The values of the data may differ from user to user. For some, the value might be in tens of terabytes of data. For others, the value might be in hundreds of petabytes.Consider the different social media platforms – Facebook records 2 billion users, YouTube has 1 billion users, 350 million users for Twitter and a whopping 700 million users on Instagram. There is exchange of billions of images, posts and tweets on these platforms. Imagine the amuck storage of data the users contribute too. Mind Boggling, is it not? This insanely large amount of data is generated every minute and every hour.VelocityThe fast rate at which the data is received and acted upon is the Velocity. Usually, the data is written to the disk. When there is data with highest velocity, it streams directly into the memory. With the advancement in technology, we now have more numbers of Internet-connected devices across industries. The velocity of the data generated through these devices that act real time or near real time may call for real-time evaluation and action.Sticking to our social media example, Facebook accounts for 900 million photo uploads, Twitter handles 500 million tweets, Google is to go to solution for 3.5 billion searches, YouTube calls for 0.4 millions hours of video uploads; all this on a daily basis. The bundled amount of data is stifling.VarietyThe data generated by the users comes in different types. The different types form different varieties of data. Dating back, we had traditional data types that were structured and organized in a relational database.Texts, tweets, videos, photos uploaded form the different varieties of structured data uploaded on the Internet.Voicemails, emails, ECG reading, audio recordings and a lot more form the different varieties of unstructured data that we find on the Internet.Who is a Data Scientist? A curious brain and an impressive training is all that you need to become a Data Scientist. Not as easy as it may sound.Deep thinking, deep learning with intense intellectual curiosity is a common trait found in data scientists. The more you ask questions, the more discoveries you come up with, the more augmented your learning experience is, the more it gets easier for you to tread on the path of Data Science.A factor that differentiates a data scientist from a normal bread earner is that they are more obsessed with creativity and ingenuity. A normal bread earner will go seeking money whereas, the motivator for a data scientist is the ability to solve analytical problems with a pinch of curiosity and creativity. Data scientists are always on a treasure hunt – hunting for the best from the trove.If you think, you need a degree in Sciences or you need to be a PhD in Math to become a legitimate data scientist, mind you, you are carrying a misconception. A natural propensity in these areas will definitely add to your profile but you can be an expert data scientist without a degree in these areas too. Data Science becomes a cinch with heaps of knowledge in programming and business acumen.Data Science is a discipline gaining colossal prominence of late. Educational institutions are yet to come up with comprehensive Data Science degree programs. A data scientist can never claim to have undergone all the required schooling. Learning the rights skills, guided by self-determination is a never-ending process for a data scientist.As Data Science is multidisciplinary, many people find it confusing to differentiate between Data Scientist and Data Analyst.Data Analytics is one of the components of Data Science. Analytics help in understanding the data structure of an organization. The achieved output is further used to solve problems and ring in business insights.The Basic Differences between a Data Scientist and a Data AnalystScientists and Analysts are not exactly synonymous. The roles are not mutually exclusive either. The roles of Data Scientists and Data Analysts differ a lot. Let us take a look at some of the basic differences:CriteriaData ScientistData AnalystGoalInquisitive nature and a strong business acumen helps Data Scientists to arrive at solutionsThey perform data analysis and sourcingTasksData Scientists need to be adept at data insight mining, preparation, and analysis to extract informationData Analysts gather, arrange, process and model both structured and unstructured dataSubstantive expertiseRequiredNot RequiredNon-technical skillsRequiredNot RequiredWhat Skills Are Required To Become a Data Scientist?Data scientists blend with the best skills. The fundamental skills required to become a Data Scientist are as follows:Proficiency in MathematicsTechnology knowhow and the knack to hackBusiness AcumenProficiency in MathematicsA Data Scientist needs to be equipped with a quantitative lens. You can be a Data Scientist if you have the ability to view the data quantitatively.Before a data product is finally built, it calls for a tremendous amount of data insight mining. There are portions of data that include textures, dimensions and correlations. To be able to find solutions to come with an end product, a mathematical perspective always helps.If you have that knack for Math, finding solutions utilizing data becomes a cakewalk laden with heuristics and quantitative techniques. The path to finding solutions to major business problems is a tedious one. It involves the building of analytical models. Data Scientists need to identify the underlying nuts and bolts to successfully build models.Data Science carries with it a misconception that it is all about statistics. Statistics is crucial; however, only the Math type is more accountable. Statistics has two offshoots – the classical and the Bayesian. When people talk about stats, they are usually referring to classical stats. Data Scientists need to refer both types to arrive at solutions. Moreover, there is a mix of inferential techniques and machine learning algorithms; this mix leans on the knowledge of linear algebra. There are popular methods in Data Science; finding a solution using these methods calls upon matrix math which has got very less to do with classical stats.Technology knowhow and the knack to hackOn a lighter note, let us put a disclaimer… you are not being asked to learn hacking to come crashing on computers. As a hacker, you need to be gelled with the amalgam of creativity and ingenuity. You are expected to use the right technical skills to build models and thereby find solutions to complex analytical problems.Why does the world of Data Science vouch on your hacking ability? The answer finds its element in the use of technology by Data Scientists. Mindset, training and the right technology when put together can squeeze out the best from mammoth data sets. Solving complex algorithms requires more sophisticated tools than just Excel. Data scientists need to have the nitty-gritty ability to code. They should be able to prototype quick solutions, as well as integrate with complex data systems. SQL, Python, R, and SAS are the core languages associated with Data Science. A knowhow of Java, Scala, Julia, and other languages also helps. However, the knowledge of language fundamentals does not suffice the quest to extract the best from enormous data sets. A hacker needs to be creative to sail through technical waters and make the codes reach the shore.Business AcumenA strong business acumen is a must-have in the portfolio of any Data Scientist. You need to make tactical moves and fetch that from the data, which no one else can. To be able to translate your observation and make it a shared knowledge calls for a lot of responsibility that can face no fallacy.With the right business acumen, a Data Scientist finds it easy to present a story or the narration of a problem or a solution.To be able to put your ideas and the solutions you arrive at across the table, you need to have business acumen along with the prowess for tech and algorithms.Data, Math, and tech will not help always. You need to have a strong business influence that can further be influenced by a strong business acumen.Companies Using Data ScienceTo address the issues associated with the management of complex and expanding work environments, IT organizations make use of data to identify new value sources. The identification helps them exploit future opportunities and to further expand their operations. What makes the difference here is the knowledge you extract from the repository of data. The biggest and the best companies use analytics to efficiently come up with the best business models.Following are a few top companies that use Data Science to expand their services and increase their productivity.GoogleAmazonProcter & GambleNetflixGoogle Google has always topped the list on a hiring spree for top-notch data scientists. A force of data scientists, artificial intelligence and machine learning by far drives Google. Moreover, when you are here, you get the best when you give the best of your data expertise.AmazonAmazon, the global e-commerce and cloud computing giant hire data scientists on a big scale. To bank upon the customers’ mindsets, enhance the geographical outreach of both the cloud domain and e-commerce domain among other business-driven goals, they make use of Data Science. Data Scientists play a crucial role in steering Data Science.Procter & Gamble and NetflixBig Data is a major component of Data Science.It has answers to a range of business problems – from customer experience to analytics.Netflix and Procter & Gamble join the race of product development by using big data to anticipate customer demand. They make use of predictive analytics, an offshoot of Data Science to build models for services in their pipeline. This modelling is an attribute that contributes to their commercial success. The significant addition to the commercial success of P&G is that it uses data and analytics from test markets, social media, and early store rollouts. Following this strategy, it further plans, produces, and launches the final products. And, the finale often garners an overwhelming response for them.The Final Component of the Big Data StoryWhen speed multiplied with storage capabilities, thus evolved the final component of the Big Data story – the generation and collection of the data. If we still had massive room-sized calculators working as computers, we may not have come across the humongous amount of data that we see today. With the advancement in technology, we called upon ubiquitous devices. With the increase in the number of devices, we have more data being generated. We are generating data at our own pace from our own space owing to the devices that we make use of from our comfort zones. Here I tweet, there you post, while a video is being uploaded on some platform by someone from some corner of the room you are seated in.The more you inform people about what you are doing in your life, the more data you end up writing. I am happy and I share a quote on Facebook expressing my feelings; I am contributing to more data. This is how enormous amount of data is generated. The Internet-connected devices that we use support in writing data. Anything that you engage with in this digital world, the websites you browse, the apps you open on your cell phone, all the data pertaining to these can be logged in a database miles away from you.Writing data and storing it is not an arduous task anymore. At times, companies just push the value of the data to the backburner. At some point of time, this data will be fetched and cooked when they see the need for it.There are different ways to cash upon the billions of data points. Data Science puts the data into categories to get a clear picture. On a Final NoteIf you are an organization looking out to expand your horizons, being data-driven will take you miles. The application of an amalgam of Infrastructure, Software and Statistics, and the various data sources is the secret formula to successfully arrive at key business solutions. The future belongs to Data Science. Today, it is data that we see all around us. This new age sounds the bugle for more opportunities in the field of Data Science. Very soon, the world will need around one million Data Scientists.If you are keen on donning the hat of a Data Scientist, be your own architect when it comes to solving analytical problems. You need to be a highly motivated problem solver to overcome the toughest analytical challenges.Master Data Science with our in-depth online courses. Explore them now!
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What Is Data Science(with Examples), It's Lifecycle and Who exactly is a Data Scientist

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What Is Data Science(with Examples), It's Lifecycle and Who exactly is a Data Scientist

Oh yes, Science is everywhere. A while ago, when children embarked on the journey of learning everyday science in school, the statement that always had a mention was “Science is everywhere”. The situation is more or less the same even in present times. Science has now added a few feathers to its cap. Yes, the general masses sing the mantra “Data Science” is everywhere. What does it mean when I say Data Science is everywhere? Let us take a look at the Science of Data. What are those aspects that make this Science unique from everyday Science?

The Big Data Age as you may call it has in it Data as the object of study.

  • Data Science for a person who has set up a firm could be a money spinner
  • Data Science for an architect working at an IT consulting company could be a bread earner
  • Data Science could be the knack behind the answers that come out from the juggler’s hat
  • Data Science could be a machine imported from the future, which deals with the Math and Statistics involved in your life

Data science is a platter full of data inference, algorithm development, and technology. This helps the users find recipes to solve analytically complex problems.

With data as the core, we have raw information that streams in and is stored in enterprise data warehouses acting as the condiments to your complex problems. To extract the best from the data generated, Data Science calls upon Data Mining. At the end of the tunnel, Data Science is about unleashing different ways to use data and generate value for various organizations.

Let us dig deeper into the tunnel and see how various domains make use of Data Science.

Example 1

Think of a day without Data Science, Google would not have generated results the way it does today.

Think of a day without Data Science, Google would not have generated results the way it does today.

Example 2

Suppose you manage an eatery that churns out the best for different taste buds. To model a product in the pipeline, you are keen on knowing what the requirements of your customers are. Now, you know they like more cheese on the pizza than jalapeno toppings. That is the existing data that you have along with their browsing history, purchase history, age and income. Now, add more variety to this existing data. With the vast amount of data that is generated, your strategies to bank upon the customers’ requirements can be more effective. One customer will recommend your product to another outside the circle; this will further bring more business to the organization.

Consider this image to understand how an analysis of the customers’ requirements helps: Analysis of the customers

Example 3

Data Science plays its role in predictive analytics too.

I have an organization that is into building devices that will send a trigger if a natural calamity is soon to occur. Data from ships, aircraft, and satellites can be accumulated and analyzed to build models that will not only help with weather forecasting but also predict the occurrence of natural calamities. The model device that I build will send triggers and save lives too.

Consider the image shown below to understand how predictive analytics works: predictive analytics

Example 4

A lot many of us who are active on social media would have come across this situation while posting images that show you indulging in all fun and frolic with your friends. You might miss tagging your friends in the images you post but the tag suggestion feature available on most platforms will remind you of the tagging that is pending.

The automatic tag suggestion feature uses the face recognition algorithm.

The automatic tag suggestion feature uses the face recognition algorithm.

Lifecycle of Data Science

Capsulizing the main phases of the Data Science Lifecycle will help us understand how the Data Science process works. The various phases in the Data Science Lifecycle are:

  • Discovery
  • Data Preparation
  • Model Planning
  • Model Building
  • Operationalizing
  • Communicating Results

Lifecycle of Data Science

Phase 1

Discovery marks the first phase of the lifecycle. When you set sail with your new endeavor,it is important to catch hold of the various requirements and priorities. The ideation involved in this phase needs to have all the specifications along with an outline of the required budget. You need to have an inquisitive mind to make the assessments – in terms of resources, if you have the required manpower, technology, infrastructure and above all time to support your project. In this phase, you need to have a business problem laid out and build an initial hypotheses (IH) to test your plan. 

Phase 2

Data preparation is done in this phase. An analytical sandbox is used in this to perform analytics for the entire duration of the project. While you explore, preprocess and condition data, modeling follows suit. To get the data into the sandbox, you will perform ETLT (extract, transform, load and transform).

We make use of R for data cleaning, transformation, and visualization and further spot the outliers and establish a relationship between the variables. Once the data is prepared after cleaning, you can play your cards with exploratory analytics.


Phase 3

In this phase of Model planning, you determine the methods and techniques to pick on the relationships between variables. These relationships set the base for the algorithms that will be implemented in the next phase.  Exploratory Data Analytics (EDA) is applied in this phase using various statistical formulas and visualization tools.

Subsequently, we will look into the various models that are required to work out with the Data Science process.

R

R is the most commonly used tool. The tool comes with a complete set of modeling capabilities. This proves a good environment for building interpretive models.

SQL Analysis Services 

SQL Analysis services has the ability to perform in-database analytics using basic predictive models and common data mining functions.

SAS/ACCESS  

SAS/ACCESS helps you access data from Hadoop. This can be used for creating repeatable and reusable model flow diagrams.

You have now got an overview of the nature of your data and have zeroed in on the algorithms to be used. In the next stage, the algorithm is applied to further build up a model.

Phase 4

This is the Model building phase as you may call it. Here, you will develop datasets for training and testing purposes. You need to understand whether your existing tools will suffice for running the models that you build or if a more robust environment (like fast and parallel processing) is required. 

The various tools for model building are SAS Enterprise Miner, WEKA, SPCS Modeler, Matlab, Alpine Miner and Statistica.

Phase 5

In the Operationalize phase, you deliver final reports, briefings, code and technical documents. Moreover, a pilot project may also be implemented in a real-time production environment on a small scale. This helps users get a clear picture of the performance and other related constraints before full deployment.

Phase 6

The Communicate results phase is the conclusion. Here, we evaluate if you have been able to meet your goal the way you had planned in the initial phase. It is in this phase that the key findings pop their heads out. You communicate to the stakeholders in this phase. This phase brings you the result of your project whether it is a success or a failure.

Why Do We Need Data Science?

Data Science to be precise is an amalgamation of Infrastructure, Software, Statistics and the various data sources.

To really understand big data, it would help us if we bridge back to the historical background. Gartner’s definition circa 2001, which is still the go-to definition says,

Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs.

When we break the definition into simple terms, all that it means is, big data is humongous. This involves the multiplication of complex data sets with the addition of new data sources. When the data sets are in such high volumes, our traditional data processing software fails to manage them. It is just like how you cannot expect your humble typewriter to do the job of a computer. You cannot expect a typewriter to even do the ctrl c + ctrl v job for you. The amount of data that comes with the solutions to all your business problems is massive. To help you with the processing of this data, you have Data Science playing the key role.

The concept of big data itself may sound relatively new; however, the origins of large data sets can be traced back to the 1960s and the '70s. This is when the world of data was just getting started. The world witnessed the set up of the first data centers and the development of the relational database.

Around 2005, Facebook, YouTube, and other online services started gaining immense popularity. The more people indulged in the use of these platforms, the more data they generated. The processing of this data involved a lot of Data Science. The masses had to store the amassed data and analyse it at a later point. As a platform that answers to the storage and analysis of the amassed data, Hadoop was developed. Hadoop is an open-source framework that helps in the storage and analysis of big data sets. And as we say, the rest will follow suit; we had NoSQL gaining popularity during this time.

With the advent of big data, the need for its storage also grew. The storage of data became a major issue for enterprise industries until 2010. We have had Hadoop, Spark and other frameworks mitigating the challenge to a very large extent. Though the volume of big data is skyrocketing, the focus remains on the processing of the data, all thanks to these efficient frameworks. And, Data Science once again hogs the limelight.

Can we say it is only the users leading to huge amounts of data? No, we cannot. It is not only humans generating the data but also the work they indulge in.

Delving into the iota of the Internet of Things (IoT) will get us some clarity on the question that we just raised. As we have more objects and devices connected to the Internet, data gathers not just by use but also by the pattern of your usage and the performance of the various products.

The Three Vs of Big Data

Data Science helps in the extraction of knowledge from the accumulated data. While big data has come far with the accumulation of users’ data, its usefulness is only just beginning.

Following are the Three Properties that define Big Data:

  • Volume
  • Velocity
  • Variety

Volume

The amount of data is a crucial factor here. Big data stands as a pillar when you have to process a multitude of low-density, unstructured data. The data may contain unknown value – such as clickstreams on a webpage or a mobile app and Twitter data feeds. The values of the data may differ from user to user. For some, the value might be in tens of terabytes of data. For others, the value might be in hundreds of petabytes.

Consider the different social media platforms – Facebook records 2 billion users, YouTube has 1 billion users, 350 million users for Twitter and a whopping 700 million users on Instagram. There is exchange of billions of images, posts and tweets on these platforms. Imagine the amuck storage of data the users contribute too. Mind Boggling, is it not? This insanely large amount of data is generated every minute and every hour.

Velocity

The fast rate at which the data is received and acted upon is the Velocity. Usually, the data is written to the disk. When there is data with highest velocity, it streams directly into the memory. With the advancement in technology, we now have more numbers of Internet-connected devices across industries. The velocity of the data generated through these devices that act real time or near real time may call for real-time evaluation and action.

Sticking to our social media example, Facebook accounts for 900 million photo uploads, Twitter handles 500 million tweets, Google is to go to solution for 3.5 billion searches, YouTube calls for 0.4 millions hours of video uploads; all this on a daily basis. The bundled amount of data is stifling.

Variety

The data generated by the users comes in different types. The different types form different varieties of data. Dating back, we had traditional data types that were structured and organized in a relational database.

Texts, tweets, videos, photos uploaded form the different varieties of structured data uploaded on the Internet.

Voicemails, emails, ECG reading, audio recordings and a lot more form the different varieties of unstructured data that we find on the Internet.

Volume, Velocity and Variety of Data in Data Science

Who is a Data Scientist? 

A curious brain and an impressive training is all that you need to become a Data Scientist. Not as easy as it may sound.

Deep thinking, deep learning with intense intellectual curiosity is a common trait found in data scientists. The more you ask questions, the more discoveries you come up with, the more augmented your learning experience is, the more it gets easier for you to tread on the path of Data Science.

A factor that differentiates a data scientist from a normal bread earner is that they are more obsessed with creativity and ingenuity. A normal bread earner will go seeking money whereas, the motivator for a data scientist is the ability to solve analytical problems with a pinch of curiosity and creativity. Data scientists are always on a treasure hunt – hunting for the best from the trove.

If you think, you need a degree in Sciences or you need to be a PhD in Math to become a legitimate data scientist, mind you, you are carrying a misconception. A natural propensity in these areas will definitely add to your profile but you can be an expert data scientist without a degree in these areas too. Data Science becomes a cinch with heaps of knowledge in programming and business acumen.

Data Science is a discipline gaining colossal prominence of late. Educational institutions are yet to come up with comprehensive Data Science degree programs. A data scientist can never claim to have undergone all the required schooling. Learning the rights skills, guided by self-determination is a never-ending process for a data scientist.

As Data Science is multidisciplinary, many people find it confusing to differentiate between Data Scientist and Data Analyst.

Data Analytics is one of the components of Data Science. Analytics help in understanding the data structure of an organization. The achieved output is further used to solve problems and ring in business insights.

The Basic Differences between a Data Scientist and a Data Analyst

Scientists and Analysts are not exactly synonymous. The roles are not mutually exclusive either. The roles of Data Scientists and Data Analysts differ a lot. Let us take a look at some of the basic differences:

CriteriaData ScientistData Analyst
GoalInquisitive nature and a strong business acumen helps Data Scientists to arrive at solutionsThey perform data analysis and sourcing
TasksData Scientists need to be adept at data insight mining, preparation, and analysis to extract informationData Analysts gather, arrange, process and model both structured and unstructured data
Substantive expertiseRequiredNot Required
Non-technical skillsRequiredNot Required

What Skills Are Required To Become a Data Scientist?

Data scientists blend with the best skills. The fundamental skills required to become a Data Scientist are as follows:

  • Proficiency in Mathematics
  • Technology knowhow and the knack to hack
  • Business Acumen

Proficiency in Mathematics

A Data Scientist needs to be equipped with a quantitative lens. You can be a Data Scientist if you have the ability to view the data quantitatively.

Before a data product is finally built, it calls for a tremendous amount of data insight mining. There are portions of data that include textures, dimensions and correlations. To be able to find solutions to come with an end product, a mathematical perspective always helps.

If you have that knack for Math, finding solutions utilizing data becomes a cakewalk laden with heuristics and quantitative techniques. The path to finding solutions to major business problems is a tedious one. It involves the building of analytical models. Data Scientists need to identify the underlying nuts and bolts to successfully build models.

Data Science carries with it a misconception that it is all about statistics. Statistics is crucial; however, only the Math type is more accountable. Statistics has two offshoots – the classical and the Bayesian. When people talk about stats, they are usually referring to classical stats. Data Scientists need to refer both types to arrive at solutions. Moreover, there is a mix of inferential techniques and machine learning algorithms; this mix leans on the knowledge of linear algebra. There are popular methods in Data Science; finding a solution using these methods calls upon matrix math which has got very less to do with classical stats.

Technology knowhow and the knack to hack

On a lighter note, let us put a disclaimer… you are not being asked to learn hacking to come crashing on computers. As a hacker, you need to be gelled with the amalgam of creativity and ingenuity. You are expected to use the right technical skills to build models and thereby find solutions to complex analytical problems.

Why does the world of Data Science vouch on your hacking ability? The answer finds its element in the use of technology by Data Scientists. Mindset, training and the right technology when put together can squeeze out the best from mammoth data sets. Solving complex algorithms requires more sophisticated tools than just Excel. Data scientists need to have the nitty-gritty ability to code. They should be able to prototype quick solutions, as well as integrate with complex data systems. SQL, Python, R, and SAS are the core languages associated with Data Science. A knowhow of Java, Scala, Julia, and other languages also helps. However, the knowledge of language fundamentals does not suffice the quest to extract the best from enormous data sets. A hacker needs to be creative to sail through technical waters and make the codes reach the shore.

Business Acumen

A strong business acumen is a must-have in the portfolio of any Data Scientist. You need to make tactical moves and fetch that from the data, which no one else can. To be able to translate your observation and make it a shared knowledge calls for a lot of responsibility that can face no fallacy.

With the right business acumen, a Data Scientist finds it easy to present a story or the narration of a problem or a solution.

To be able to put your ideas and the solutions you arrive at across the table, you need to have business acumen along with the prowess for tech and algorithms.

Data, Math, and tech will not help always. You need to have a strong business influence that can further be influenced by a strong business acumen.

Companies Using Data Science

To address the issues associated with the management of complex and expanding work environments, IT organizations make use of data to identify new value sources. The identification helps them exploit future opportunities and to further expand their operations. What makes the difference here is the knowledge you extract from the repository of data. The biggest and the best companies use analytics to efficiently come up with the best business models.

Following are a few top companies that use Data Science to expand their services and increase their productivity.

  • Google
  • Amazon
  • Procter & Gamble
  • Netflix

Google 

Google.comGoogle has always topped the list on a hiring spree for top-notch data scientists. A force of data scientists, artificial intelligence and machine learning by far drives Google. Moreover, when you are here, you get the best when you give the best of your data expertise.

Amazon

Amazon.inAmazon, the global e-commerce and cloud computing giant hire data scientists on a big scale. To bank upon the customers’ mindsets, enhance the geographical outreach of both the cloud domain and e-commerce domain among other business-driven goals, they make use of Data Science. Data Scientists play a crucial role in steering Data Science.

Procter & Gamble and Netflix

P&G, Netflix

Big Data is a major component of Data Science.

It has answers to a range of business problems – from customer experience to analytics.

Netflix and Procter & Gamble join the race of product development by using big data to anticipate customer demand. They make use of predictive analytics, an offshoot of Data Science to build models for services in their pipeline. This modelling is an attribute that contributes to their commercial success. The significant addition to the commercial success of P&G is that it uses data and analytics from test markets, social media, and early store rollouts. Following this strategy, it further plans, produces, and launches the final products. And, the finale often garners an overwhelming response for them.

The Final Component of the Big Data Story

When speed multiplied with storage capabilities, thus evolved the final component of the Big Data story – the generation and collection of the data. If we still had massive room-sized calculators working as computers, we may not have come across the humongous amount of data that we see today. With the advancement in technology, we called upon ubiquitous devices. With the increase in the number of devices, we have more data being generated. We are generating data at our own pace from our own space owing to the devices that we make use of from our comfort zones. Here I tweet, there you post, while a video is being uploaded on some platform by someone from some corner of the room you are seated in.

The more you inform people about what you are doing in your life, the more data you end up writing. I am happy and I share a quote on Facebook expressing my feelings; I am contributing to more data. This is how enormous amount of data is generated. The Internet-connected devices that we use support in writing data. Anything that you engage with in this digital world, the websites you browse, the apps you open on your cell phone, all the data pertaining to these can be logged in a database miles away from you.

Writing data and storing it is not an arduous task anymore. At times, companies just push the value of the data to the backburner. At some point of time, this data will be fetched and cooked when they see the need for it.

There are different ways to cash upon the billions of data points. Data Science puts the data into categories to get a clear picture. 

On a Final Note

If you are an organization looking out to expand your horizons, being data-driven will take you miles. The application of an amalgam of Infrastructure, Software and Statistics, and the various data sources is the secret formula to successfully arrive at key business solutions. The future belongs to Data Science. Today, it is data that we see all around us. This new age sounds the bugle for more opportunities in the field of Data Science. Very soon, the world will need around one million Data Scientists.

If you are keen on donning the hat of a Data Scientist, be your own architect when it comes to solving analytical problems. You need to be a highly motivated problem solver to overcome the toughest analytical challenges.


Master Data Science with our in-depth online courses. Explore them now!

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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1 comments

sudhakar 06 Aug 2019 1 likes

Great Article, Such a fabulous explanation and Really very helpful., All the details included for the beginners, Thank you knowledgehut, keep writing like this type blogs

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Role of Statistics in Data Science

Takeaways from this article In this article, we understand why data is important, and talk about the importance of statistics in data analysis and data science. We also understand some basic statistics concepts and terminologies. We see how statistics and machine learning work in sync to give deep insights into data.  We understand the fundamentals behind Bayesian thinking and how Bayesian theorem works. Introduction Data plays a huge role in today’s tech world. All technologies are data-driven, and humongous amounts of data are produced on a daily basis. A data scientist is a professional who is able to analyse data sources, clean and process the data, understand why and how such data has been generated, take insights from it, and make changes such that they profit the organization. These days, everything revolves around data.  Data Cleaning: It deals with gathering the data and structuring it so that it becomes easy to pass this data as input to any machine learning algorithm. This way, redundant, irrelevant data and noise can also be eliminated.  Data Analysis: This deals with understanding more about the data, why the data has yielded certain results, and what can be done to improve it. It also helps calculate certain numerical values like mean, variance, the distributions, and the probability of a certain prediction.  How the basics of statistics will serve as a foundation to manipulate data in data scienceThe basics of statistics include terminologies, and methods of applying statistics in data science. In order to analyze the data, the important tool is statistics. The concepts involved in statistics help provide insights into the data to perform quantitative analysis on it. In addition to this, as a foundation, the basics and working of linear regression and classification algorithms must also be known to a data science aspirant.  Terminologies associated with statistics Population: It is an entire pool of data from where a statistical sample is extracted. It can be visualized as a complete data set of items that are similar in nature.  Sample: It is a subset of the population, i.e. it is an integral part of the population that has been collected for analysis.  Variable: A value whose characteristics such as quantity can be measured, it can also be addressed as a data point, or a data item.  Distribution: The sample data that is spread over a specific range of values.  Parameter: It is a value that is used to describe the attributes of a complete data set (also known as ‘population’). Example: Average, Percentage  Quantitative analysis: It deals with specific characteristics of data- summarizing some part of data, such as its mean, variance, and so on.  Qualitative analysis: This deals with generic information about the type of data, and how clean or structured it is.  How does analyzing data using statistics help gain deep insights into data? Statistics serve as a foundation while dealing with data and its analysis in data science. There are certain core concepts and basics which need to be thoroughly understood before jumping into advanced algorithms.  Not everyone understand the performance metrics of machine learning algorithms like f-score, recall, precision, accuracy, root mean squared error, and so on. Instead, visual representation of the data and the performance of the algorithm on the data serves as a good metric for the layperson to understand the same.  Also, visual representation helps identify outliers, specific trivial patterns, and certain metric summary such as mean, median, variance, that helps in understanding the middlemost value, and how the outlier affects the rest of the data.  Statistical Data Analysis Statistical data analysis deals with the usage of certain statistical tools that need knowledge of statistics. Software can also help with this, but without understanding why something is happening, it is impossible to get considerable work done in statistics and data science.  Statistics deals with data variables that are either univariate or multivariate. Univariate, as the name suggests deals with single data values, whereas multivariate data deals with the multiple number of values. Discriminant data analysis, factor data analysis can be performed on multivariate data. On the other hand, univariate data analysis, Z-test, F-test can be performed if we are dealing with univariate data.  Data associated with statistics is of many types. Some of them have been discussed below. Categorical data represents characteristics of people, such as marital status, gender, food they like, and so on. It is also known as ‘qualitative data’ or ‘yes/no data’. It takes numerical values like ‘1’, ‘2’, where these numbers indicate one or other type of characteristics. These numbers are not mathematically significant, which means it can’t be associated with each other. Continuous data deals with data that is immeasurable, and can’t be counted, which basically continual forms of values are. Predictions from a linear regression are continuous in nature. It is a continuous distribution that is also known as probability density function. On the other hand, discrete values can be measured, counted, and are discontinuous. Predictions from logistic regression are considered to be discrete in nature. Discrete data is non-continuous, and density concept doesn’t come into the picture here. The distribution is known as probability mass function. The Best way to Learn Statistics for Data Science The best way to learn anything is by implementing it, by working on it, by making mistakes and again learning from it.  It is important to understand the concepts, either by going through standard books or well-known websites, before implementing them.  Before jumping into data science, the core statistics concepts like such as regression, maximum likelihood, distributions, priors, posteriors, conditional probability, Bayesian theorem and basics of machine learning have to be understood clearly. Core statistics concepts Descriptive statistics: As the name suggests, it uses the data to give out more information about every aspect of the data with the help of graphs, plots, or numbers. It organizes the data into a structure, and helps think about the attributes that highlight the important parts of the data. Inferential statistics: It deals with drawing inferences/conclusions on the sample data set which is obtained from the population (entire data set) based on the relationship identified between data points in the data set. It helps in generalizing the relationship to the entire dataset. It is important to remember that the dataset drawn from the population is relevant and represents the population accurately. Regression: The term ‘regression’ which is a part of statistics and machine learning, talks about how data can be fit to a line, and how every point from the straight line gives some insights. In terms of machine learning, it can be understood as tasks that can be solved without explicitly being programmed. They discuss how a line can be fit to a given set of data points, and how it can be further extrapolated for the predictions to be done.  Maximum likelihood: It is a method that helps in finding values of parameters for a specific model. The values of the parameters have to be such that the likelihood of the predictions that occur have to be maximum in comparison to the data values that were actually observed. This means the difference between the actual and predicted value has to be less, thereby reducing the error and increasing the accuracy of the predictions.  Note: This concept is generally used with Logistic regression when we are trying to find the output as 0 or 1, yes or no, wherein the maximum likelihood tells about how likely a data point is near to 0 or 1.  Bayesian thinking Bayesian thinking deals with using probability to model the process of sampling, and being able to quantify the uncertainty associated with the data that would be collected.  This is known as prior probability- which means the level of uncertainty that is associated with the data before it is collected to be analysed.  Posterior probability deals with the uncertainty that occurs after the data has been collected.  Machine learning algorithms are usually focussed on giving the best predictions as output with minimal errors, exact probabilities of specific events occurring and so on. Bayes theorem is a way of calculating the probability of a hypothesis (a situation, which might not have occurred in reality) based on our previous experiences and the knowledge we have gained by it. This is considered as a basic concept that needs to be known.  Bayes theorem can be stated as follows: P(hypo | data) = (P(data | hypo) * P(hypo)) / P(data)In the above equation,   P(hypo | data) is the probability of a hypothesis ‘hypo’ when data ‘data’ is given, which is also known as posterior probability.   P(data | hypo) is the probability of data ‘data’ when the specific hypothesis ‘hypo’ is known to be true.   P(hypo) is the probability of a hypothesis ‘hypo’ being true (irrespective of the data in hand), which is also known as prior probability of ‘hypo’.   P(data) is the probability of the data (irrespective of the hypothesis). The idea here is to get the value of the posterior probability, given other data. The posterior probability for a variety of different hypotheses has to be found out, and the probability that has the highest value is selected. This is known as the maximum probable hypothesis, and is also known as the maximum a posteriori (MAP) hypothesis.MAP(hypo) = max(P(hypo | data))If the value of P(hypo | data) is replaced with the value we saw before, the equation would become:MAP(hypo) = max((P(data | hypo) * P(hypo)) / P(data))P(data) is considered as a normalizing term that helps in determining the probability. This value can be safely ignored when required, since it is a constant value. Naïve Bayes classifier   It is an algorithm that can be used with binary or multi-class classification problems. It is a simple algorithm wherein the probability for every hypothesis is simplified.   This is done in order to make the data more traceable. Instead of calculating value of every attribute like P(data1, data2,..,datan|hypo), we assume that every data point is independent of every other data point in the data set when the respective output is given.   This way, the equation becomes:P(data1 | hypo) * P(data2 |hypo) * … * P(data-n| hypo).This way, the attributes would be independent of each other. This classifier performs quite well even in the real world with real data when the assumption of data points being independent of each other doesn’t hold good.  Once a Naïve Bayes classifier has learnt from the data, it stores a list of probabilities in a data structure. Probabilities such as ‘class probability’ and ‘condition probability’ are stored. Training such a model is quick since the probability of every class and its associated value needs to be determined, and this doesn’t involve any optimization processes or changing of coefficient to give better predictions.   Class probability: It tells about the probability of every class that is present in the training dataset. It can be calculated by finding the frequency of values that belongs to each class divided by the total number of values.  Class probability = (number of classes/(number of classes of group 0 + number of classes of group 1)) Conditional probability: It talks about the conditional probability of every input that is associated with a class value. It can be calculated by finding the frequency of every data attribute in the data for a given class, and this can be determined by the number of data values that have that data label/class value.  Conditional probability P(condition | result ) = number of ((values with that condition and values with that result)/ (number of values with that result)) Not just the concept, once the user understands the way in which a data scientist needs to think, they will be able to focus on getting cleaner data, with better insights that would lead to performing better analysis, which in turn would give great results.  Introduction to Statistical Machine Learning The methods used in statistics are important to train and test the data that is used as input to the machine learning model. Some of these include outlier/anomaly detection, sampling of data, data scaling, variable encoding, dealing with missing values, and so on.  Statistics is also essential to evaluate the model that has been used, i.e. see how well the machine learning model performs on a test dataset, or on data that it has never seen before.  Statistics is essential in selecting the final and appropriate model to deal with that specific data in a predictive modelling situation.  It is also needed to show how well the model has performed, by taking various metrics and showing how the model has fared.  Metrics used in Statistics Most of the data can be fit to a common pattern that is known as Gaussian distribution or normal distribution. It is a bell-shaped curve that can be used to summarize the data with the below mentioned two parameters:  Mean: It is understood as the central most value when the data points are arranged in a descending or ascending order, or the most likely value.Mode: It can be understood as the data point that occurs the greatest number of times, i.e. The frequency of the value in the dataset would be very high.  Median: It is a measure of central tendency of the data set. It is the middle number, that can be found by sorting all the data points in a dataset and picking the middle-most element. If the number of data points in a dataset is odd, one single middle value is picked up, whereas two middle values are picked and their mean is calculated if the number of data points in a dataset is even. Range: It refers to the value that is calculated by finding the difference between the largest and the smallest value in a dataset. Quartile: As the name suggests, quartiles are values that divide the data points in a dataset into quarters. It is calculated by sorting the elements in order and then dividing the dataset into 4 equal parts. Three quartiles are identified: The first quartile that is the 25th percentile, the second quartile which is the 50th percentile and the third quartile that is the 75th percentile. Each of these quartiles tells about the percentage of data that is smaller or larger in comparison to other percentiles of data. Example: 25th percentile suggests that 25 percent of the data set is smaller than the remaining 75 percent of the data set. Quartile helps understand how the data is distributed around the median (which is the 50th percentile/second quartile). There are other distributions as well, and it depends on the type of data we have and the insights we need from that data, but Gaussian is considered as one of the basic distributions. Variance: The average of the difference between every value and the mean of that specific distribution.  Standard deviation: It can be understood as the measure that indicates the dispersion that occurs in the data points of the input data.  Conclusion In this post, we understood why and how statistics is important to understand and work with data science. We saw a few terminologies of statistics that are essential in understanding the insights which statistics would end up giving to data scientist. We also saw a few basic algorithms that every data scientist needs to know, in order to learn other advanced algorithms.  
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Role of Statistics in Data Science

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Getting Started With Machine Learning With Python: Step by Step Guide

Takeaways from the article This article helps you understand the cases wherein Machine learning can be used, and where it is relevant (and where it is not). It discusses the basic steps involved in a machine learning problem, along with code in Python. It discusses how the data involved in a Machine Learning problem can be visualized using certain Python packages.  Introduction  Machine Learning has remained a hot topic since many years. Many know how to make sense of it, and where it can actually be used. It is not a universal solution to all the challenging problems out there (that are difficult to be solved) in the universe. It can only be used when certain conditions are satisfied. Only then does a problem qualify to be solved using a Machine Learning algorithm. In general, Python is the most preferred language to work with algorithms that involve Machine Learning.  Introduction to Machine Learning Machine Learning, also known as ML in short, is a sub-topic that falls under Artificial Intelligence (AI), to achieve specific goals. ML is the art of understanding or designing an algorithm that can be used to process large or small amounts of data. This algorithm will not explicitly define or set the rules for the machine to learn from the data. The machine learns from the data on its own. There are no ‘if’ or ‘else’ statements to guide the machine.    This is very much similar to how humans learn from their experiences in day-to-day life, how a child learns to ride a bike, how a child learns to read letters, then words, then sentences, and conversations.  Getting started with Machine learning in Python Python has been used to implement machine learning algorithms, since it is open-source, extremely popular and has gained immense support from the community as well. In addition to this, there are loads of packages in Python, and they support usage of machine learning algorithms for a variety of version of Python application.  These algorithms can be implemented in python by calling simple functions and these functions are placed inside classes. In turn, these classes are encapsulated in a module as a package.  The ‘scikit-learn’ package for Python is one of the most popular and has most of the machine learning algorithms pre-implemented, and housed inside packages. To implement an algorithm, the package can be imported (or a specific class from the package can be imported) and it can be bound with the variable or the class object using a dot operator and accessed. In general, to begin implementing any machine learning algorithm, the following steps can serve as a blue-print: Define your problem, and confirm that it can be solved using machine learning (so that it is not a trivial “set of rules” related problem) Prepare the data: In this step, the data needed for this model is collected from various resources. Another way is to generate data using the innumerable functions that are present in Python. In either case, the data has to be cleaned, structured, analysed, and the outliers have to be identified. Also, the data has to be pre-processed so that it is easy for the algorithm to build a model based on the data. Certain irrelevant columns maybe removed, and missing data should be handled.  The data needs to be trained and hyperparameters need to be tuned so as to get better prediction accuracy.  Note: It is understood that the users have Python 3.5 or a higher stable version installed on their workstations before beginning to execute the code in the upcoming sections. Other packages can be installed as and when required.  Where Machine Learning can be used?The simplest place is when there is no prediction or complex data insight needed, it need not be used.  Machine Learning algorithm are built by humans to help understand data better, make predictions etc. When we try to solve a problem, there are certain principles that we hold as a foundation (when dealing with physics- gravity, newton’s law) but algorithms don’t. They are stochastic (random) in nature.  Not all problems that have a large amount of data is suited to work with Machine Learning algorithms. It is important to understand the deterministic nature of problems, and try to avoid solving such problems using Machine Learning.  Machine Learning in PythonLet us jump into a simple problem of linear regression using Machine learning, Linear regression is a simple algorithm that predicts the value of a variable, based on certain other values. There are many variations to Linear Regression that includes Multi-variate regression, etc.   Before jumping into the algorithm, let us understand what linear regression means. ‘Linear’ basically means a straight line, and ‘regression’ which is a part of machine learning, talks about how tasks can be solved without explicitly being programmed.   There are various machine learning algorithms, and Linear Regression is just the beginning to it. This includes supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.Why should Machine Learning be used? Certain task needs intricate detailing, and patterns might not be fully unveiled if manual or simple methods are used to extract patterns. Machine learning, on the other hand, will be able to extract all important, hidden patterns, and work well even when the amount of data increases exponentially. It also becomes easy to improve pattern recognition. It will also be possible to deliver results in a time manner, get deeper and better insights into the data in hand.   The results computed using a Machine Learning algorithm would be more accurate in comparison to traditional methods, and the models build can serve as a foundation for other data as well. There are different classifications in machine learning, depending on various types. The 4 basic classifications are:Supervised learning algorithms Semi-supervised learning algorithms Unsupervised learning algorithms  Reinforcement learning algorithmsMachine learning algorithms can also be classified based on how they learn- on the fly or incrementally, into 2 types:Online learning Batch learningMachine learning algorithms can also be classified based on how they detect patterns- whether they detect patterns in data or compare new data values with previously seen data values:Model-based learning  Instance-based learning Supervised LearningMost popular Easy to understand Easier to implement Gives decent results Expensive, since human intervention is requiredSupervised learning involves human supervision. In real-time, supervision is present in the form of labelled features, feedback loop to the data (insights on whether the machine predicted correctly, and if not, what the correct prediction has to be) and so on.  Once the algorithm is trained on such data, it can predict good outputs with a high accuracy for never-before-seen inputs. Applications of supervised learning:Spam classification: Classifying emails as spam or important.  Face recognition: Detecting faces, mapping them to a specific face in a database of faces. Supervised algorithms can further be classified into two types:Classification algorithms: They classify the given data into one of the given classes or group of data. This basically deals with data grouping/data mapping into specific classes.   Regression algorithms: This deals with fitting the data to a given model, predicting continuous or discrete values.   Semi-supervised LearningIn between the supervised and unsupervised learning algorithms.   Created to bridge the gap between dealing with fully structured and fully unstructured data.   Comes between supervised and unsupervised algorithms.   Input is a combination of unlabelled (more) and labelled (less) data.Applications of semi-supervised learning algorithms:Speech analysis, sentiment analysis Content classificationUnsupervised LearningNo data labelling No human intervention May not be very accurate Can’t be applied to a broad variety of situations Algorithm has to figure out how and what to learn from the data Similar to real-world unstructured data Can’t be applied to a broad variety of situationsApplications of unsupervised learning:Clustering Anomaly detectionUnsupervised data can be classified into two categories:Clustering algorithms Association algorithmsReinforcement LearningIt is a ‘punish and reward’ mechanism. Learns from surrounding and experience. An agent decides the next relevant step to arrive at the desired result.   If algorithm learns correctly, then it is rewarded indicating that it is on the right path. If the algorithm made a mistake, it is punished to indicate the mistake and to learn from it.Supervised learning algorithm is different from reinforcement, since the former has a comparable value, whereas the latter has to decide the next action and take it and bear the result and learn from it.Applications of reinforcement learning:Robotics in automation   Machine learning and data processingOther types of learning algorithmsOnline learning Batch learning: It has two different categories: Model-based learning, and instance-based learningOnline LearningAlso known as incremental/out of the core learning. Assumption is that the learning environment changes constantly.Machine learning models that are trained consistently and constantly on new data to predict output. On the other hand, during this period, the model is getting trained on new data in real time. Whenever the model sees a new example, it quickly has to learn from it and adapt to it. This way, even the newly learnt example will be a part of the trained model, and will be a part of giving the prediction/output.Batch LearningThis is also known as data learning in a group.  Data is grouped/classified into different batches.  There batches are used to extract different patterns since every batch would be considerably different from the other one. These patterns are learned by the model in time.  Model-based learningThe specifications associated with a problem in a domain is converted into a model-format. When this model sees new data, it detects patterns from it, and these patterns are used to make predictions on the newly seen data.   Instance-based learningIt is the simplest form of clustering and regression algorithms.They either result in grouping the algorithm into different classes (due to classification) or give continuous or discrete values as output (due to linear or logistic regression).Classification and regression is based on how similar or different the queries are, with respect to the values in the data.Linear RegressionIn this algorithm, we will understand the problems with two different variables in hand- one is an independent variable, and the other one- a dependant variable. We will take a basic problem of finding prices of a house when its area is given. Assume that we have the below dataset:Price of house (independent value)Area of the house (dependant value)356500 sq m5781000 sq m8901500 sq m13002000 sq m18002500 sq m?3000 sq mWhen the above data is given, and the price of house is asked to be found (see last row), given the area of the house, simple linear regression (that gives a decent amount of accuracy) can be used. Below is how the data will look when plotted on a graph. It yields an almost straight line, which means the dependant value depends on the independent value, i.e the area of the house matters when the price of the house is being fixed.The basic steps involved in a machine learning problem-  Identify the problem: see if it qualifies to be solved using a Machine Learning algorithm.  Gather the data: The data required can either be collected from a single source or various source, or it could be generated randomly (if it is for a specific purpose) using certain formulas and methods.  Data cleaning: The data gathered may not be clean or structured, make sure it is cleaned, and in a structured or at least semi-structured format.  Package installation: Install the packages that are required to work with the data.  Data loading: Load the data into the Python environment using any IDE (Usually, Spyder is preferred). This is done so that the machine learning algorithm can access the data and perform the operations.  Data cleaning: Data can be cleaned after it has been placed in the Python environment using certain packages and methods, or it can be cleaned before (manually or by applying some logic).  Summarize the data: Understand the terms we are looking at, perform some operations on them, get the type of value, mean, median, variance, and standard deviation, which are insights into the data. This can be done easily by importing packages that have these functions. Data training: In this step, the input dataset is trained by passing it as parameter to the respective algorithm. This is done so that it can predict the output for the not-ever-seen data also known as testing dataset.  Linear Regression application: Apply the Linear Regression algorithm to this data. Data visualization: The data that has interacted with the linear regression algorithm is visualized using many Python packages. Prediction: The predictions are made with the help of the data trained, and are then displayed on the console. Code for Linear Regression using Python Code to implement linear regression using Python  import numpy as np  import matplotlib.pyplot as plt  from sklearn.metrics import mean_squared_error, r2_score  from sklearn.linear_model import LinearRegression    #Random data set generated  np.random.seed(0)  x_dep = np.random.rand(100, 1)  y_indep = 5.89 + (2.45)* x_dep + np.random.rand(100, 1)    #The model is initialized using LinearRegression that is present in the scikit-learn package  model_of_regression = LinearRegression()    #The data is fit on the model, with the help of training  model_of_regression.fit(x_dep, y_indep)    #The output is predicted   predicted_y_val = model_of_regression.predict(x_dep)    #The model built is evaluated using mean squared error parameter  rmse = mean_squared_error(y_indep, predicted_y_val)    r2 = r2_score(y_indep, predicted_y_val)    print("The value of slope is: ", model_of_regression.coef_)  print("The intercept value is: ", model_of_regression.intercept_)  print("The Root Mean Squared Error value (RMSE) is: ", rmse)    #The data is visualized usign the matplotlib library  plt.scatter(x_dep, y_indep, s=8)  plt.xlabel('X-axis')  plt.ylabel('Y-axis')    #The values are predicted and plotted on a graph and displayed on the screen  plt.plot(x_dep, predicted_y_val, color='r')  plt.show() Output:Code review-Explanation of every step  The required packages are imported using the ‘import’ keyword.  Make sure that ‘scikit-learn’ package is installed before working on this code.  Instead of using precooked data, we are generating data here, using the ‘random’ function.  A seed is defined, and a formula is created that assumes random values for variables and generates random data.  The ‘LinearRegression’ function, present in the ‘scikit-learn’ package is initiated so as to create a model, and one of the functions inside the LinearRegression package-namely ‘fit’ is called by passing the dependant and the independent values.  The ‘predict’ function from the LinearRegression is used to predict the value that is not known for a given independent value. After the model is built with the data, it is important to see how it has fared.  Hence, an attribute named RMSE (Root Mean Squared Error) is used to see the difference between the value that had to actually be predicted and the value that was predicted.  Next, the data is visualized on the screen using a package named ‘matplotlib’.  Conclusion In all, Machine Learning is a game changer when it comes to identifying its use cases, and applying the right kind of algorithm in the right place, with the right amount of data, and right computational resources and power. Linear Regression is just a simple algorithm of where Machine Learning begins to show its aspects. Usually, the Python language is used to implement Machine Learning algorithms, but other new languages could also be used.  
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Types of Classification in Machine Learning

Takeaways from this article In this post, we understand the concept of classification, regression, classification predictive modelling, and the different types of classification and regression.  We understand why and how classification is important. We also see a few classification algorithms and their implementations in Python.  We understand logistic regression, decision trees, random forests, support vector machines, k nearest neighbour and neural networks. We understand their inner workings and their prominence. IntroductionClassification refers to the process of classifying the given data set into different classes or groups. The classification algorithm is placed under predictive modelling problem, wherein every class of the dataset is given a label, to indicate that it is different from other classes. Some examples include email classification as spam or not, recognition of a handwritten character as a specific character only, and not another character and so on.   Classification algorithms need data to be trained with many inputs and their respective output, with the help of which the model learns. It is important to understand that the training data must encompass all kinds of data (options) which could be encountered in the test data set or real world. ClassificationThe 4 different prominent types of classification include the following:Binary classification Multi-class classification Multi-label classification Imbalanced classification  Binary classificationAs the name suggests, it deals with the tasks in classification that only have two class labels. Some examples include: email classification as spam or not, whether the price of a stock will go up or go down (ignoring the fact that it could also remain as is), and so on. The value obtained after classifying the data would be either 0 or 1, yes or no, normal or abnormal.  The Bernoulli probability distribution is used as prediction to classify the data as 0 or 1. Bernoulli distribution is a discrete (discontinuous) distribution that gives a binary outcome -- a 0 or a 1. Algorithms that are used to perform binary classification include the following:Logistic regression Decision trees Support vector machine Naïve Bayes ‘k’nn (k nearest neighbors) Code to demonstrate a binary classification task:  from numpy import where  from collections import Counter  from sklearn.datasets import make_blobs  from matplotlib import pyplot  X, y = make_blobs(n_samples=560, centers=2, random_state=1)  print("Data has been generated ")  print("The number of rows and columns are ")  print(X.shape, y.shape)  my_counter = Counter(y)  print(my_counter)  for i in range(10):  print(X[i], y[i])  for my_label, _ in my_counter.items():  row_ix = where(y == my_label)[0]  pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))  pyplot.legend()  pyplot.show()Output: Data has been generated   The number of rows and columns are   (560, 2) (560,)  Counter({1: 280, 0: 280})  [-9.64384208 -4.14030356] 1  [-0.8821407  4.2877187] 0  … Code explanation The required packages are imported using the ‘import’ function.  The dataset is generated using the ‘make_blobs’ function and by specifying the number of rows and columns that need to be generated.  In addition, the number of classes into which the data points need to be labelled into is also defined. Here, it is 2. The number of rows and columns are displayed along with the summarization of class labelling.  A ‘for’ loop is used to print the first few classified values.  The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  Multi-class classificationIt is a type of classification wherein the input data set is classified/labelled into more than 2 classes. Some examples of multi-class classification include:Animal species classification Facial recognition/classification Text translation (special type of multi-class classification task) This is different from binary classification in that it doesn’t have just two classes like 0 or 1, but more, and they need not be 0 or 1. They could be names or other continuous or discontinuous numbers. The data points are classified into one among many different classes given.  The number of class labels may be too high, when trying to classify a given photo into that of a specific person. Text translation also deals with a similar issue, wherein the word placement may vary widely and there maybe thousands of combinations of the same number of words. Multinoulli probability distribution is a discrete/discontinuous probability distribution, where the output could be any value within a given range. Algorithms that are used for binary classification can also be used for multi-class classification.  Code to demonstrate the multi-class classification: from numpy import where  from collections import Counter  from sklearn.datasets import make_blobs  from matplotlib import pyplot    X, y = make_blobs(n_samples=670, centers=5, random_state=1)  print("The dataset has been generated")  print("The rows and columns are ")  print(X.shape, y.shape)  my_counter = Counter(y)  print(my_counter)  for i in range(10):  print(X[i], y[i])  for my_label, _ in my_counter.items():  row_ix = where(y == my_label)[0]  pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))  pyplot.legend()  pyplot.show() Output:  The dataset has been generated  The rows and columns are   (670, 2) (670,)  Counter({3: 134, 0: 134, 2: 134, 4: 134, 1: 134})  [-6.45785776 -3.30981436] 3  [-6.44623696 -2.90184841] 3  [-5.60217602 -0.65990849] 3 Code explanation: The required packages are imported using the ‘import’ function.  The dataset is generated using the ‘make_blobs’ function and by specifying the number of rows and columns that need to be generated.  In addition, the number of classes into which the data points need to be labelled into is also defined. Here, it is 5.  The number of rows and columns are displayed along with the summarization of class labelling.  A ‘for’ loop is used to print the first few classified values.  The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen. Multi-label classification   Multi-label classification refers to those classification problems that deal with more than one class being assigned to a single data point, i.e. every data point would belong or be labelled into more than one class/label. A simple example would be a photo that contains multiple people, not just one. This means one photo might be classified or labelled as more than one (in fact thousands) of persons. This is different from binary and multi-class classification, since the number of labels into which one data point is classified remains same, i.e one.Some multi-label classification algorithms include: Multi-label random forests Multi-label gradient boosting Code to demonstrate multi-label classification: from sklearn.datasets import make_multilabel_classification  X, y = make_multilabel_classification(n_samples=800, n_features=2, n_classes=5, n_labels=3, random_state=1)  print("The number of rows and columns are ")  print(X.shape, y.shape)  for i in range(8):  print(X[i], y[i]) Output: The number of rows and columns are   (800, 2) (800, 5)  [22. 24.] [1 0 0 1 1]  [12. 35.] [0 1 0 1 0]  [27. 30.] [1 1 0 0 1]  ..  Code explanation The required packages are imported using the ‘import’ function.  The dataset is generated using the ‘make_multilabel_classification’ function present in the scikit-learn package is used.  It is done by specifying the number of rows and columns that need to be generated.  The number of rows and columns are displayed along with the summarization of class labelling.  A ‘for’ loop is used to print the first few classified values.  The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  Imbalanced classification This is a type of classification wherein the number of data points of the dataset in every class is not distributed equally. This means imbalanced classification is basically a binary classification problem, which doesn’t have a uniform distribution of points, one class could contains an extremely large amount of data points, and the other class might contains a very small number of data points.  Examples of imbalanced classification problem include: Fraud detection in credit cards Anomaly detection in the given dataset There are specialized algorithms that are used to classify this data into the large data point group or small data point group. Some algorithms have been listed below: Cost sensitive decision trees Cost sensitive logistic regression Cost sensitive support vector machines Code to demonstrate imbalanced binary classification #An example of imbalanced binary classification task  from numpy import where  from collections import Counter  from sklearn.datasets import make_classification  from matplotlib import pyplot  #The dataset is defined  X, y = make_classification(n_samples=800, n_features=2, n_informative=2, n_redundant=0, n_classes=2, n_clusters_per_class=1, weights=[0.99,0.01], random_state=1)  #The shape of the dataset is summarized  print("The number of rows and columns ")  print(X.shape, y.shape)  #The labelled data is summarized  my_counter = Counter(y)  print(my_counter)  #A few data points are summarized  for i in range(10):  print(X[i], y[i])  #The dataset is plotted on a graph and displayed  for my_label, _ in my_counter.items():  row_ix = where(y == my_label)[0]  pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))  pyplot.legend()  pyplot.show() Output: The number of rows and columns   (800, 2) (800,)  Counter({0: 785, 1: 15})  [0.28622882 0.38305399] 0  [1.17971415 0.48003249] 0  [1.32658794 0.71712275] 0  Code explanation The required packages are imported using the ‘import’ function.  The dataset is generated using the ‘make_classification’ function present in the scikit-learn package is used.  It is done by specifying the number of rows and columns that need to be generated.  The number of rows and columns are displayed along with the summarization of class labelling.  A ‘for’ loop is used to print the first few classified values.  The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  Logistic regression In this classification technique, instead of finding continuous values like that of linear regression, we are concerned with finding discrete values. It is simply a classification technique that classifies the given data points into one of the labelled classes. Usually, we are looking at a Boolean output, wherein the result is either 0 or 1, yes or no and so on. Some examples include: Classifying an email as spam or not Finding whether it would rain today or not Naïve Bayes classification Bayes theorem is way of calculating the probability of a hypothesis (situation, which might not have occurred in reality) based on our previous experiences and the knowledge we have gained by it.  Bayes theorem is stated as follows: P(hypo | data) = (P(data | hypo) * P(hypo)) / P(data)  In the above equation,  P(hypo | data) is the probability of a hypothesis ‘hypo’ when data ‘data’ is given, which is also known as posterior probability.  P(data | hypo) is the probability of data ‘data’ when the specific hypothesis ‘hypo’ is known to be true.  P(hypo) is the probability of a hypothesis ‘hypo’ being true (irrespective of the data in hand), which is also known as prior probability of ‘hypo’.  P(data) is the probability of the data (irrespective of the hypothesis). The idea here is to get the value of the posterior probability, given other data. The posterior probability for a variety of different hypotheses is found out, and the probability that has the highest value is selected. This is known as the maximum probable hypothesis, and is also known as maximum a posteriori (MAP) hypothesis.  MAP(hypo) = max(P(hypo | data))  If the value of P(hypo | data) is replaced with the value we saw before, the equation would become:  MAP(hypo) = max((P(data | hypo) * P(hypo)) / P(data))  P(data) is considered as a normalizing term that helps in determining the probability. This value can be ignored when required, since it is a constant value. Naïve Bayes classifier is an algorithm that can be used with binary or multi-class classification problems. Once a Naïve Bayes classifier has learnt from the data, it stores a list of probabilities. Probabilities such as ‘class probability’ and ‘condition probability’ is stored. Training such a model is quick since the probability of every class and its associated value needs to be determined, and this doesn’t involve any optimization processes or coefficient changing.  K-nearest neighbour (KNN)  The simplest way to understand k-nearest neighbour, is that the training data for the algorithm is all the data in its entirety. KNN doesn’t have a different model, other than the one that stores the entire dataset, which means there is no machine learning that is actually happening. This means KNN makes predictions and extracts patterns directly from the training dataset itself. When a new data point is encountered, the corresponding value for that can be found using KNN by navigating through the entire training dataset, by looking at the ‘k’ number of very similar neighbours. Once the ‘k’ neighbours have been identified, they are summarized and the output for every instance is found. In case of regression, the mean of this output is the result, and in case of classification, the mode of this output is the result.  How to determine the ‘k’ neighbours? To find ‘k’ number of instances from the training dataset that are very similar to the new data point, we use a distance factor, and the most popular metric is the Euclidean distance.  Euclidean distance can be determined by finding the square root of the sum of the square of difference between the new point and an existing point in the data set, and this sum is from values in the range (a,b). Euclidean Distance: (a,b) = square root( sum( a – b) ^ 2))  Other distances that can be used include: Hamming distance Manhattan distance Minkowski Distance When the number of data points in the training set increases, the complexity of KNN also increases.  Support vector machines (SVM) The hyperplane present in linear SVM is learnt by performing simple transformations using linear algebra. The sum of the product of every pair of input data points is multiplied, and this is known as the inner product. The basic idea behind SVM is that the inner product of two vectors can be expressed as a sum of product of the first value of every vector.  To find inner product of two input vectors: [a,b] and [c,d], we do [a*c + b*d]  In order to predict new value, the dot product can be used, and the support vector can be calculated using the below equation: f(x) = coeff-1 + sum(coeff-2 * (a,b))   Here, ‘a’ and ‘b’ are input vectors and coeff-1 and coeff-2 are coefficients that are determined with the help of the training dataset and the learning algorithm. Stochastic gradient descent or sequential minimal optimization technique can be used. All these optimization techniques break down the main problem into sub-problems and every sub problem is solved by calculating the required value.  Decision trees It is a part of predictive modelling in machine learning that is considered as one of the most powerful algorithms. It is also known as CART, i.e. classification and regression trees since this can be used in the process of classification as well as regression tasks. Decision tree can be simply visualized as a binary tree that has a root and many branches from it and leaves. It is the same as the tree data structure. The root is a single input value, and the branches that lead to leaves are used in predicting the values for the given input.  The tree structure can be stored in the form of a graph structure or a set of rules. Once the data in the form of tree is available, it is simple to make predictions on it with the help of the leaf nodes. The specific branch and its leaf node is examined to reach the node.  Data is filtered from the root of the tree and goes and sits in the branch and the leaf that is relevant to it.  No data preparation or pre-processing is required while working with CART or decision trees.  Gradient boosting It is a method to build predictive models in machine learning. The idea behind boosting is to understand whether a weak learning algorithm can be made to learn better. This involves three attributes: A weak learning algorithm that makes prediction: Decision tree is considered to be a weak learner when it comes to gradient boosting. The best splits are chosen in decision trees, thereby minimizing the loss, hence they need to be improved so that they work well even when the split is random.  A loss function that needs to be optimized: This value depends on the situation in hand. Many different loss functions can be used, such as squared error, measure squared error, logarithmic loss function and so on. A new boosting algorithm won’t have to be figured out for every loss function.  An additive model that adds weak learner to minimize the loss function: The trees to the gradient boosting technique are added one at a time, so that the existing model trees don’t have changes. This way, the loss is minimized when new trees are added. Usually, gradient descent optimization technique is used to minimize the loss.  Random forest Random forest is an ensemble machine leaning algorithm that uses bootstrap aggregation or bagging. It is a statistical method that helps in estimating the quantity from a given data sample. It is done to reduce the variance for those algorithms that seem to have a high variance. Examples of algorithms that have high variance include CART, and decision trees. Decision trees are extremely sensitive to the data on which they are trained. If the training data changes, the resultant tree would also be completely different. A small change in the input makes a huge difference to the overall training and output.  An ensemble method is the one that combines the predictions that have come from many different machine learning algorithms, thereby making sure that the predictions are more accurate in comparison to dealing with an algorithm that gives a single prediction. It is like combining the best algorithms to give the best of best values.  Random forest makes sure that the every sub-tree that learns and trains on the data and makes the predictions is less correlated to the other sub-trees that do the same. The learning algorithm is limited to be able to look at a random sample of the data points, so that it doesn’t have the opportunity to look through all the variables, and select an optimal point to split upon (which is actually the case with CART). It is seen that for classification trees, a good value for the number of randomly selected columns from the dataset is square root (p) where p refers to the number of input variables. On the other hand, for regression trees, a good value for the number of randomly selected columns from the dataset is p/3.  Neural networks It is a part of deep learning that deals with artificial neural networks. In general, the word ‘neural’ or ‘neuro’ deals with the decision making branch of the human brain. The idea behind artificial neural network, also abbreviated as ANN, is that it takes decision similar to how the neurons in the brain function while performing a function or taking a decision.  It is called deep learning since these networks have various layers, and every layer has a large number of nodes. Every layer processes some part of the data and passes on the computed data to the next layer. The input data to one layer is the output data of the previous layer. Usually, the input layer’s nodes are large in number, and the output layer has just one node indicating that the data was processed, and the output has been obtained.  Conclusion In this post, we understood how classification works, the different types of classification and regression, their working, implementations by generating simple dataset and working through it using Python and other relevant machine learning related packages. 
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Types of Classification in Machine Learning

Takeaways from this article In this post, we und... Read More