I translate into the more on a kind of a statistical programming understanding and the language so you can see my stream the dark circles it is more related with our technical skills and the circles with you know light light circle without the color in it it's more going to have your more personality traits so no doubt you need to have the technical expertise like HPC is a kind of a high-performance computing ability you shouldn't you should develop you should have the good ability to develop the data into a shape which can translate it into the modeling model building data you need to have the understanding on beautiful statistical techniques that is available to analyze the data.

Now if I talk other than quantitative skills you are also required to have you know collaborative skills because all the projects in analytics are going to take multi functional approach so for example if you're working with the sales you may require to have the data of your sales consultant how what is their experience what is there you know the exposure into the different products they have how long they have been working what happened the attrition in that particular function so sales function is driven by the human resource data so you should be able to collaborate and think creatively that what are the different functions you are going to integrate to build the solution graphical artisan ship is like once you build the complex algorithm.

How you're going to bring the you know more you know the readability in the results which you are going to take it to the various business folks and they are going to able to understand from this quantitative model into the graphical knowledge and they are going to take actionable fact action on these things so that is how and and you should have the intellectual curiosity this means that you know means you should question the different function so for an example if I am working with the sales or the marketing team I make questions that how is our supply chain working and how is the product fulfillment happening on the ground so you should have those attributes which is going to combine with your quantitative skills.

We call it soft skills which is going to take you to the next level now this is very relevant and in today's time in the soft skills and the domain expertise how you're going to get the leadership ability leadership is like you're going to initiate the talks and the collaboration with the functions you need to discover the new business movements with the help of the data and analytics you are going to identify the new revenue sources you are going to identify the synergies in the business so you should be very clear on the domain and the functionality of your business having the storytelling is one of the attributes which is talked very intensively because imagine the scenario you might be a great data scientist programmer but if you're not able to translate the output of your statistical engine into the kind of a story which any normal person can consume even assume the HR.

The any of the business function expert into their day-to-day relevance with the business it is not going to help them and not going to help you so you should have the ability which you can where you can translate insights into the stories and which you can translate to them you should have the again range of IT skills it is not about only building the statistical models but also that how you're going to create the data warehouse how you can minimize the computation time how you can design the parallel computation system to have the faster output so these are the various skills that you are required to develop you know the kind of journey if you are planning to take it up and now connecting with your one of the question the person having lits a twelve-year with mainframe you may have many of these skills already existing.

What do you mean lack in learning about little bit statistics little bit programming about the statistical programming language like SAS R and Python kind of so learning those tools can help you yeah so I hope this answer your questions as a fresher you have immense scope like you can learn on the these tools and techniques and especially you are early at your stage so you can be definitely adopted in some of the functions where you go with even the minimum statistical programming and the model building skills as well so let me just cover quickly this is another from the same report this is for the Gartner 2016 report top skill gaps skill gaps identified by technical professional.

So what are the three biggest talent gaps related to the information technology or the digital business that your organization is trying to fulfill so you know means 34 of them have said that the gap comes under where you bring the competency under the cloud a cloud related competencies then another 23 of them have answered data analytics then 22 of them have said critical thinking and the problem solving then again related to if I think of IOT is one of the areas where 16 people have responded on its percent of respondents is in the percent and then again on the digitization and digital marketing 10 percent of people have responded so data and analytics come under the top three skills that are considered as a gap and that's how you see the opportunity and these are the areas where is still like we are evolving.

So now I'm going to talk about what happens and what are the models in the data-driven world so in data driven world you have to have discovery and innovation and this there has to be a radical personalization based on the data your or to go data sets other than the existing transactional systems now you are supposed to integrate so that you can bring the more cumulative view of your business this is going to help you as a model in the enhanced decision-making and now there has been the recommendation engine which takes you on a real-time hyper scale matching which is again more on a side of you know on demand and the real-time analysis that you are going to have in your day-to-day exploration it is going to take you on the massive data integration and based on the data integration you are going to again run the discovery and the innovation.

Now if I combine these models with our algorithmic landscape where they're the mini algorithm that you will explore as a data science professional or to be aspiring data science professional that you know including clustered and then various neural network activities and the kind of concepts that we have we are developing law of competences into the deep learning and learn of algorithm which are specialized in speech analytics and the image recognization which is going to enable with the some of the traditional algorithm which is more related to the linear and the non-linear functions that we used to have you're going to design your you use cases in resource allocation either having the predictive maintenance hyper personalization basis the algorithm discovering the new trends and anomalies and forecasting the business scenarios price and product optimizations even pricing for the new product launches that you are going to discover you are going to convert unstructured data into the meaningful information.

You're going to design mini you know the information system which are going to help you in triggering the alerts and alarms like fraud and the default kind of scenarios so those are the kind of you know changes or the data-driven world is going to see the use cases based the algorithm and the flow of data now let me take you on some of the attribute you know what is required when you are trying to build the transformation in your organization so think about the scenarios that if you are bringing the transformation where you know it is going to make your decision-making process more efficient and agile you have to have the people participation so people I mean to say that you know the one pillar that you need to strengthen is the skill sets with the people either it can be hiring the new talent who are already trained into the data science.

The data analytics related specialization either revamping the skills of the existing resources in today's time no matter how and which function and how how much experience and world you are it is required to have certain exposure and skills in the data science to read to remain you know I can say that relevant in today's decision making process so people turn as a one of the important pillar now intent in the leadership definitely the intent in the leadership is going to be one of the crucial factor which is going to help you to transform the decision-making or transformation so more we call it as an intent is that how your top leadership is aligned with bringing the new AI.

The AI powered application in the organization and even identifying how the competition is having and the growth in the adoption of the AI and analytics so having the intent of the leadership is going to help you once you have the intent and the people ready then you have to have the right data sources there should be a collection of the user data the data should centrally store it should not be stored in your personal laptop and desktop because you cannot leverage the and monetize the data if it is stored personally or in you, you have to identify the right tools and techniques for your function.

So I'm going to talk about what are the relevant platform or the software that is going to help you to learn the analytical you know the journey that you want to take up for your organization so for example very old and traditional organization like banking and financial services and pharmaceutical industry still they are using SAS as one of the major data analytics or the kind of the analytical engine many of the new organizations where you know the data sizes are not so big and even if it is big and they have designed in the right storage and the parallel computation and the Big Data Platform they are very successfully utilizing our so if I try to classify the SAS is still captures they around 60 to 65% market market share in terms of you know or people working on their system the new generation companies they have adopted are and Python and because of its nature are in Python now because of it is open source so you you avoid the cost of licensing which is quite high in the case of product slacks like SAS and SPSS.

So the companies new gen companies who do not want to land up in immediate investment in the cost of a statistical platform licensing they are very successfully using the R and Python and because it's open source majority of the resole researchers are also working on this platform and you get to see the latest algorithm and modules available as soon as it is discovered into the research any cadmium s pieces have been now it happened now the IBM product they acquired in 2009 and it's one of the great product in case of the market research however lot of companies other than market research have also started using the difference is more are and python is your programming intensive you need to have more number of lines written to perform a job however in SAS it is just classified into the modules which are like a procedure for every statistical algorithm so it's very easy to apply on the various business problems.

You can get benefited if you want to have the chassis's system for your analytics engine because it gives you the ready-made module and there you can get a is get an is in terms of deployment of analytical algorithm it is much easier however very costly in the application so that is going to be your technology landscape and if you want to start learning analytics I would advise you to explore an R and Python because it is available in open source and you can get a trial version of the SPSS as well there's a university edition of SAS also so you can try for your application so I am just writing SAS University Edition and you can use for your learning if you want to get into this software and you can try the SPSS trial version as well if you want to get certain exposure.

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