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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!

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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Types of Probability Distributions Every Data Science Expert Should know

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values.  Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course. 
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Top Data Analytics Certifications

What is data analytics?In the world of IT, every small bit of data count; even information that looks like pure nonsense has its significance. So, how do we retrieve the significance from this data? This is where Data Science and analytics comes into the picture.  Data Analytics is a process where data is inspected, transformed and interpreted to discover some useful bits of information from all the noise and make decisions accordingly. It forms the entire basis of the social media industry and finds a lot of use in IT, finance, hospitality and even social sciences. The scope in data analytics is nearly endless since all facets of life deal with the storage, processing and interpretation of data.Why data analytics? Data Analytics in this Information Age has nearly endless opportunities since literally everything in this era hinges on the importance of proper processing and data analysis. The insights from any data are crucial for any business. The field of data Analytics has grown more than 50 times from the early 2000s to 2021. Companies specialising in banking, healthcare, fraud detection, e-commerce, telecommunication, infrastructure and risk management hire data analysts and professionals every year in huge numbers.Need for certification:Skills are the first and foremost criteria for a job, but these skills need to be validated and recognised by reputed organisations for them to impress a potential employer. In the field of Data Analytics, it is pretty crucial to show your certifications. Hence, an employer knows you have hands-on experience in the field and can handle the workload of a real-world setting beyond just theoretical knowledge. Once you get a base certification, you can work your way up to higher and higher positions and enjoy lucrative pay packages. Top Data Analytics Certifications Certified Analytics Professional (CAP) Microsoft Certified Azure Data Scientist Associate Cloudera Certified Associate (CCA) Data Analyst Associate Certified Analytics Professional (aCAP) SAS Certified Data Analyst (Using SAS91. Certified Analytics Professional (CAP)A certification from an organisation called INFORMS, CAP is a notoriously rigorous certification and stands out like a star on an applicant's resume. Those who complete this program gain an invaluable credential and are able to distinguish themselves from the competition. It gives a candidate a comprehensive understanding of the analytical process's various fine aspects--from framing hypotheses and analytic problems to the proper methodology, along with acquisition, model building and deployment process with long-term life cycle management. It needs to be renewed after three years.The application process is in itself quite complex, and it also involves signing the CAP Code of Ethics before one is given the certification. The CAP panel reviews each application, and those who pass this review are the only ones who can give the exam.  Prerequisite: A bachelor’s degree with 5 years of professional experience or a master's degree with 3 years of professional experience.  Exam Fee & Format: The base price is $695. For individuals who are members of INFORMS the price is $495. (Source) The pass percentage is 70%. The format is a four option MCQ paper. Salary: $76808 per year (Source) 2. Cloudera Certified Associate (CCA) Data Analyst Cloudera has a well-earned reputation in the IT sector, and its Associate Data analyst certification can help bolster the resume of Business intelligence specialists, system architects, data analysts, database administrators as well as developers. It has a specific focus on SQL developers who aim to show their proficiency on the platform.This certificate validates an applicant's ability to operate in a CDH environment by Cloudera using Impala and Hive tools. One doesn't need to turn to expensive tuitions and academies as Cloudera offers an Analyst Training course with almost the same objectives as the exam, leaving one with a good grasp of the fundamentals.   Prerequisites: basic knowledge of SQL and Linux Command line Exam Fee & Format: The cost of the exam is $295 (Source), The test is a performance-based test containing 8-12 questions to be completed in a proctored environment under 129 minutes.  Expected Salary: You can earn the job title of Cloudera Data Analyst that pays up to $113,286 per year. (Source)3. Associate Certified Analytics Professional (aCAP)aCAP is an entry-level certification for Analytics professionals with lesser experience but effective knowledge, which helps in real-life situations. It is for those candidates who have a master’s degree in a field related to data analytics.  It is one of the few vendor-neutral certifications on the list and must be converted to CAP within 6 years, so it offers a good opportunity for those with a long term path in a Data Analytics career. It also needs to be renewed every three years, like the CAP certification. Like its professional counterpart, aCAP helps a candidate step out in a vendor-neutral manner and drastically increases their professional credibility.  Prerequisite: Master’s degree in any discipline related to data Analytics. Exam Fee: The base price is $300. For individuals who are members of INFORMS the price is $200. (Source). There is an extensive syllabus which covers: i. Business Problem Framing, ii. Analytics Problem Framing, iii. Data, iv. Methodology Selection, v. Model Building, vi. Deployment, vii. Lifecycle Management of the Analytics process, problem-solving, data science and visualisation and much more.4. SAS Certified Data Analyst (Using SAS9)From one of the pioneers in IT and Statistics - the SAS Institute of Data Management - a SAS Certified Data Scientist can gain insights and analyse various aspects of data from businesses using tools like the SAS software and other open-source methodology. It also validates competency in using complex machine learning models and inferring results to interpret future business strategy and release models using the SAS environment. SAS Academy for Data Science is a viable institute for those who want to receive proper training for the exam and use this as a basis for their career.  Prerequisites: To earn this credential, one needs to pass 5 exams, two from the SAS Certified Big Data Professional credential and three exams from the SAS Certified Advanced Analytics Professional Credential. Exam Fee: The cost for each exam is $180. (Source) An exception is Predictive Modelling using the SAS Enterprise Miner, costing $250, This exam can be taken in the English language. One can join the SAS Academy for Data Science and also take a practice exam beforehand. Salary: You can get a job as a SAS Data Analyst that pays up to $90,000 per year! (Source) 5. IBM Data Science Professional CertificateWhenever someone studies the history of a computer, IBM (International Business Machines) is the first brand that comes up. IBM is still alive and kicking, now having forayed into and becoming a major player in the Big Data segment. The IBM Data Science Professional certificate is one of the beginner-level certificates if you want to sink your hands into the world of data analysis. It shows a candidate's skills in various topics pertaining to data sciences, including various open-source tools, Python databases, SWL, data visualisation, and data methodologies.  One needs to complete nine courses to earn the certificate. It takes around three months if one works twelve hours per week. It also involves the completion of various hands-on assignments and building a portfolio. A candidate earns the Professional certificate from Coursera and a badge from IBM that recognises a candidate's proficiency in the area. Prerequisites: It is the optimal course for freshers since it requires no requisite programming knowledge or proficiency in Analytics. Exam Fee: It costs $39 per month (Source) to access the course materials and the certificate. The course is handled by the Coursera organisation. Expected Salary: This certification can earn you the title of IBM Data Scientist and help you earn a salary of $134,846 per annum. (Source) 6. Microsoft Certified Azure Data Scientist AssociateIt's one of the most well-known certifications for newcomers to step into the field of Big Data and Data analytics. This credential is offered by the leader in the industry, Microsoft Azure. This credential validates a candidate's ability to work with Microsoft Azure developing environment and proficiency in analysing big data, preparing data for the modelling process, and then progressing to designing models. One advantage of this credential is that it has no expiry date and does not need renewal; it also authorises the candidate’s extensive knowledge in predictive Analytics. Prerequisites: knowledge and experience in data science and using Azure Machine Learning and Azure Databricks. Exam Fee: It costs $165 to (Source) register for the exam. One advantage is that there is no need to attend proxy institutions to prepare for this exam, as Microsoft offers free training materials as well as an instructor-led course that is paid. There is a comprehensive collection of resources available to a candidate. Expected Salary: The job title typically offered is Microsoft Data Scientist and it typically fetches a yearly pay of $130,993.(Source) Why be a Data Analytics professional? For those already working in the field of data, being a Data Analyst is one of the most viable options. The salary of a data analyst ranges from $65,000 to $85,000 depending on number of years of experience. This lucrative salary makes it worth the investment to get a certification and advance your skills to the next level so that you can work for multinational companies by interpreting and organising data and using this analysis to accelerate businesses. These certificates demonstrate that you have the required knowledge needed to operate data models of the volumes needed by big organizations. 1. Demand is more than supply With the advent of the Information Age, there has been a huge boom in companies that either entirely or partially deal with IT. For many companies IT forms the core of their business. Every business has to deal with data, and it is crucial to get accurate insights from this data and use it to further business interests and expand profits. The interpretation of data also aims to guide them in the future to make the best business decisions.  Complex business intelligence algorithms are in place these days. They need trained professionals to operate them; since this field is relatively new, there is a shortage of experts. Thus, there are vacancies for data analyst positions with lucrative pay if one is qualified enough.2. Good pay with benefitsA data analyst is an extremely lucrative profession, with an average base pay of $71,909 (Source), employee benefits, a good work-home balance, and other perks. It has been consistently rated as being among the hottest careers of the decade and allows professionals to have a long and satisfying career.   Companies Hiring Certified Data Analytics Professionals Oracle A California based brand, Oracle is a software company that is most famous for its data solutions. With over 130000 employees and a revenue of 39 billion, it is surely one of the bigger players in Data Analytics.  MicroStrategy   Unlike its name, this company is anything but micro, with more than 400 million worth of revenue. It provides a suite of analytical products along with business mobility solutions. It is a key player in the mobile space, working natively with Android and iOS.   SAS   One of the companies in the list which provides certifications and is also without a doubt one of the largest names in the field of Big Data, machine learning and Data Analytics, is SAS. The name SAS is derived from Statistical Analysis System. This company is trusted and has a solid reputation. It is also behind the SAS Institute for Data Science. Hence, SAS is the organisation you would want to go to if you're aiming for a long-term career in data science.    Conclusion To conclude, big data and data Analytics are a field of endless opportunities. By investing in the right credential, one can pave the way to a viable and lucrative career path. Beware though, there are lots of companies that provide certifications, but only recognised and reputed credentials will give you the opportunities you are seeking. Hiring companies look for these certifications as a mark of authenticity of your hands-on experience and the amount of work you can handle effectively. Therefore, the credential you choose for yourself plays a vital role in the career you can have in the field of Data analytics.  Happy learning!    
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Top Data Analytics Certifications

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Why Should You Start a Career in Machine Learning?

If you are even remotely interested in technology you would have heard of machine learning. In fact machine learning is now a buzzword and there are dozens of articles and research papers dedicated to it.  Machine learning is a technique which makes the machine learn from past experiences. Complex domain problems can be resolved quickly and efficiently using Machine Learning techniques.  We are living in an age where huge amounts of data are produced every second. This explosion of data has led to creation of machine learning models which can be used to analyse data and to benefit businesses.  This article tries to answer a few important concepts related to Machine Learning and informs you about the career path in this prestigious and important domain.What is Machine Learning?So, here’s your introduction to Machine Learning. This term was coined in the year 1997. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences.”, as defined in the book on ML written by Mitchell in 1997. The difference between a traditional programming and programming using Machine Learning is depicted here, the first Approach (a) is a traditional approach, and second approach (b) is a Machine Learning based approach.Machine Learning encompasses the techniques in AI which allow the system to learn automatically looking at the data available. While learning, the system tries to improve the experience without making any explicit efforts in programming. Any machine learning application follows the following steps broadlySelecting the training datasetAs the definition indicates, machine learning algorithms require past experience, that is data, for learning. So, selection of appropriate data is the key for any machine learning application.Preparing the dataset by preprocessing the dataOnce the decision about the data is made, it needs to be prepared for use. Machine learning algorithms are very susceptible to the small changes in data. To get the right insights, data must be preprocessed which includes data cleaning and data transformation.  Exploring the basic statistics and properties of dataTo understand what the data wishes to convey, the data engineer or Machine Learning engineer needs to understand the properties of data in detail. These details are understood by studying the statistical properties of data. Visualization is an important process to understand the data in detail.Selecting the appropriate algorithm to apply on the datasetOnce the data is ready and understood in detail, then appropriate Machine Learning algorithms or models are selected. The choice of algorithm depends on characteristics of data as well as type of task to be performed on the data. The choice also depends on what kind of output is required from the data.Checking the performance and fine-tuning the parameters of the algorithmThe model or algorithm chosen is fine-tuned to get improved performance. If multiple models are applied, then they are weighed against the performance. The final algorithm is again fine-tuned to get appropriate output and performance.Why Pursue a Career in Machine Learning in 2021?A recent survey has estimated that the jobs in AI and ML have grown by more than 300%. Even before the pandemic struck, Machine Learning skills were in high demand and the demand is expected to increase two-fold in the near future.A career in machine learning gives you the opportunity to make significant contributions in AI, the future of technology. All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment.  The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of $112,000.  Huge numbers of jobs are expected to be created in the coming years.  Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch $20.83B in 2024, according to Forbes.  We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. Machine Learning is the only way to extract meaning out of data and businesses need Machine Learning engineers to analyze huge data and gain insights from them to improve their businesses.If you like numbers, if you like research, if you like to read and test and if you have a passion to analyse, then machine learning is the career for you. Learning the right tools and programming languages will help you use machine learning to provide appropriate solutions to complex problems, overcome challenges and grow the business.Machine Learning is a great career option for those interested in computer science and mathematics. They can come up with new Machine Learning algorithms and techniques to cater to the needs of various business domains.As explained above, a career in machine learning is both rewarding and lucrative. There are huge number of opportunities available if you have the right expertise and knowledge. On an average, Machine Learning engineers get higher salaries, than other software developers.Years of experience in the Machine Learning domain, helps you break into data scientist roles, which is not just among the hottest careers of our generation but also a highly respected and lucrative career. Right skills in the right business domain helps you progress and make a mark for yourself in your organization. For example, if you have expertise in pharmaceutical industries and experience working in Machine learning, then you may land job roles as a data scientist consultant in big pharmaceutical companies.Statistics on Machine learning growth and the industries that use MLAccording to a research paper in AI Multiple (https://research.aimultiple.com/ml-stats/), the Machine Learning market will grow to 9 Billion USD by the end of 2022. There are various areas where Machine Learning models and solutions are getting deployed, and businesses see an overall increase of 44% investments in this area. North America is one of the leading regions in the adoption of Machine Learning followed by Asia.The Global Machine Learning market will grow by 42% which is evident from the following graph. Image sourceThere is a huge demand for Machine Learning modelling because of the large use of Cloud Based Applications and Services. The pandemic has changed the face of businesses, making them heavily dependent on Cloud and AI based services. Google, IBM, and Amazon are just some of the companies that have invested heavily in AI and Machine Learning based application development, to provide robust solutions for problems faced by small to large scale businesses. Machine Learning and Cloud based solutions are scalable and secure for all types of business.ML analyses and interprets data patterns, computing and developing algorithms for various business purposes.Advantages of Machine Learning courseNow that we have established the advantages of perusing a career in Machine Learning, let’s understand from where to start our machine learning journey. The best option would be to start with a Machine Learning course. There are various platforms which offer popular Machine Learning courses. One can always start with an online course which is both effective and safe in these COVID times.These courses start with an introduction to Machine Learning and then slowly help you to build your skills in the domain. Many courses even start with the basics of programming languages such as Python, which are important for building Machine Learning models. Courses from reputed institutions will hand hold you through the basics. Once the basics are clear, you may switch to an offline course and get the required certification.Online certifications have the same value as offline classes. They are a great way to clear your doubts and get personalized help to grow your knowledge. These courses can be completed along with your normal job or education, as most are self-paced and can be taken at a time of your convenience. There are plenty of online blogs and articles to aid you in completion of your certification.Machine Learning courses include many real time case studies which help you in understanding the basics and application aspects. Learning and applying are both important and are covered in good Machine Learning Courses. So, do your research and pick an online tutorial that is from a reputable institute.What Does the Career Path in Machine Learning Look Like?One can start their career in Machine Learning domain as a developer or application programmer. But the acquisition of the right skills and experience can lead you to various career paths. Following are some of the career options in Machine Learning (not an exhaustive list):Data ScientistA data scientist is a person with rich experience in a particular business field. A person who has a knowledge of domain, as well as machine learning modelling, is a data scientist. Data Scientists’ job is to study the data carefully and suggest accurate models to improve the business.AI and Machine Learning EngineerAn AI engineer is responsible for choosing the proper Machine Learning Algorithm based on natural language processing and neural network. They are responsible for applying it in AI applications like personalized advertising.  A Machine Learning Engineer is responsible for creating the appropriate models for improvement of the businessData EngineerA Data Engineer, as the name suggests, is responsible to collect data and make it ready for the application of Machine Learning models. Identification of the right data and making it ready for extraction of further insights is the main work of a data engineer.Business AnalystA person who studies the business and analyzes the data to get insights from it is a Business Analyst. He or she is responsible for extracting the insights from the data at hand.Business Intelligence (BI) DeveloperA BI developer uses Machine Learning and Data Analytics techniques to work on a large amount of data. Proper representation of data to suit business decisions, using the latest tools for creation of intuitive dashboards is the role of a BI developer.  Human Machine Interface learning engineerCreating tools using machine learning techniques to ease the human machine interaction or automate decisions, is the role of a Human Machine Interface learning engineer. This person helps in generating choices for users to ease their work.Natural Language Processing (NLP) engineer or developerAs the name suggests, this person develops various techniques to process Natural Language constructs. Building applications or systems using machine learning techniques to build Natural Language based applications is their main task. They create multilingual Chatbots for use in websites and other applications.Why are Machine Learning Roles so popular?As mentioned above, the market growth of AI and ML has increased tremendously over the past years. The Machine Learning Techniques are applied in every domain including marketing, sales, product recommendations, brand retention, creating advertising, understanding the sentiments of customer, security, banking and more. Machine learning algorithms are also used in emails to ease the users work. This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Why Should You Start a Career in Machine Learning?

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