Taking into consideration the positive trends of Data Science from previous years, there lies an immense well of possibilities that awaits us in the future, that is, the upcoming year 2020. Some of these Data Science Trend Forecast for 2020 can be foreseen as follows:
Augmented Analysis
Complicated code and extensions will no longer be required to get deep insights from data. The augmented analysis helps layman users/analysts (in machine learning/data science) to make use of AI to analyze data. This will change the way data is consumed, created and shared across all data-intensive fields. Already several BI and analytics tools are trying to implement AI assistance full force in their platforms.
Continuous/ Real-time Intelligence
There is intensive activity ongoing every second in real-time platforms. If through some method, one can plug into this data, real-time user experience can be enhanced manifold. Continuous or real-time intelligence aims to do just that by analyzing data in real-time so that instant results can be provided to the user while he is still surfing the platform. It can also help increase profit margins by re-aligning the platform as per the observed interaction of the user.
NLP
Natural Language Processing is a very important segment of Artificial Intelligence since most real-world data are in text or voice format. To process such data, advanced NLP techniques are required which are being innovated with each passing data. Today, we can read, understand, classify and even create unique text documents with the help of machines. Further developments like intelligent summarization, entity recognition and task management using text input and much more are expected to happen, owing to the intense research and increasing data-science experts choosing NLP specialisation.
Conversational technology
There has already been a visible surge in the performance of voice assistants in 2019. In 2020, it is expected to further improve such that the conversational systems become more sensitive to the human language and also more humane in their response. By more humane, it will mean that the systems can keep track of previous responses and questions (which is not a very developed feature in any voice assistant in the market to this day). Also, most client interactions are expected to be taken over by conversational technology, thus, increasing response rate and efficiency.
Explainable AI
The last decade has seen massive growth in AI aided decisions for sure, but it has been a persistent problem to be able to explain these decisions or why the AI wants to go a certain way instead of another. Recently, however, a lot of research has increased the scope of explainable AI. 2020 can further be invested in understanding problems like say, how and why a certain neural network arrived at a certain decision. This will indefinitely increase the faith of clients on adolescent technology.
Persistent memory/ In-memory computation
In-memory computing or IMC can deliver extremely high-performance tasks due to optimized memory architecture. It also has become more feasible due to the decreasing expense of memory which owes credit to constantly emerging innovations.
Data Fabric
Data Fabric helps in the smooth access and sharing of data in distributed environments. It is usually custom made and helps in the transfer and store of data, data pipelines, APIs and previously used data services that have a chance of being re-invoked. Trusted and efficient data fabric can help to catalyze data science pipelines and reduce delays in customer-client interaction/iterations.
Advances in Quantum Computing
The research in Quantum Computing has a very high momentum at the moment. Even though the whole architecture of Quantum computing is at a very basic stage, increased investments and research are helping the field to grow by inches every passing day. A quantum computer is said to perform calculations which will take general computers a few years, in just a few seconds! As remarkable as it sounds, it can bestow superpowers to mankind! Imagine munching on years and years of historical data to arrive at conclusions about the future in just a few seconds. A whole lot of astonishing things await us, and we must be blessed to be a part of this century.
It is expected that India’s job openings in the analytics sector will double to about 200000 or two lakh jobs in 2020. Here is what 2020 for job seekers in data science will look like:
Fields like finance, IT, professional services and insurance will see a boom in demand for data science and analytics.
Having analytics skills like MapReduce, Apache Pig, Machine learning and Hadoop can provide an edge over other competitors in the field. The most fundamental in-demand skills will be Python and Machine Learning. Statistics is an added advantage.
Vacancies for roles like data developers, data engineers and data scientists will go over 700,000 by 2020.
The most promising sectors that will tend to create increasing opportunities include Aviation, Agriculture, Security, Healthcare and Automation.
The average salaries in India in development roles like Data Scientist or Data Engineer will range from 5 to 8 Lakh per annum.
The average salaries in India in management/strategizing roles like data architect or business intelligence manager will range from 10 to 20 Lakh per annum.
As exciting as all of it sounds, there is always a bag of unforeseen advancements that are bound to take us all by surprise, as has always happened with Data Science and AI in the past. So, hold tight for yet another mind-boggling ride through the lanes of technology this 2020!
Data Science has seen a massive boom in the past few years. It has also been claimed that it is indefinitely one of the fastest-growing fields in the IT/academic sector. One of the most hyped Trends in Data Science this year was that the sector saw a major hike in jobs as compared to the past years!
Such an unprecedented growth owes all its dues to the unimaginable benefits that artificial intelligence has brought to the plate of mankind for the very first time. It was never before imagined that external machines could aid us with such sophistication as is present today. Owing to this, it is imperative that an individual, irrespective of his/her calling, must have at least a superficial knowledge about the past advances and future possibilities of this field of study. Even if it is the job of scientists and engineers to figure out solutions using machine learning and data science, the solutions, undoubtedly is bound to affect all our lives in the upcoming years. Moreover, if you are planning to plug into the huge well of job openings in data science, exploring the past and upcoming trends in this field will surely take you a step ahead.
Looking back on the achievements of the year 2019, there is much which has happened. Here is a brief glimpse of what Trends in Data Science of 2019 looked like:
Accessible AI
The once-popular belief that AI technology was only meant for high-scale and high-tech industries, is now an old wives’ tale. AI has spread so rapidly across every phase of our lives, that sometimes we do not even realize that we are being aided by AI. For instance, recommendations that we get on online forums are something we have become very used to in recent times. However, very few have the conscious knowledge that the recommendations are regulated by AI technology. There are also several instances where a layman can use AI to get optimized outputs, like in automated machine learning pipelines. We even have improvised AI-aided security systems, music systems and voice assistants in our very homes! Overall, the impact of AI in everyday lives saw a massive boost in 2019, and it is only bound to increase.
The rapid growth of IoT products
As was already forecasted, the number of machines/devices which came online in 2019 was immense. Billions were invested in research to back the uprising IoT industry. Today it is nothing out of the ordinary to control home appliances like television and air conditioners with our smartphones or lock our and unlock our cars from even the opposite end of the globe. Bringing devices online not only makes the user experience far smoother but also generates crucial data for analysis. With such data, several unopened gates can be explored across several domains. The investments and count of IoT devices are expected to go up at an increasing rate in the upcoming years.
Evolution of Predictive Analysis
The concept of predictive analysis is to use past data to learn recurring patterns, such that it can predict outcomes of future events based on the patterns learnt. Today, with increasing data it becomes extensively important to make use of optimized predictive solutions. Big data comes into picture here and significant advancements have been made in 2019 about it. Tools like PySpark and MLLib have helped scale simple predictive solutions to extensive data.
Migration of Dark Data
Dark data is very old data which has probably been sitting in obsolete archives like old systems or even files in storage rooms! There is a general understanding that such unexplored data can show us the way to crucial insights about past trends which can help grab useful opportunities and even avoid unwanted loopholes. Therefore, there has been visible initiatives to make dark data more available to present-day systems with the help of efficient storage and migration tools.
Implementation of Regulations
In 2018, General Data Protection Regulation (GDPR) brought in a few data governance rules to emphasize the importance of data governance. The rules were laid down so fast that even at the year-end, several companies dealing with data are still trying to comply wholly with all the principles laid down. These principles have not only created a standard for data consumption and data handling domains but are also bound to shape the future of data handling with great impact.
DataOps
DataOps is an initiative to bring in some order in the way the data science pipeline functions. It is essentially a reflection of agile and DevOps methods in the field of data science. In 2019, it has been one of the major concerns of management in data science to integrate DataOps into their respective teams. Previously, such integration was not possible since the generic pipeline was still in making or under research. However, now, with a more robust structure, integrating DataOps can mean wonders for data science teams.
Edge Computing
As stated by Gartner, Inc. cloud computing and edge computing has evolved to become a complementary model in 2019. Edge computing goes by the concept of “more the proximity (or closeness to the source of computation), better is the efficiency”. Edge computing allows workloads to be located closer to the consumers and thus, reduces latency several-fold.
There is, however, a huge recurring gap when it comes to the need and availability of skilled people who can launch and contribute to these developments significantly. India contributed to 6% of job openings worldwide in 2019, which scales to around 97000 jobs!
The job trends of 2019 looked as follows:
BFSI sector had a massive demand for analytics professionals, followed by the e-commerce and telecom sectors. The banking and financial sectors continued to have high demand throughout.
Python served as a great skill to attract employers to skilled job seekers
A 2% increase in jobs offering over 15 Lakh per annum was observed
Also, 21% of jobs demanded young talent in data science, a great contrast to all previous years. 70% of job openings were for professionals with less than 5 years of experience.
The top in-demand designations were Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultants, Statistical Analyst and Hadoop Developer
Big data skills like Hadoop and Spark were extremely in demand due to the growing rate of data.
Telecom industry saw a fall in demand for data science professionals.
The median salary of analytics jobs was just over 11 Lakh per annum.
Now unless you’ve been a hermit or a monk living in total isolation, you will have heard of Amazon Web Services and AWS Big Data. It’s a sign of an emerging global market and the entire world becoming smaller and smaller every day. Why? The current estimate for the cloud computing market in 2020, according to Forbes (a new prediction, highly reliable), is a staggering 411 Billion USD$! Visit the following link to read more and see the statistics for yourself:
To know more, refer to Wikipedia for the following terms by clicking on them, which mark, in order the evolution of cloud computing (I will also provide the basic information to keep this article as self-contained as possible):
This was the beginning of the revolution called cloud computing. Companies and industries across verticals understood that they could let experts manage their software development, deployment, and management for them, leaving them free to focus on their key principle – adding value to their business sector. This was mostly confined to the application level. Follow the heading link for more information, if required.
PaaS began when companies started to understand that they could outsource both software management and operating systems and maintenance of these platforms to other companies that specialized in taking care of them. Basically, this was SaaS taken to the next level of virtualization, on the Internet. Amazon was the pioneer, offering SaaS and PaaS services worldwide from the year 2006. Again the heading link gives information in depth.
After a few years in 2011, the big giants like Microsoft, Google, and a variety of other big names began to realize that this was an industry starting to boom beyond all expectations, as more and more industries spread to the Internet for worldwide visibility. However, Amazon was the market leader by a big margin, since it had a five-year head start on the other tech giants. This led to unprecedented disruption across verticals, as more and more companies transferred their IT requirements to IaaS providers like Amazon, leading to (in some cases) savings of well over 25% and per-employee cost coming down by 30%.
After all, why should companies set up their own servers, data warehouse centres, development centres, maintenance divisions, security divisions, and software and hardware monitoring systems if there are companies that have the world’s best experts in every one of these sectors and fields that will do the job for you at less than 1% of the cost the company would incur if they had to hire staff, train them, monitor them, buy their own hardware, hire staff for that as well – the list goes on-and-on. If you are already a tech giant like, say Oracle, you have everything set up for you already. But suppose you are a startup trying to save every penny – and there and tens of thousands of such startups right now – why do that when you have professionals to do it for you?
There is a story behind how AWS got started in 2006 – I’m giving you a link, so as to not make this article too long:
OK. So now you may be thinking, so this is cloud computing and AWS – but what does it have to do with Big Data Speciality, especially for startups? Let’s answer that question right now.
A startup today has a herculean task ahead of them.
Not only do they have to get noticed in the big booming startup industry, they also have to scale well if their product goes viral and receives a million hits in a day and provide security for their data in case a competitor hires hackers from the Dark Web to take down their site, and also follow up everything they do on social media with a division in their company managing only social media, and maintain all their hardware and software in case of outages. If you are a startup counting every penny you make, how much easier is it for you to outsource all your computing needs (except social media) to an IaaS firm like AWS.
You will be ready for anything that can happen, and nothing will take down your website or service other than your own self. Oh, not to mention saving around 1 million USD$ in cost over the year!If you count nothing but your own social media statistics, every company that goes viral has to manage Big Data! And if your startup disrupts an industry, again, you will be flooded with GET requests, site accesses, purchases, CRM, scaling problems, avoiding downtime, and practically everything a major tech company has to deal with!
Bro, save your grey hairs, and outsource all your IT needs (except social media – that you personally need to do) to Amazon with AWS!
And the Big Data Speciality?
Having laid the groundwork, let’s get to the meat of our article. The AWS certified Big Data Speciality website mentions the following details:
The AWS Certified Big Data – Specialty exam validates technical skills and experience in designing and implementing AWS services to derive value from data. The examination is for individuals who perform complex Big Data analyses and validates an individual’s ability to:
Implement core AWS Big Data services according to basic architecture best practices
Design and maintain Big Data
Leverage tools to automate data analysis
So, what is an AWS Big Data Speciality certified expert? Nothing more than an internationally recognized certification that says that you, as a data scientist can work professionally in AWS and Big Data’s requirements in Data Science.
Please note: the eligibility criteria for an AWS Big Data Speciality Certification is:
Minimum five years hands-on experience in a data analytics field
Background in defining and architecting AWS Big Data services with the ability to explain how they fit in the data life cycle of collection, ingestion, storage, processing, and visualization
Experience in designing a scalable and cost-effective architecture to process data
To put it in layman’s terms, if you, the data scientist, were Priyanka Chopra, getting the AWS Big Data Speciality certification passed successfully is the equivalent of going to Hollywood and working in the USA starring in Quantico!
Suddenly, a whole new world is open at your feet!
But don’t get too excited: unless you already have five years experience with Big Data, there’s a long way to go. But work hard, take one step at a time, don’t look at the goal far ahead but focus on every single day, one day, one task at a time, and in the end you will reach your destination. Persistence, discipline and determination matters. As simple as that.
From whizlabs.org
Five Advantages of an AWS Big Data Speciality
1. Massive Increase in Income as a Certified AWS Big Data Speciality Professional (a long term 5 years plus goal)
Everyone who’s anyone in data science knows that a data scientist in the US earns an average of 100,000 USD$ every year. But what is the average salary of an AWS Big Data Speciality Certified professional? Hold on to your hat’s folks; it’s 160,000 $USD starting salary. And with just two years of additional experience, that salary can cross 250,000 USD$ every year if you are a superstar at your work. Depending upon your performance on the job! Do you still need a push to get into AWS? The following table shows the average starting salaries for specialists in the following Amazon products: (from www.dezyre.com)
Top Paying AWS Skills According to Indeed.com
AWS Skill
Salary
DynamoDB
$141,813
Elastic MapReduce (EMR)
$136,250
CloudFormation
$132,308
Elastic Cache
$125,625
CloudWatch
$121,980
Lambda
$121,481
Kinesis
$121,429
Key Management Service
$117,297
Elastic Beanstalk
$114,219
Redshift
$113,950
2. Wide Ecosystem of Tools, Libraries, and Amazon Products
From slideshare.net
Amazon Web Services, compared to other Cloud IaaS services, has by far the widest ecosystem of products and tools. As a Big Data specialist, you are free to choose your career path. Do you want to get into AI? Do you have an interest in ES3 (storage system) or HIgh-Performance Serverless computing (AWS Lambda). You get to choose, along with the company you work for. I don’t know about you, but I’m just writing this article and I’mseriouslyexcited!
3. Maximum Demand Among All Cloud Computing jobs
If you manage to clear the certification in AWS, then guess what – AWS certified professionals have by far the maximum market demand! Simply because more than half of all Cloud Computing IaaS companies use AWS. The demand for AWS certifications is the maximum right now. To mention some figures: in 2019, 350,000 professionals will be required for AWS jobs. 60% of cloud computing jobs ask for AWS skills (naturally, considering that it has half the market share).
4. Worldwide Demand In Every Country that Has IT
It’s not just in the US that demand is peaking. There are jobs available in England, France, Australia, Canada, India, China, EU – practically every nation that wants to get into IT will welcome you with open arms if you are an AWS certified professional. And look no further than this site. AWS training will be offered soon, here: on Dimensionless.in. Within the next six months at the latest!
5. Affordable Pricing and Free One Year Tier to Learn AWS
Amazon has always been able to command the lowest prices because of its dominance in the market share. AWS offers you a free 1 year of paid services on its cloud IaaS platform. Completely free for one year. AWS training materials are also less expensive compared to other offerings. The following features are offered free for one single year under Amazon’s AWS free tier system:
The following is a web-scrape of their free-tier offering:
AWS Free Tier One Year Resources Available
There were initially seven pages in the Word document that I scraped from www.aws.com/free. To really have a look, go to the website on the previous link and see for yourself on the following link (much more details in much higher resolution). Please visit this last mentioned link. That alone will show you why AWS is sitting pretty on top of the cloud – literally.
Final Words
Right now, AWS rules the roost in cloud computing. But there is competition from Microsoft, Google, and IBM. Microsoft Azure has a lot of glitches which costs a lot to fix. Google Cloud Platform is cheaper but has very high technical support charges. A dark horse here: IBM Cloud. Their product has a lot of offerings and a lot of potential. Third only to Google and AWS. If you are working and want to go abroad or have a thirst for achievement, go for AWS. Totally. Finally, good news, all Dimensionless current students and alumni, the languages that AWS is built on has 100% support for Python! (It also supports, Go, Ruby, Java, Node.js, and many more – but Python has 100% support).
Keep coming to this website – expect to see AWS courses here in the near future!
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