Big Data Analytics is everywhere, across geographies, across industries. It is a known fact that the demand supersedes the supply of seasoned professionals in data science field. It is at the frontier of IT. Also, many IT professionals on a medium skilled level, are in danger of losing their jobs due to automation, giving rise to further need of upscaling their skills, and what better way than entering the field of big data analytics.
Keeping in mind with the growing needs of the industry, Imarticus Learning proudly introduces the ‘Data Science Prodegree’ Program in collaboration with Genpact, a global leader in Analytics.
Firstly, let’s agree that doing a course in big data analytics is the best career move, there is a soaring demand for professionals, as data is considered useless without the skills to analyse it. We are in an age where data is being created in high volumes, across channels and there is a dearth in the number of qualified professionals to accurately interpret it.
It is a lucrative career option with a starting salary of 5 lakhs for fresher’s to above 35 lakhs for 10 plus years of experience. Adoption of Big data analytics is increasing in industries, and according to the field gurus, it is becoming a high priority for most organisations.
So the “Why” of why you should do a course in big data analytics is pretty convincing.
Read on to gauge why doing the Data Science Prodegree Program with Imarticus Learning in collaboration with Genpact, be beneficial to you.
They are the pioneers in designing, transforming, and managing intelligent operations in the digital and analytics stream. They are the catalysts in running digital led transformation, through extensive digital, analytics and consulting capabilities, thus streamlining numerous processes for fortune 500 companies.
All these capabilities make Genpact the perfect partner for Imarticus, specially the expertise they bring to the program. Genpact has collaborated with Imarticus on design and content creation, hence ensuring that on completing the course the candidate becomes a job ready professional in analytics.
What does the Data Science Prodegree program offer, and how is it designed?
The data science Prodegree program offered by Imarticus Learnings, in association with the knowledge partner Genpact, is a program spread over 200 hours. It covers all foundational concepts of leading analytical tools, such as SAS, R, Python, Hive, Spark and Tableau, it includes self-paced videos and project work. The course is delivered by faculty with over 10 years of experience in the data analytics industry.
The Course is spread over 4 semesters, which will gauge the candidates with timely assessments, after each semester, and additionally will help them gain theoretical knowledge of data science tools along with exposure to business perspectives and industry best practices, as the program is assisted with several guest lectures and data based project work. All this will eventually help the participant develop an essence in data science and analytics.
The program is a hybrid model between class room lectures, by expert faculty with industry credentials and self-paced instructor videos to manage to learn at your own convenience. A mix of conceptual and application based learning.
Basically, it is a course designed to upscale your skills in analytics.
Who should consider enrolling for the course?
The Data Science Prodegree program is beneficial for professionals or students who are firstly, interested in building a profession in the analytics industry. Secondly, individuals who are keen in enhancing their technical skills with exposure to cutting edge practices.
So if you are a post graduate with a degree in science math, statistics, or computer application, or if you are working as a professional in programming or IT, with an experience of not more than 4 years, with an objective to sharpen your skill sets and change career paths, you should strongly consider enrolling for the Data Science Prodegree Program.
Where do I stand post the course completion in the Data Analytics Market?
Top companies are hiring for business analytics, companies like Visa, Amazon, Facebook, and overall industries of all nature are recognising the importance of data analysis and hiring to meet the demands.
On completion of the course, not only will you be trained in a manner which will make you a job ready professional in the data analysis industry, but the Data Science Prodegree Program will also assist you in resume building, interview preparation by conducting mock interviews and will also assist you with a placement portal, through private and open networks on their placement portal.
So from relevant content designed by industry stalwarts, to industry centric faculty, instructionally designed modules, and great networking opportunities, to ensuring you pick up skills in not only the theoretical concepts of data analytics, but also gain learning knowledge of the technical tools. Ample assistance in picking up the best jobs in the industry, the Data Science Prodegree Program from Imarticus Learning, offers you all.
Give a boost to your career and enrol now!
There is a lot of confusion in the data science Job, as it is relatively new profession. We have got a lot of queries about Data Scientist salary and there career path. In this blog will talk about the how data scientist came into a picture and what is the starting salary for this job.
Statistics state that history’s most unbalanced demand and supply ratio is seen today in the Big Data Industry. It is known that in the U.S.A there would soon be a shortage of around 140,000-190,000 professionals, with the required skill set for data analytics. With a tsunami like amount of information being generated by firms on a daily basis, it becomes difficult to for them to make sense of it.
This is where the Data Scientist or the Data Analyst comes into the picture. These are individuals equipped with a certain skill set, who can take all this information or more popularly known as data and make sense of it. They work with great volumes of data sets, study them and generate various insights which help the company prosper.
As this is a fairly new thing, there are a lot of areas which are clearly out of focus. There has been no clear distinction between the two terms ‘Data Scientist’ and ‘Data Analyst’ and people still haven’t had any clear cut idea about what is meant by either Hadoop or SAS Programming and so on.
As this field needs a specific skill set like statistics, an eye for drawing out the patterns, being great at analysis and exceptional at programming knowledge; makes the number of professionals apt for this job very limited. The fact that there has been a rising demand in the firms for Data Scientists, states that the career prospects in this field have grown exponentially.
Glassdoor placed it in the first position on the 1st, as a Best Jobs in America list. According to IBM, demand for this role will soar 28% by 2020.
It is believed that the field of Data Analytics would be further divided into three different categories. These would be for professionals who would be good at coding and creating languages to sort the data, people possessing exemplary statistical skills and those who have an eye for drawing traits and patterns from the same.
With the Data Analytics Industry becoming dynamic by the day, the prospects for someone looking to make it their career are really high. The average salary of a Data Scientist starting into this industry can range from 3lakh-4lakh and can go onto 12lakh- 20lakh per annum.
There are a lot of courses offered in Data Analytics today, whereby any aspirant can get trained in various data analytics tools like R Programming, Python, SAS Programming, Big Data Hadoop and many others.
At, Imarticus Learning we offer various short term and long term courses in Data Analytics and the tools therein.
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One should have a good grasp of technology, as its uses and advantages have seeped in almost all spheres of professional setups. If you are working in the field of IT, programmer to be specific, a quick way to upgrade your resume would be to learn Python. Python is considered to be the most commonly used programming languages. Hence for a programmer who is on the brink of embarking his career should learn Python.
So if you are considering learning to code, and be updated and efficient with your skills in the world of programming. Then further read on to understand five undisputable reasons you should learn Python.
Quick and Fast
Python is definitely an easy language to learn, to be true the language was designed keeping this feature in mind. For a beginner, the biggest advantage is that the codes are approximately 3-5 times shorter in Python than in any other programming language. Python is also very easy to read, almost like reading the English language, hence it becomes effective yet uncomplicated in its application.
The dual advantage is that a beginner will not only pick up faster but, will also be able to code complex programmes in a shorter amount of time. And an experienced programmer will increase productivity.
Big Corporates use Python
Python is one of the most favourite languages used at Google, and they are ever hiring experts. Yahoo, IBM, Nokia, Disney, NASA all rely on Python. They are always in search of Python web developers, and a point to note is that they are big pay masters. Hence learning Python equals to big Pay cheques.
Python for Machine Learning and Artificial Intelligence
The biggest USP of Python is that it is easy to use, flexible and fast, hence it is the preferred language choice. And especially so in computer science research. Through Python, one can perform complex calculation with a simple ‘import’ statement, followed by a function call, thanks to Python’s numerical computation engines. With time Python has become the most liked language for Machine Learning.
Python is Open Source and comes with an exciting Ecosystem
Python has been there for almost 20 years or so, running across platforms as an open source. With Python, you will get codes for, Linux, windows and MacOS. There is also a number of resources that get developed for Python that keeps getting updated. It also has a standard library with in-built functionality.
Nothing is Impossible with Python
And if the above reasons are not convincing, perhaps the best reason to learn Python, is that irrespective of what your career goals are you can do anything. Since it is easy and quick to learn, with it, you can adapt to any other language or more importantly environment. Be it web development, big data, mathematical computing, finance, trading, game development or even cyber security, you can use Python to get involved.
Python is not some kind of a niche language, and neither is it a small time scripting language, but major applications like YouTube or Dropbox are written in Python. The opportunities are great, so learn the language and get started.
Data is omnipresent, it is available everywhere. We cannot deny the fact that data is changing the world in ways we cannot fathom. We are at a time, where we are witnessing innovation in the way we are collecting and interpreting this data. Big data analysis is a groundbreaking step and it is only becoming bigger by the day. We are slowly realising that the use of data in almost all aspects of our lives, is actually making our lives simpler, at the same time it is presenting an opportunity for us, by helping us shape the world that we live in for the good.
There are many mainstream uses of data, which can be collected from various sources, and on interpretation can produce huge values. For example, e-commerce sites, that increase revenue with the inputs from customer feedback and shopping pattern.
Eventually, every aspect of our lives will be impacted by the advent of big data. It is not all buzz without action. There are some significant areas where big data is already making a difference, in fact in some areas it is doing so in a camouflaged approach.
Analytics is working in wonderful ways, and it has found some pretty interesting applications.
A quick read below will reveal some unobserved ways in which big data is touching our lives :
1. Enhanced Sport Performance
Most select sports are now using the benefits of big data analytics, video analytics is used to map the performance in football or baseball, sensor technology is also used in the sports equipment such as basket, golf clubs which relay feedback over smartphones, via cloud servers, on how to improve performance in the game. Health bands help track activities of sport persons outside of the sporting environment to keep a check on their nutrition and sleep also social media usage and conversations to understand the psychological wellbeing.
2. Elevating Machine Performance
Big data tools are used to control autonomous cars, for example, Toyota’s Prius is equipped with GPS, powerful computers, and sensors to safely drive on road without human intervention. Computer performance can also be enhanced by using big data tools.
3. Augmenting and Refining Cities and Countries
Many cities use big data to improve aspects of its functioning. For example, traffic situations could be better controlled, by getting factual data, by weather understanding and through social media. Some cities ae planning to use big data to upgrade themselves as smart cities, where the transport, infrastructure and utility processes are all interconnected. In other words, a train would wait for a delayed plane, or where traffic signals change their functioning on predictions to minimize traffic jams.
4. Use of Sentiments in Elections
Big data analytics can impact the way we choose our leaders, or perhaps the way the leader ensure we choose them by appealing to our sentiments and needs, voter models are designed to identify specific voters who could make a difference in elections and specifically target messages to those voters. The Obama campaign in 2012 presidential elections took this to another level into their stride.
5. Better Healthcare
Big data, with its computing ability, can assist us to decode entire DNA strings in moments. This will allow us to achieve better results and maybe understand or better still predict disease patterns. It can almost help us identify epidemics and outbreaks by linking data from medical records along with social media analytics, and imagine all this will be in actual time. Just by reading messaging like ‘not feeling good today” or “in Bed no energy” on social media.
As time extends, there will be many applications and tools of big data that will have a variety of possibilities. While it can be debated how this data is invading our privacy, it is best to look at the bright side and acknowledge how this data will make our lives easy.
Data engineering and data scientist are job titles which might be new to us in recent times, however, these roles have been around for a while.
Traditionally, anyone who would analyse data would be called a Data Analyst, and the person responsible for creating platforms to support the analysis is a Business Developer.
In the world of IT, the data scientist gets more visibility and praise, as they are the ones, extracting vital intelligence from big data and help organisations take critical decisions with regards to their business swiftly. But it is important to note that the data scientist does not work in isolation, they are not capable of generating valuable information independently, and they need the constant support of Data Engineers. The engineers are the ones designing and maintaining software and platforms that operate the big data pipeline. They set the stage and keep it running.
A Data Scientist is someone who is an excellent statistician, with above average software engineering skills. Should be primarily inquisitive, have the skills of data visualisation and storytelling along with programming skills. His tasks would essentially be to identify the question and finding answers through data, finding a correlation between dissimilar data, to be able to tell the findings, hence storytelling ability, and lastly should be hands on with tools like Julia, Python Programming, data visualisation tools like Qlik view or Tableau.
The description of Data Engineers and Data Scientist can be quite obscure, there is an overlap. While these roles still maintain to be distinct data science job roles, they require different skills and experience. Some data scientist can do data engineering, while some data engineers can do data analysis and visualisation as well.
The emergence of big data has opened space for new titles and roles to come into existence. Over the past couple of years’ businesses have applied all means to get individuals who have the skills to turn data into gold.
A lot has changed in the way businesses function, earlier a lot of companies were functioning in the physical world, nowadays most businesses function on the digital platform. When a company is mostly functioning online, there is a huge accumulation of data. Data about who is visiting your website, if they are choosing your competitor’s website as opposed to yours, what could be the reason, you also get data about the statistics of the competitor’s target audience, So the possibility of the data accumulation is too big and very fast. The data are screaming information and is noisy beyond comprehension.
In order to find a way in this data, one needs to sort this in two ways,
Firstly, to create a database to process the data and to store it and the second would be the need of people to comprehend the data and know how to ask a relevant question and research the data in a method that the concerned business can take informed pointers from it. This stored data needs people who know statistics who know how to write code, in order to get insightful information.
Data Scientist and Data Engineers are these people; they are the need of the hour. To know how to process data using various platforms, and more importantly, we need them to be around, These people also know how making sense of the information, how to analyse it. They don’t only plot graphs from data collected from a spreadsheet but also create statistical models that over a period of time affects the business and products with effective ways to increase the revenue.
The data available could be stand but smart and appropriately skilled people are the ones who help find that needle in the haystack.
You cannot achieve tomorrow’s results using yesterday’s methods, and this line is the need to understand and accept the impact of BIG DATA and growing demands of business.
Big data means access to big information, leading to abilities in doing things you could not do before.
Case in point – a small exercise…
Before you read ahead, check the posture you are sitting in, now look around and check the posture of others sitting beside you, observe… what do you see? Someone is slouching. Someone has their legs crossed, elbows resting on the armchair, palms pressed against their chins, a constant moving of the limbs. Not everyone sitting around you has the same posture. What if we put many sensors on the chairs around you to help record and create an index, which is unique to you, which says person X sits in these particular postures during specific intervals of the day?
In addition, say what if this data was sold to car manufacturers as an anti-theft design to help recognise that the person sitting behind the wheel is not the same and say until he keys in a password the car even though entered would not start.
Now imagine what if every single car in the world had this technology, think of the benefits of aggregating this data. Maybe we would be able to predict which postures while driving would lead to an accident say in something as close as the next 7 seconds do, and alert the driver to change position or take a break from driving.
Now my dear reader THAT is the power of BIG DATA. It can help record, collect, understand, predict, and prevent events from a random collection of information.
So if that is so useful where lies the problem? Why is the world not already a better place?
Because there is a gap between opportunity and demand in skilled professionals to help comprehend and present valuable insights into specific relevant trends, big or small from the available data.
Big data is all around us and there is a calling need to preserve all the generated data for the fear of missing something that could be important. Hence, comes the need for big data Analytics, Big data is crucial to do better business, to take accurate decisions and in always being a step ahead of your competitors. Therefore, if you are a professional in the Analytics domain there is a sea of opportunity waiting for you to dive in.
Big data Analytics is Unfathomable and depending on the environment one can choose from
- Prescriptive Analytics
- Descriptive Analytics
- Predictive Analytics
Big Data Analytics market is predicted to surpass $125 billion in between 2015 – 2020, which in turn in some sense means handsome pay brackets for the skilled individual.
- Salary Aspect
To improve the performance of the organisation, most companies have either already implemented or are in the process of implementing Big Data Analytics, as they already have the data at their disposal.
- All Organisations adopting some form of Data Analytics = Growing Market
There is a big gap in skilled professionals who are able to convert the data available. The ability to see small trends from the pool of big information. Which ultimately advances the organisation in the right direction. There are two types of talent deficits. Data consultants – who have the ability to not only, understand but also use the data at hand in the appropriate way and the other is Data Scientists who can perform analytics.
- Skill Deficit
Big data Analytics is not restricted to any specific Domain; it can be and in recent times is being used in Healthcare, Automobiles, Manufacturing and more, creating a massive global demand.
- Global demand across industries
Analytics becomes a competitive resource for organisations, according to certain studies Analytics has already become the most important asset in current times. Because we are emerging from the undeveloped analytics trends to more advanced forms. It is undeniable that Analytics plays a vital role in decision making and taking strategic initiatives for the business.
- Importance of Analytics for Better Decision Making in Organisation
So in deduction to the above, Analytics no matter how advanced is human dependent. These are exciting times for skilled people who can comprehend data and give valuable insights from the business point of view. A trained individual with the right Analytics insight can master an ocean of big data and become an indispensable asset to the organisation becoming a springboard to the business and their career.
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The best possible example of the usage of big data analytics can be found in the legendary fictional works of Sir Arthur Conan Doyle. Sherlock Holmes, as we all know him today through the famous crime detective show, Sherlock is said to be one of the biggest patrons of this concept. The world’s first consulting detective, Holmes once said, “It is a capital mistake to theorize before one has data.” These words uttered during the case of A Study in Scarlet, hold true as data proved to be his timely assistant in solving extremely difficult cases, by helping him come to perfect deductions.
As we accumulate more and more of this data, we feel the need to have optimal processing skills and analytical capabilities in order to process it. India, as a country has been making a lot of growth with many government agencies and private companies, getting on board with the data analytics revolution. Continuing in the same vein, the Comptroller and Auditor General (CAG) has also put together the ‘Big Data Management Policy’ for Indian audit and accounts departments, in order to foster the use of data analytics and ensure the improvement of their functions. Many more efforts are being taken in the same regard, like for instance the Centre for Data Management and Analytics (CDMA) has been inaugurated, in order to synthesize and integrate relevant data for auditing process. The aim here is to exploit data rich environments, on both the state level as well as the central level in order to develop the audit and accounts department.
Data has always helped humans in increasing the level of their decision making skills, in every field of medicine, science, and technology. The recent couple of years saw a great surge in the availability and accessibility of Big Data and its storage options. With big data coming to the fore, it has begun arriving on the scene with alarming velocity, volume, and variety. With so many technological advancements revolving around the accessibility and storage, have led to the opening up of new and empowering possibilities.
On the other hand, there are DISCOMS, which are set up for capturing all the data from the sensors, which are installed in order to analyse the power usage patterns, so as to put together preventive measures for Aggregated Technical and Commercial losses. All the cloud based and predictive analytics solutions given by industries in retail, telecom, and healthcare, have collectively resulted in the rapid growth of the country’s industry and economy. Today as it stands, India has about 600 data analytics firms, in addition to the 100 new start-ups, that have been set up in the year 2015. This clearly reflects on the demand for data scientists in the not so far away future. This is why a number of students have been attracted to the field of data science. As not many generic educational institutes are able to provide the required training, many candidates seek help from professional training institutes like Imarticus Learning, which provide a number of industry endorsed courses in the field of Finance and Data Analytics.
Since the time its popularity hit the roofs, there’s one statement about Big Data that’s remained a constant. “It isn’t about what you know, it is mainly all about what you do, with what you now.” While this may seem as a bit of gibberish to some, industry experts claim that it happens to be a valuable lesson, that companies across the globe will soon end up learning, in the coming years, especially when it comes to the field of Big Data.
Innumerable industry experts among us claim, that 2017 will be the ‘It’ year. The year when data science and big data are bound to go mainstream. Did you know that there are a number of teenagers out there, who are entirely dependent on Google analytics to monitor their brands, regardless of their size. There are a number of parallels drawn between, the thriving start-up culture on one hand, and the increasing developments in the field of predictive analytics and target marketing.
As we are well into the year of 2017, it can be noted, that there are a number of changes in store for Data Science. There are signs of a meaningful shift, gradually taking place when it comes to business and big data. It probably would be the first time, when data analytics, would be the driver of a number of business operations. This change will be a very rewarding proposition for all of those working in the data science industry. While on the other hand, those companies who are lagging behind in this race of technology, could be in for some serious liabilities.
According to Harvard Business Review, “A majority of business outfits today, are nowhere close to recognizing the value and benefits, that data analytics can bring to their firms.” Industry experts believe there happen to be a number of reasons for the same. From lack of communication to absolute absence of a proper, well-designed plan could result into businesses, being entirely oblivious to kind of benefit data analytics can bring. While this news may lead you to panic, there are still a number of things that you, as a business entrepreneur can do and you, as a data science professional can be well aware of.
When it comes to gathering the generated data, almost every single person in the company must buy into the value analytics. If your firm fails to do so, it runs the risk of your company ending up with data, that is either worthless, or enormous amounts of data insights, which will rarely be put to use. Every company and firm out there, needs to make a proper action plan, especially when it comes to the professionals, who are responsible for managing data, reporting it, gathering information, inputting the data and most importantly, who analyzes this data. If these processes aren’t outlined properly, your data will almost never pay.
As the whole world comes to terms with the potential of Big Data and data analytics, there is an increasing need for trained professionals, who are adept in working with data analytics tools. A number of data enthusiasts have begun to look for institutes like Imarticus Learning, which will offer them industry endorsed training programs, in various data analytics tools like R, SAS, Python, Hadoop and so on.
Data Science today has become the most advanced field industry in comparison to all those industries that have existed in the market sphere. One thing that is very evident about this field is that it is ever evolving in nature. This is one of the reasons why a lot of data science experts advice professionals to forever remain on their toes when it comes to the various developments in this landscape.
Mark Twain’s famous line, “Don’t let school interfere with your education” work the very best for all the professionals in this sphere.
As the field of Data Science is fairly new, there are a number of tweaking’s, replacements, additions and newer solutions being introduced here almost on a daily basis also. This is the reason why it makes it so imperative for Data Scientists to be aware of all the newer trends. After getting certified in any one data analytics tool, keeping in touch with the various new developments in terms of other tools and functions, becomes very important for any Data Scientist, who is looking to expand and improve their career. Data Scientists are time and again advised to learn and relearn certain ‘soft-skills’, which will help them stay on top of their game when it comes to the various requirements of the industry.
While technical knowledge is of utmost importance, being able to develop certain professional traits and habits has great benefits Data Science professionals for these. It is said that learning never ends, it is a continuous process. Similarly for a data analyst, keeping up with all the market trends and trying their best to expand their skill set is a per-requisite. This is the very reason, why a number of professionals today reach out to us to help them gain knowledge of other data analytics tools like SAS Programming, R Programming, Hadoop, Python and more. It is always a better bet to add to your laurels than just resting on them. The most crucial parts of being a Data Scientist is not just to have great skills, but also be able to communicate their results very effectively. As this field has expanded from just being IT related to more fields throughout the market sphere, the same is expected out of a Data Scientist. A professional who has all the technical knowledge, but does not have any knowledge of the business perspective, would not be able to effectively deliver the results of the analytics work.
Business strategizing and development are two very important parts of data analytics and until a professional is not able to deliver on the technical as well as the business front, he becomes more of a liability than an asset to a firm. Thus reaching a balance between these two aspects will open up a candidate to huge benefits thus. Apart from working on your soft skills, working on your networking skills can also make a world of difference for all the data scientists out there. Attending a number of conferences and related events, will not only help you learn a trick or two but also will help you gauge current trends and give you a sterling CV.
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The recent couple of years have been witness to this huge amount of buzz created all over the IT world by the concept of ‘big data’, with it more often than not being related to in the print and electronic media. One of the most phenomenal discoveries was that all the data that has been rapidly generated was stored in the entirety of the past decade. For instance, it was in the year 2000 when 25% of the world’s information was being stored in the virtual format. Cut to the current year, more than 98% of information is stored in the digital format in our daily lives. Believe it or not, today, the entire world is generated more than 30,000 gigabytes of data that is almost being generated every second of every day. When you sit to carefully think about this, the change seems to be of almost astronomical proportions. This as it would, in the progression of all things IT, has led to the creation of huge data sets and in a way there has also been the whole issue, about how and where and especially in what manner would the data be stored.
More important than that is the question of what. The answer to what of this data, more specifically the answer to what exactly can be done about this data, lies within two words: Data Scientist. These are professionals who are basically given the responsibility of extracting value out of these huge data sets, thus in a way, assisting in making valuable decisions, for the growth and development of the companies. Today there is no field left that, has not been penetrated by the. The various fields that have been developed, in terms of their efficiency and their functioning, mostly due to the presence of big data are, health, IT, finance, oil and gas and many more.
The newest addition to this ever increasing list, is the field of agriculture. The National Research Council, is of the opinion that something known as, precision agriculture can come into existence due to the introduction of big data. It basically refers to a management strategy, which will be making use of information technologies to collect data, from multiple sources in order to facilitate various quality decisions, when it comes to anything and everything related to crop production. While so far the system of precision agriculture and big data are a little different from each other, in terms of the technical aspects, there are chances of inculcating these two concepts in the near future.
Precision agriculture is still involved, with graphical representations of the field maps, wherein the main purpose is to just identify the areas which would be less nutrient deficient in order to sow seeds. As this concept already has a lot of generation of data in place, it surely will help with the process of data analytics, thus these two fields being complementary. This promises a lot of development in terms of job opportunities for all those great Data Scientists out there.
If you are planning to earn career in data science, then Imarticus Learning offer various business and data analytics courses in both online and classroom mode.