There is a lot of growth and evolution that is witnessed in the field of data analytics in the recent years. One has observed a fast growth of Machine Learning and Deep Learning, and hence you will see a big change in the trends of 2018 for Business Intelligence and Data Analytics. The entire focus is on Automation, to automate action and decision making, and replace the mundane tasks. Times are also witnessing a deep penetration of Internet of Things and Big Data Analytics, in the global business environment, which altogether has sparked the need for evolved Business Intelligence Systems that further work towards automation.
According to a report by Gartner, the market for Data Analytics, more specifically the Business Intelligence Market is expected to grow to $20.81 Billion by 2018. The sudden Business Intelligence growth is influenced by many factors. like,
- Organisations are increasingly tapping opportunities to leverage streaming data generated by devices, to make faster, relevant and real-time decisions.
- Data Analytics will include Cloud Deployments of BI and Data Analytics platforms which have the potential of reducing the cost of ownership and aid speedy deployment. Thus according to Gartner, the majority of new license buying will be in the cloud deployments by 2020.
- BI and the Analytics space will garner a lot of interest and will see a growth spurt as there is an availability in the marketplace, where buyers and sellers can collaborate to exchange analytic applications, or grouped data sources, custom visualization and algorithms.
- There is also a need for business users to analyse, large and complex combinations of the data source and data models. This needs to be done faster than before, in a more automated method for expanded use.
In the year 2018, Artificial Intelligence, Cloud Computing, Internet of Things, and several Business Applications, together will reshape the way IT functions in every business and industry.
While there is a lot of speculation, the sudden needs and changes in BI and Data Analytics have brought along some challenges as well,
- As we are discovering new and smart data analytics and augmented analytics, one basic struggle still exists of keeping up with the huge volumes of data. In the traditional systems as well, a huge percentage of data was underutilized, which already raised questions on the usefulness of the analytics system. With the current platforms on which BI data is hosted, the data is not only huge but pours from diverse sources, it would be interesting to understand how the risk of under usage is mitigated.
- With advanced Machine Learning Predictive Models, there is always a threat that it will replace the very minds that initiated it. The data professionals, data scientist, and the data programmers and the data analyst, might be replaced by self-service business intelligence.
Even though the challenges exist, with certainty it can be confirmed that in the year 2018…….
- Augmented Data Penetration will see popularity as it will enable the non-IT staff to pursue data testing tasks.
- Predictive analytics will thrive due to smart data discovery, making it the most preferred business analytics activity.
- Many organisations and business will invest in Business intelligence and Data Analytics platform, and this trend will be observed across industries.
The developments in BI and Data Analytics will not only improve data visibility and comprehension but will also reduce costs while producing better results.
Data Analytics, Big Data, Data Scientist, these are no longer big terms from a far away profession, these words or rather roles are becoming catalysts, impacting the growth of our businesses and enhancing the overall experience we get in doing our daily tasks.
Our online presence is not a matter of choice anymore; we often find ourselves using online portals to shop, connect with a doctor, research, basically from going on a vacation to preparing for motherhood, marriages, and dating, to banking, and even school and college admissions, all of these are done online, we even use social networking to express ourselves, through tweets, posts etc…,
Excessive usage of the internet creates online activity logs that contain humongous amounts of data.
Now imagine the camera’s mounted almost on every corner of the street and satellite based observations like the google map and google earth, they also collect data in large numbers on how people conduct themselves.
This data that is generated is being collected in large numbers around the clock, in real time and historic, this data further needs to be extracted, however, it is easier said than done, data is huge and extraction and explanation of the same cannot be done effortlessly. Most of the data collected is unstructured and not authentic, so you need to be wise to catch the correct characteristics at the right time.
People who can perform this extraction in a functional manner and make sense of it are called, Data Scientist or Data Analysts. The competencies that help them in this task are, sound knowledge of Mathematics, Computer Science, and Statistics.
The job of a data scientist is not only extracting data and analysing it, but to clean the data in such a manner that they can also predict and forecast trends for an assigned business, based on certain hypothesis or conditions. And that is the uniqueness they get to their job, the ability to accurately pre-process data and predict and forecast, sets one data analyst apart from the other.
A career in big data has become a dream choice for most job seekers these days, there is a lot that an organisation can achieve with the right application of data science. Some companies have identified this, and are either training their internal staff on the skills required to perform the job, while others are not yet too open to hire a full time resource. Although that day is not too far when the position of a data analyst will become imperative in every organisation.
If you are planning to enter the data science industry to make a great career in big data, then you need to adapt and acquire certain competencies and expertise in data analytics related tools, in addition to the above mentioned prerequisites. For example, programming languages, like R, and Python, SAS, a working knowledge of Machine Learning, and Predictive analysis. Also a sound knowledge in the industry you plan to work for, e.g., healthcare, or IT, Education etc.., will be an added advantage.
There is a huge gap between the demand and available resources in the field of data science, hence making a career shift in this direction would be wise and also lucrative, recent researchers have suggested that a data scientist earns more than experienced engineers. Clearly, this is a field with huge potential.
Do take up certifications, that will further assist you to springboard yourself in the field of data science.
Breakdowns make us intensely mindful of our reliance, innovation additionally has made it super simple to arrange on LinkedIn, enthusiastically eat up tweets on Twitter, and remain associated on Facebook. To deal with the expanding reams of information that innovation tosses out with lightning speed you require a SAS master or two. This data analytics tool is by far known to be one of the best tools for data science.
In the following ten years, the rundown of provocative employments will incorporate analyst.
Particular parts are difficult to mark since each association has their own particular part definition in light of complex needs. Be that as it may, here are some normal parts you can hope to discover as you look. You can likewise discover more SAS forte parts on the web.
- Business expert
- Clinical information software engineer
- Information Analyst
- Information Quality Steward
- Information Scientist
- Information stockroom designer
- Database manager
- Database software engineer
- ETL authority
- Monetary expert
- IT Manager
- Advertising expert
- Stage Administrator
- Software engineer
- Venture Manager
- Quality expert
- Report software engineer
- Announcing Analyst
- Securities investigator
- Measurements developer
- Frameworks/organize developer
Like whatever other work ability, there are 2 ways to deal with enter/begin an examination profession:
Approach 1 – Get contracted by an organization which trains you (on work/inside training) on the fundamental abilities. These eventual organizations which have Analytics in their DNA and utilize it for their everyday choices. While this approach is better from long haul viewpoint, it requires some investment and venture (particularly if there is no organized preparing in the organizations). A portion of the organizations known for utilizing bleeding edge Analytics (in India) are:
Innovation pioneers: Google, Facebook, Linkedin
Saving money, Financial Services and Insurance (BFSI): Capital One, American Express, ICICI, HDFC
Telecom organizations: Idea, Vodafone, Airtel
Examination Consultancies: Fractal, Mu-Sigma, Absolutdata, ZS Associates
One of the backup way to go to get into these organizations can be entry level positions. So in the event that you have a 2 – half year break, give an attempt to turning into an understudy in these organizations.
Approach 2 – Get Business Analytics related affirmation: While these confirmations furnishes you with the specialized abilities required, these would not have the capacity to make up for involvement at work.
SAS Learning is popularly becoming the best data analytical tool which a lot of IT freshers prefer to learn today. This is mainly because of the fact that this programming tool has been in the industry for quite a long time and has a great base of clients.
For individuals with work understanding, different driving scholastic organizations run confirmation courses. I had shrouded them in more points of interest in my past post here. On the off chance that you have the required involvement and assets, I would suggest the course from ISB.
For freshers, there are confirmation courses keep running by SAS preparing establishment, Imarticus Learning, This is one very esteemed institute which offers a number of industry endorsed courses in SAS Programming as well as other data analytics tool and verticals in finance.
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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We continue hearing measurements about the growth of data. For example:
• Data volume in the ventures will grow 50x year-over-year amongst now and 2020.
• The volume of business data around the world, over all organizations, copies at regular intervals.
• In 2010, Eric Schmidt broadly expressed that each 2 days, we make as much data as we did from the beginning of human progress up until 2003.
How might you utilize this data further bolstering your good fortune?
On the off chance that you need to exploit this data, you should initially start putting away it some place. In any case, how might you store and process enormous informational indexes without spending a fortune on capacity?
That is the place Hadoop becomes an integral factor.
Hadoop is an open-source programming structure for putting away and handling huge informational collections. It stores information in a disseminated mould on bunches of ware equipment and is intended to scale up effortlessly as required. Hadoop enables organizations to store and process huge measures of information without buying costly equipment.
The colossal preferred standpoint of Hadoop: It lets you gather data now and make inquiries later. You don’t have to know each inquiry you need replied before you begin utilizing Hadoop.
When you start putting away data in Hadoop, the potential outcomes are huge. Organizations over the globe are utilizing this data to take care of enormous issues, answer squeezing questions, enhance income, and the sky is the limit from there. How? Here are some genuine cases of ways different organizations are utilizing Hadoop further bolstering their good fortune. Machines produce an abundance of data–much of which goes unused. When you begin gathering that data with Hadoop, you’ll learn exactly how helpful this information can be.
Here’s another awesome illustration: One power organization joined sensor information from the shrewd matrix with a guide of the system to foresee which generators in the lattice were probably going to bomb, and how that disappointment would influence the system in general. Utilizing this data, they could respond to issues before they happened.
Do you ever ponder what clients and prospects say in regards to your organization? Is it great or awful? Simply envision how helpful that information could be in the event that you caught it.
With Hadoop, you can mine online networking discussions and make sense of what individuals consider you and your opposition. You would then be able to examine this data and settle on continuous choices to enhance client discernment.
Money related administrations organizations utilize investigation to evaluate chance, form speculation models, and make exchanging calculations; Hadoop has been utilized to help fabricate and run those applications.
Retailers utilise it to help examine organized and unstructured information to better comprehend and serve their clients.
In the benefit concentrated vitality industry Hadoop-controlled investigation is utilized for prescient support, with contribution from Internet of Things (IoT) gadgets bolstering information into huge information programs.
Media communications organizations can adjust all the previously mentioned utilize cases. For instance, they can utilize Hadoop-controlled examination to execute prescient upkeep on their foundation. Enormous information investigation can likewise design proficient system ways and prescribe ideal areas for new cell towers or other system development.
Data Science and Machine Learning are mostly used synonymously; most people also believe one is a trendy word for another.
Data Science is in some sense an umbrella of techniques used to extract information and get better insights to the available data. The range of this type of analysis varies from something as elementary as MIS reports on the one hand and on the other, an intense scientific approach where techniques such as getting inferential analysis, predictive analysis, descriptive analysis, exploratory analysis and so on are considered.
Machine Learning can be explained as an essential part of Artificial Intelligence. Machine Learning empowers the computers to get into a self-learning mode, eliminating the need for overt programming. With the help of new data being fed into the system, these computers can then learn information, adapt to the required changes, learn and develop all by themselves. They are not human dependent for improvement. Automation of the later part of data mining can be called as Machine Learning.
Machine Learning is not a new term, it has been around for a while, some common applications include, web search, spam filters, credit scoring, online recommendation engines, cyber fraud detection or some advance recent development like the automated google car, however, the ability to automatically learn and develop and apply mathematical calculations to the big data is only currently getting impetus.
Why does Machine Learning matter?
Like all fields which aid development, Machine Learning is also constantly evolving. And as a custom approach to development comes the rise in importance and the demand. One can say Machine Learning in imperative to Data scientist as it helps them drive high-value predictions that can help arrive at better decisions and help take the right actions most importantly in real time, to be effective, and to do this with as minimal human intervention as possible. It eases the task of the data scientist in an automated process and hence is gaining a lot of importance.
Availability of massive data increases the difficulty in analysing it, hence increase in data is directly proportionate to the problems associated with bringing in predictive models that work appropriately. You see a statistical analysis is limited to understanding samples that are static, as a result with time it could give inaccurate conclusions or solutions.
As a knight in shining armour enters Machine Learning which is able to give good solutions to analysing the data in huge volumes. Machine Learning is a leap forward from other available applications like, statistics, computer science, etc.., Machine Learning will help produce real results and analysis through the development of effective and efficient algorithms and data-driven models for real-time processing of data.
Machine learning and Data Science will be partners working together. This is the ability of the machine to gain knowledge from data, so without the data, there is very little that machines can learn to do. Thus, it gives a push to get valuable data in order to get valuable and accurate solutions or predictions. So the increased use of machine learning will act as a catalyst to give higher importance to data science. In future, basic levels of machine learning will become a standard operating for a data scientist.
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.
Any successful innovation is a result of a good measure of disruption. Over this, you must add a very generous helping of talent and a lot of creativity to go into the mixture and finally a splash of intuition and you’re good to go.
While this may be the probable recipe for any innovation, but does it also happen to be the recipe for a successful innovation? That seems to be a different story altogether, because usually the one thing that any successful innovation depends on, is whether it meets or exceeds the assigned business goals.
It also finally boils down to the inclusion of big data into the mix. It is this ingredient that would help you ensure that your innovation is very successful. This is how you will figure out the coveted je ne sais quoi for your success. So one must remember to always get out their big data analytics tools and crunch some data in order to get amazing results.
Innovativeness, responsiveness, and resilience happen to be the trifecta of deriving business agility. These happen to be the core business drivers, which when put together, you have a great picture of how businesses deal with change.
Analysing the information that you have gathered or that is at your disposal, will help any organization deal much better with change as a result of a thorough optimization process. All a professional is required to do is gather all the data that is available on anything that they are doing, crunch all the numbers and go on to make recommendations on what all changes need to be made in order to ensure the betterment of the process.
While data analytics may be supremely efficient in making human processes more efficient, it has also experienced one flaw. That flaw as surprising as it may sound is humans. This would be more clear as the processes become complex. For example, when the data grows, it inadvertently means that the need for analysing the same also grows. But the downside here is that people, as a rule, have a limited attention span. So when it comes to analysing information, this attention span can prove detrimental in the processing of the information. So in a way no matter how good and exemplary your data analytics tool is in giving off results, it is redundant if there happens to be no one to read them or even understand them.
This is the reason why in order to eliminate the weakest link, there are many organizations which are trying to establish totally automated feedback loops. For instance take a firm which is responsible for managing giant amounts of data, from airports to factories and data centres. While the traditional approach to handling any kind of glitches here would be, drawing up of a number of reports, based on mathematical formulas and adjust the maintenance schedule accordingly. On the other hand, if there are mechanisms that would know beforehand when the glitches would occur and correct them way before happening, this would be a better arrangement.
This is why Data Analytics is becoming the most sought after profession, with many data aspirants trying to get professionally trained from Imarticus Learning.
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Today’s society is absolutely in throes of a pulsating data revolution. Every kind of information out there, including information about the most minute of details, like for instance, the life forms thriving in the smallest inch of earth, is available online. This very information is rapidly being converted into data, that is accessible and can be read by machines. New ways are being discovered, to make great progress, across a number of fields and industries, all of which are on the basis of analysing of data. Many experts belonging to the field of Data Science believe, that the future holds a lot in store especially when it comes to innovation with data. As technology progresses, the world finds better ways to adapt, in terms of collecting, storing and analysing data. It won’t be long until the time when our economies and societies are fully data driven.
While data is gradually becoming one of the key drivers of the 21st Century economy. Data Science and Data Analytics, both these concepts have shown incredible potential, for stimulating innovations as well as progress in various different areas of the industry. Since its emergence, people belonging to the IT sector or other related industries, have been able to understand the complexities of the world, in a much better way, which has then prompted them to make better decisions, for the growth and development of their respective firms. As the world and humankind evolves, it gets more complex and the advent of technology is ensuring that we are able to make sense out of this melange of complexities. Take for example cars manufactured by the Tesla company, which specializes in manufacturing self driven cars and is also trying to make use of non-conventional energy resources in the place of fuel and gas for cars. From a lay perspective, this is what is meant by Data Driven Innovation.
Big Data, probably is the most used term in the corporate world these days. It basically refers to the process of various firms and companies, which gather enormous amounts of information, in the form of large data sets, which are frequently updated to be further analyzed and used, in order to make value based decisions for the furthering of the companies. In the earlier days, companies and firms especially in the service sector, had to host a number of surveys and take into account samples (set of people for an experiment) and then make assumptions, as to how it would impact the larger society. With the introduction of Data Science and Big Data, companies no longer have to collect data sporadically, as they can very well generate their own data. The generation of their own data may possibly depend on a number of sources, but one of the most popular sources would be inviting consumers to provide feedback on the various, different products and services they provide. Another very important source of data is the Internet of things, which is a term that is used in reference to a number of devices, which are wirelessly connected to any particular network. This is how data driven innovation has been taking place and as the area for this development increases, the number of people wanting a make career out of Data Science also multiplies.
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The field of Artificial Intelligence seems to working on a winning streak. In the year 2005, the U. S Defence Advance Research Project Agency, held the DARPA Grand Challenge, which was supposedly held to spur development of autonomous vehicles, basically in order to make self-driven, smart cars. This challenge was taken up and successfully completed by 5 teams. In the year 2011, in a great competition of Jeopardy, the IBM Watson system, was successfully able to beat two long time, human champions of the same legendary game. Another great win of technology over the human race would be in the year 2016, when Google DeepMind’s AlphaGo system was able to successfully defeat the world champion of Go Player, who was reportedly the world champion for 18 consecutive times.
While these feats of technology over the human brain are extremely commendable, today the long surviving dream of humans, which basically revolved around developing technology to control their surroundings, has finally come to fruition. This has resulted in the form of Google’s Google Assistant, Microsoft’s Cortana, Apple’s Siri and Amazon’s Alexa. As a result of all of these AI (Artificial Intelligence) powered virtual assistants, people are able to make greater use of technology in order to live better lives.
Artificial Intelligence is considered to be a field of computer science, which is entirely devoted to the creation of computing machines and systems, all of which are able to perform operations that are similar to human learning and decision making. According to the Association for the Advancement of Artificial Intelligence, AI is, “the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines.” While these intelligence levels can never be compared to those of the humans, but they can certainly vary in terms of various technologies.
Artificial Intelligence includes a number of functions, which include learning, which primarily includes a number of approaches such as deep learning, transfer learning, human learning and especially decision making. All of these functionalities can later help in the execution of various fields such as cardiology, accounting, law, deductive reasoning, quantitative reasoning, and mainly interactions with people, in order to not only perform tasks, but also to learn from the environment.
While the recent changes may be extremely mind blowing, the promise of AI has always been existing since era of electromechanical computing, this began in the time period, after the World War 2. The first conference of Artificial Intelligence was held at the college of Dartmouth in the year 1956 and at that time, it was said that AI could be achieved within the time period of summer. Later on, in the 1960’s there were scientists, who claimed that in the next decade, it would be possible to see various machines controlling human lives. But it was in the year 1965, when the Nobel Laureate, Herbert Simon, who is supposed to have predicted the words, which would have some substance and which were, “In the next 20 years, it would be possible that machines would be able to do any work of labour that man can”.
With Artificial Intelligence, going in full fervour, the field which it has affected most in the field of Data Science. And as there are many who believe that there is a great to achieve in this field, have begun to get trained in the same by approaching professional training institute – Imarticus Learning.