It is July 1st, 2017 – the epic day when GST, aka The Goods and Services Tax comes into effect, simplifying the whole tax collection procedure of the nation. From today, there will be a single tax on the supply of goods and services that will replace all other state and central levies. GST is pegged to be one of the most impressive economic tax reforms implemented by PM Narendra Modi to take Bharat to the summit of transparent digitization.
Data is crucial. While it ushers in a greater transparency and simplified tracking through data, it also unleashes the requirement for Data Analytics and ERP solutions. Besides, GST includes several billing software and payment gateway channelization, triggering plenitude of job opportunities in the IT sector. Reports say it is going to be a $1 billion opportunity for IT vendors over the next two years. Quite a lot to think about!
Spurred by advanced Analytics and Big Data technologies, Healthcare industry is going towards a major transformation, of course for the good! The catalyst here is none but our very own, our most favorite Big Data – it is robustly opening all the doors of health and medical science, and the possibilities seem endless.
Electronic Health Records have been around for sometime – numerous systems of variable reliability have been designed to ensure data is more easily accessible as well as transferable between the healthcare professionals, institutions and whatever it is for better patients’ care. With Big Data, scientists are coming up with improved sophisticated methods of incorporating the derived information with the data from innumerable number of health-related sources. The main objective is to make the best use of the relevant information in consultation with the doctors and patients to serve in the best way possible.
Nowadays, plenty of veritable companies provide systems which not only help in providing the doctors a detailed study of a patient’s medical history but also supply with data that can be used largely for fine treatment purposes. Highlighting correlations between different medical conditions inaccessible before, sparing insights into how these conditions may be influenced by other factors, like treatment methods or in which part of the world they are taking place are some improvements to be witnessed now.
As estimated, 75% of healthcare data is generated from unstructured sources like clinical notes, laboratory tests, emails, telematics, digital devices, imaging and third party sources. This data revolution is brought to you by Big Data, and this is how you can derive the best of its benefits:
Reduce fraud, abuse and waste
We all know how fraud, abuse and waste have been spiking healthcare costs, thanks to data science, the tides are changing now. To ascertain abuse and fraud, insurers require the expertise to analyze large unstructured datasets related to historical claims using machine learning algorithms.
Improve outcomes, embrace Predictive Analysis
Predictive Modeling is helping the health world in detecting the early signs of life threatening diseases, like sepsis. The availability of a vast pool of patients’ data means Predictive Analytics would find not only similar symptoms but also will curate a similar response to a specific medication.
Healthcare Internet of Things
The Internet of Things (IoT) is the aggregation of the increasing number of smart, interconnected, technology-efficient devices and sensors that share data over the internet. In healthcare, IoT refers to the devices that monitor almost all kinds of patient behavior, right from blood pressure to ECG. As per statistics, spending on healthcare IoT could cross $120 billion mark in the coming four years and the possibilities are quite high.
Minimum costs but better patients’ recovery rates
Through data convergence, stream processing and application agility, full-scale digital transformation is now possible in the medical world. Improving patients’ diagnosis is a new milestone achieved in the field of medicines and it has only been possible due to advancement in data science.
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With state-of-the-art technology looming on the horizon, the $150-billion Indian IT industry has a high appetite for workers accomplished in the fields, like AI, Data Science, Big Data, and more.
Soon, it wouldn’t be enough to flash an engineering degree or some minor knowledge in Java or Python – the need for data science and artificial intelligence is on the rise. Automation is going to be the key to change. Globally, 12% of employers have started thinking of downsizing their workforce owing to technological advancement. Amidst all this, don’t think India would be spared. Indian bosses fear automation will reduce their headcount too. But fret not, it’s not all a bad news – there is always a silver lining after rains and that is Big Data jobs.
Shine bright with Big Data
In India, the number of job openings in the Analytics field almost doubled from the last year. Digital natives, like Amazon, Citi, HCL, IBM, and Accenture are waiting to fill close to 50000 positions, according to a study conducted by Analytics India Magazine and Edvancer. All these definitely signify parting off the dark clouds, and I can’t agree more!
Artificial Intelligence and Machine Learning are building a base of its own. Moreover, AI is deemed to be the hottest technical sector in the next 5 years and would beam in success. Along with top-of-the-line tech firms, more than 170 startups have transfixed their gaze on this field. To surf on the next wave of IT jobs, candidates need to step aside from low-in-demand stale skills to excel on budding Analytics skills. Every single HR Manager out there is seeking professionals who can manipulate algorithms and work wonders in various machine-learning models and you can be one of them!
Get better, get evolved
Expertise in languages, like Java/C/C++ gives you a certain edge, but to enter the dominating field of Big Data, techies will be asked to master intricate languages, such as Scala and Hive that are less conventional. Millennial recruiters are also looking out for those who have a keen insight for good design and flawless code architecture. “Programmers who focus on good design principals are always preferred over programmers who can just code,” Rajat Vashishta, founder of Falcon Minds, a resume consulting firm, says. “User experience matters a lot more than it used to, say, five years ago.”
Where skills in technology, like business intelligence, artificial intelligence, machine learning and DevOps are flourishing, minute attention need to be given on proper implementation of these skills, according to Aditya Narayan Mishra, chief executive officer of CIEL HR Services, a recruitment firm, otherwise all of it would be a total waste.
It’s all in the layout
Presentation matters, you agree or not! Make your resume ready to strike the job criteria you are applying for. For example, if a user interface developer wants to become a full stack developer, he must mention back-end programming skills in the profile. This will give an instant boost to the resume. The design of a resume has also changed over the years. Now, the shorter your resume the better response you get. “Most techies write pages and pages of projects in their resumes. While it is important, in most cases, the same information gets repeated. Anything above two pages is a big no,” says Vashishta.
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Big Data is the new big name in the present tech industry. Day by day, it is burgeoning and becoming capacious for companies, including corporate, SMBs and budding startups. It is also the major reason for better opportunities for people, who want to explore newer career realms across sectors, such as healthcare, banking, education, government, retail and manufacturing.
The current IT industry is passing through a jinxed phase, where a lot of layoff fears are on the airwaves but the field of analytics remains largely unaffected. In fact, the number of analytics jobs in the past one year has nearly doubled, as per a report by Analytics India Magazine – a platform for big data, analytics and data science and Edvancer Eduventures – an online analytics training institute. The Analytics & Data Science India Jobs Study 2017 has predicted nearly 50000 positions related to analytics are at present available to be filled in India.
With the Fourth Industrial Revolution looming ahead, many would think that we are already in a digital economy era. Well, somewhat it holds true even. There are countless new apps and software programmes that help people hail a cab, make reservations in a hotel or mop floors by using robotic technology. Smart machines have become really smart to do a plethora of highly adept jobs, which would have been a little bit difficult on the part of humans to perform.
“While technology has long been developed to serve specific business needs, we are now in an era where people are central to the design and development of technologies,” stated Bhaskar Ghosh, group chief executive, Accenture Technology Services. In a recent interview with a leading financial magazine, he talked over Accenture’s Technology Vision 2017 and gave snippets about the latest trends and innovations that have become a pre-requisite to achieve success in the more-than-ever digitised economy.
DexLab Analytics is proud to announce that Tanmoy Ganguli, a proficient Data Analyst who has a long standing experience in Credit Risk Modelling, SAS and regression models is joining our Gurgaon institute as Program Director. Here are some excerpts from an interview we conducted, where he talks about the various challenges he faced in his career and the rapid development of Data Analytics.
Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL
Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments.
Machine Learning:You should be able to explain K-nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python. These algorithms show to employers that you have exposure to how data science can be used in more practical manners.
Data Wrangling: You should be able to clean up data. This basically means understanding that “California” and “CA” are the same thing – a negative number cannot exist in a dataset that describes population. It is all about identifying corrupt (or impure) data and and correcting/deleting them.
Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them)
Software Engineering: You should know algorithms and data structures, as they are often necessary in creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc.
What they look for? @ Mu-Sigma, Fractal Analytics
Most of the analytics and data science companies, including third party analytics companies such as Mu-sigma and Fractal hire fresher’s in big numbers (some time in hundreds every year).
You see one of the main reasons why they are able to survive in this industry is the “Cost Arbitrage” benefit between the US and other developed countries vs India.
Generally speaking, they normally pay significantly lower for India talent in India compared to the same talent in the USA. Furthermore, hiring fresh talent from the campuses is one of the key strategies for them to maintain the low cost structure.
If they are visiting your campuses for interview process, you should apply. In case if they are not visiting your campus, drop your resume to them using their corporate email id that you can find on their websites.
Better will be to find someone in your network (such as seniors) who are working for these companies and ask them to refer you. This is normally the most effective approach after the campus placements.
Key Skills that look for are-
Love for numbers and quantitative stuff
Grit to keep on learning
Some programming experience (preferred)
Structured thinking approach
Passion for solving problems
Willingness to learn statistical concepts
Technical Skills
Math (e.g. linear algebra, calculus and probability)
Statistics (e.g. hypothesis testing and summary statistics)
Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.)
Software engineering skills (e.g. distributed computing, algorithms and data structures)
Data mining
Data cleaning and munging
Data visualization (e.g. ggplot and d3.js) and reporting techniques
Unstructured data techniques
Python / R and/or SAS languages
SQL databases and database querying languages
Python (most common), C/C++ Java, Perl
Big data platforms like Hadoop, Hive & Pig
Business Skills
Analytic Problem-Solving: Approaching high-level challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
Effective Communication: Detailing your techniques and discoveries to technical and non-technical audiences in a language they can understand.
Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve problems.
Industry Knowledge: Understanding the way your chosen industryfunctions and how data are collected, analyzed and utilized.
Interested in a career in Data Analyst?
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Big data come with big promises, but businesses often face tough challenges to determine how to take big advantage of big data and deploy the effective architecture seamlessly into their system.
From descriptive statistics to AI to SAS predictive analytics – every single thing is spurred by big data innovation. At the 2017 Dell EMC World conference, which took place on Monday, the chief systems engineer for data analytics at Dell EMC, Cory Minton – gave a presentation simplifying the biggest decisions an organisation need to make when employing big data.
Let’s get started with 6 questions that every organization should ponder over before stepping into the tech space:
Buy or build?
Do you want to buy a successful data system or build one right from the scratch? Minton said, though buying offers simplicity and a shorter time to value, it comes at a hefty price. The building idea is good and provides huge scale and variety, but it is very complicated, and interoperability is one of the biggest issues faced by admins, who take this route.
Teradata, SAS, SAP, and Splunk can be bought, while Hortonworks, Cloudera, Databricks and Apache Flink are used to build big data systems.
Products like Oracle, Hadoop MapReduce and Apache Spark offers batch data – they are descriptive and can manage large chunks of data. On the other hand, Products like Apache Kafka, Splunk, and Flink creates potential predictive models, coupled with immense scale and variety.
Kappa or lambda architecture?
Twitter is the best example of lambda architecture. This kind of architecture works best as it gives the organisation access to batch and streaming insights along with balances lossy streams, as said by Minton. While, kappa architecture is hardware efficient and Minton recommends it for any newbie organisation starting fresh with data analytics.
Ask your employees, about what kind of security platform they are comfortable working, and then decide.
Physical or virtual?
Minton said – a decade ago, the debate surrounding virtual or physical infrastructure used to gain more momentum. Now, things have changed. Virtualization has become so competitive that sometimes it outdoes physical hardware. Today, it stresses more on what works for our infrastructure rather than individual preferences.
Minton said Direct-attached storage (DAS) is the only way to initiate a Hadoop cluster. Today, the tides are changing; with increasing bandwidth in IP networks, the Network-attached storage (NAS) option is becoming more feasible for big data implementation.
DAS is easily initiated and the model works well within software-defined concepts. NAS is efficient in handling multi-protocol needs, offers functionality at scale and addresses security and compliance issues.
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