Your essential weekly guide to Data and Business Analytics 

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By 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy, according to Gartner. The march of analytics into the collective consciousness of businesses around the world is unstoppable now, and the implications are far-reaching. From a glut of new skills that employees need to learn, to the shiny new applications of Analytics that’s changing the way humans live, there’s quite a lot of activity going on here. We try to make sense of all that news in our digest that encapsulates the Analytics landscape.

Here are some articles that will take you through recent advancements in the data and analytics domains. 

Businesses Face Three Biggest Challenges While Leveraging Big Data

According to a report from Dun & Bradstreet, the three biggest challenges businesses still face when it comes to leveraging big data are protecting data privacy, having accurate data, and Analysing/processing data. The global big data market was estimated at $23.56 billion in 2015 and now is expected to reach $118.52 billion by 2022.

Big Data & Business Analytics Market to Rear Excessive Growth During 2015 to 2021

Due to the tremendous increase in organizational data the adoption of big data and business analytics has been increased within organizations to better understand their customer and drive efficiencies. Read more to know about Drivers and Challenges of Big Data and Business analytics market. 

‘Jeopardy!’ Winner Used Analytics to ‘Beat the Game’

An aggressive strategy, mathematical finesse, a sharp mind, and a willingness to take risks were some of the factors that spurred ‘Jeopardy!’ game-show contestant James Holzhauer to win 32 consecutive games and rake in more than $2.4 million. Read more to know how this happened. 

The Age of Analytics: Sequencing’s New Frontier is Clinical Interpretation

Today, genomic data is being generated faster than ever before. And those on the frontier of this field are trying to make sure that data is as useful as possible. While the surge in sequencing has benefited many patients, the genomic data avalanche has caused its own problems. Read more about the challenges and proposed solutions to manage and analyze the volumes of genomic data. 

Times Techies: Upskilling is Key to Meeting Demand For Analytics

An exhaustive Nasscom-Zinnov report released last year flags a huge talent demand-supply gap in the artificial intelligence (AI) and big data analytics (BDA) family of jobs. By 2021, the total AI and BDA job openings in India is estimated to go up by 2,30,000. But the fresh employable talent or university talent available will be just 90,000, leaving a huge gap of 1,40,000. 

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Your essential weekly guide to Data and Analytics – July 17

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Stay updated with the Business Analytics applications, and advancements across the globe.

Analytics Talent Growing Fastest in India: Report

The worldwide advanced analytics talent pool is expected to reach 1 million people in 2020 — double of the 2018 level — and much of that is coming from India, says a report by global consultancy firm Bain & Company. Until now, the US was the leading source of advanced analytics-trained talent.

South African Schools Will Soon Get These New Subjects

Data Science & Analytics | Blockchain | Artificial Intelligence | Robotics | Quantum Technologies

The South African government has committed to a ‘skills revolution’ that will give the country human capital required for a digital economy, says President Cyril Ramaphosa. 

This Data Analytics Trend is Turning The Industry on Its Head

According to a new report by analyst house Gartner, Inc., augmented analytics is the most disruptive (and pervasive) data analytics trend right now. In fact, the researcher believes augmented analytics will be the dominant driver of new business intelligence and data science purchases by the end of next year. The prediction comes from Gartner’s paper entitled Top 10 Data and Analytics Technology Trends That Will Change Your Business

Amazon Plans to Spend $700M to Retrain a Third of Its Workforce For Data, Analytics Roles

To keep up with the demand for data scientists and software developers within its own ranks as well as in other industries, Amazon pledged Thursday to spend more than $700 million to upskill 100,000 of its employees, about a third of its U.S. workforce.

How Big Data Can Improve eCommerce for Businesses and Customers

Businesses are taking advantage of the expanding eCommerce market to sell their products or services in a more efficient framework. This is made possible by the collection of vast amounts of information on online purchase activities, which is stored in big data repositories. Read how big data can improve a business’ eCommerce performance and deliver enhanced services to its customers.

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Data and Analytics Weekly Round-up: July 9, 2019

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Here are a few Data and Analytics updates from last week to keep you informed.

4 Challenges with Leveraging Analytics — and How to Overcome Them

To fully capitalize on the potential of modern analytics, enterprises must balance a complex mix of technical, organizational and cultural requirements. With this complexity come possible roadblocks that can hinder efforts to gain competitive advantage and also dilute returns on investments. Read along how to combat them.

Revenues from Big Data and Business Analytics to Hit $260 bn in 2022: IDC

Worldwide revenues for Big Data and Business Analytics (BDA) solutions will reach $260 billion in 2022 with a compound annual growth rate (CAGR) of 11.9 percent over the 2017-2022 period, according to a new forecast from International Data Corporation (IDC)…. [Read More]

What Matters Most in Business Intelligence, 2019

Improving revenues using BI is now the most popular objective enterprises are pursuing in 2019. Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today…. [Read More]

The Coolest Business Analytics Companies of the 2019 Big Data 100

As part of the 2019 Big Data 100, CRN has put together a list of business analytics software companies offering everything from simple-to-use reporting and visualization tools to highly sophisticated software for tackling the most complex data analysis problems…. [Read More]

Top Five Business Analytics Intelligence Trends for 2019

From explainable AI to natural language humanizing data analytics, James Eiloart from Tableau gives his take on the top trends in business analytics intelligence as we head into 2019…. [Read More]


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With Career support, I got to interview with many companies – Sai Ramya Machavarapu, Data Analyst at Mercedes Benz.

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A career transition can be a daunting experience for many. But given the right direction, learning, and support, it is more like a cakewalk. That’s why here at Great Learning, we strive to provide the right learning, practical exposure, and complete career support. 

What has your professional journey been like?

I completed my graduation in Electronics and Communications Engineering from Amrita College, Bangalore. Then I moved to the USA to pursue my Masters in Electrical Engineering from the University of Missouri, Kansas-City in the year 2014. I got placed in Reliable Software Resources as a QA Tester and worked until May 2017. I will be joining Mercedes Benz very soon as a Data Analyst.

How did you develop an interest in Data Science? Why did you choose GL to pursue it?

Previously, I was working as a manual tester for a Consulting firm. The job profile involved manual testing for a project of Banking. The role was very limited and monotonous, so I decided not to go deeper into testing. I left my job and moved back to India. As I was from a non-programming background, I was very sceptical to get into coding and related fields. I was looking into various technologies and was suggested by a friend to consider Data Science as an option. I attended many seminars and workshops on Data Science organized by various companies. I developed an interest in this field and was looking for a classroom course. On the recommendation of the same friend, I joined Great learning to pursue PGP-DSE.

Coming from a non-programming background, was it difficult for you to understand the subjects?

Not at all. As most of the students in the batch were from the non-IT background, the course is designed keeping them in mind. The faculties ensured that the basics were covered. I understood that the course is based on Logics, so I slowly developed pace and contrary to my presumptions, I didn’t find it difficult. The faculty put in a lot of time and attention towards us and even repeated the sessions whenever required.

How was your experience of the academic and career support given by GL?

The team was always available, especially Akhila as she helped us thoroughly in preparing for the interviews and gave regular suggestions and feedback for us to improve at the same. Whenever we had any issues, Akhila and the team resolved them at priority.

With Career support, I got to interview with many companies like CTS, Mercedes, etc. Based on my experience, I realized that the curriculum is self-sufficient to crack any interview. The entire course is designed in a way to help us understand the concepts, crack interviews, and guide during the projects. 

What did you like the most in the program?

We were assigned mini projects on the completion of every topic. This gave a lot of hands-on experience of every topic in terms of understanding and its practical application. This hands-on experience on mini projects gave me a lot of confidence and helped me in exams as it gave a recap of all that we had learned in the course. After the completion of the course, during the capstone project, there were many remedial sessions to clear doubts. 

Share your experience of the interview with Mercedes.

The interview was organized by GL at Mercedes’s Bangalore office. The interview included a total of 3 rounds; 2-Technical and 1-HR. The first round included questions based on whatever I had mentioned in my resume and basic questions over Coding, ML, SQL, etc. 2nd round involved questions related to the Business aspect. The final round was an HR round, where they gave me a confirmation after the interview.

Any advice to aspirants who wish to take this course?

They should be confident in sharing what they know and admit to what they don’t know. Give your 100% to every interview thinking that this is the last opportunity as there is a huge competition in the market. There focus should be in developing a strong foundation of whatever they are learning. The interviews are based on basics and focus to test you in your understanding of the field. So, have a stronghold of basics and you will be good enough to crack through it.


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The placement assistance was excellent – Debashis Gogoi, Data Analyst at Indegene.

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There lies a big challenge among engineering students to pick the right field of specialization and build a successful career within the same. Once you understand the core area of interest, upskilling in the same with a relevant course could be a key to unlock your dream career. Here’s how Debashis did it. 

What is your professional background?

I completed my graduation in Civil Engineering from Royal School of Engineering & Technology. After graduation, I worked for 3 months in National Highway project and Gammon India Pvt. Ltd., Guwahati. Then, I moved to Bangalore to pursue the course in Data Science Engineering. Currently, I am working with Indegene as a Data Analyst.

How did you develop an interest in Data Science and How did you choose GL?

I wanted to pursue graduation in Computer Science Engineering but could not as there was no scope for IT in Assam. Based on the available opportunities, I took a course in Civil Engineering. While working during my internship, I realised that I have a passion for Analytics. So I moved from Assam to Bangalore and took some certification courses from Coursera. Meanwhile, I was looking for Data Science courses and got to know BABI is the No.1 course in India for Analytics. Since the only full-time course was of DSE and it was designed for Freshers like me, I took this course.

How was the overall experience with Great Learning?

It was a very nice experience. Before joining the course, I checked the curriculum and found it was very extensive. DSE is a 5-month program and I believe GL did justice in delivering the basics and in-depth understanding. The faculty members were industry experts and they spent a good amount of time with almost all topics. The management was very supportive and the placement assistance was excellent. From CV reviews to Mock interviews, everything made the students really comfortable and industry-ready.

How was your experience at the interview with Indegene?

I got to participate in the placement drives of 6 companies. I got to interview with 3 companies, namely, Kargil Solutions, Evive, and Indegene. With Indegene, there were 2 interview sessions; 1st was a Case Study and 2nd was a Technical Round where they tested me with my basic ML concepts. After the interviews, they offered me the role of Data Analyst. 

Coming from a non-programming background, how easy was it for you to understand the course?

The course is designed with the first week dedicated to Python. Initially, it was a bit tough but then eventually things got easier as we got acquainted with it. The course and the curriculum are very well designed keeping the diversity of the batch in mind.

Any advice for our future aspirants?

My father always quoted me with “Patience and Perseverance always pay”. Along with it, working hard, being focused, and believing in oneself will help anyone achieve the best out of the program. I will suggest them to practice more and participate in a lot of competitions.


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GL helped me to kick-start my career – Yeknath Merwade, Associate Analyst at Ugam Solutions

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One needs career support the most when they are a fresh graduate. The right direction and support at the right time help multifold in shaping a successful career. What kind of support did Yeknath get? Read on:

What has your professional background been?

I completed my Graduation in Electrical, Electronics & Communications Engineering in 2018 from Belagavi, Karnataka. I then took a course in Data Science at Great Learning, Bangalore and currently, I am working in Ugam Solutions as an Associate Analyst.

How did you develop an interest in Data Science?

I finished my graduation with 58% aggregate score. With this score, I was not eligible to attend interviews for any good role or company. I understood the need to upskill myself as my father suggested me to read about Data Science which has created a lot of buzz. After researching online, I developed an interest in it and got fascinated with what this field can do.

Why did you choose GL to pursue a course in Data Science Engineering?

After viewing the scope and growth opportunities, I immediately started to search for courses. But to choose the best out of them was a task in itself. All I wanted was to take a classroom program as for a fresher it was better compared to online training. I visited GL’s website for weeks and saw it was regularly updated with relevant data and testimonials. I checked the reviews on Google and LinkedIn as well. Finally, I looked at the faculty profiles on LinkedIn and saw their experience. I understood that GL is the best institute in India to study Data Science, so I took up the course here. 

What did you like the most in the program?

There were many things that I loved about the program.

  1. The Faculty: Since I looked at the LinkedIn profiles of almost all the teaching professionals, I got to know that they all were Industry experts and had a great experience in their respective fields. When I enrolled myself for the course, I was surprised to see how grounded and friendly they were. Also, they taught us everything from scratch. 
  2. Course-Curriculum: The course is well designed and well structured. The curriculum is exhaustive and gave me a good understanding of the domain. The course includes what is needed by the industry and everything is accommodated in the syllabus.
  3. Career Assistance: I got to sit on campus drive of 7 companies and got shortlisted in all of them. Apart from this, the CV reviews and Mock Interviews helped me develop confidence and crack interviews. Also, they organized Bootcamps for the students and helped us in all aspects. There were ample opportunities and it got us placed.

Overall it was a nice experience as I got good friends and faculty with whom I learned a lot and I am still in touch with them. I feel very grateful to GL, that helped me to kick start my career.

Being from a non-programming background, did you face any issues with the course or the transition?

Initially, it was very hard for me to adjust to the syllabus as I was not at all familiar with Coding or programming. The first week of the course started with Python, which was a new thing for me. Here, I would like to mention that the teaching faculty boosted my confidence by mentioning that “It is not rocket science and is easy to learn”. After the EDA session, I felt self-motivated and realised that irrespective of any branch, one can achieve success in their ventures. Slowly things started to fall in place. I was in regular sync with sessions, and the regular exams and quizzes kept us in constant touch of the topics. In the end, everything was good and great.

Share your experience of interviewing with Ugam?

I had 4 rounds of the interview; An SQL Test of 30 minutes duration, followed by a Case Study of 30 minutes duration again, a Technical round and finally an interview session with Vice President and HR. The technical round involved questions around my Project mentioned in the Resume and general technical questions to check my understanding of algorithms. With the VP, the interview was to check how my understanding can contribute to the Analytics team of Ugam and general questions from the HR. After the interview, I received a job confirmation from them. 

Any advice to our future aspirants of this course.

I would like to suggest to prepare well on Stats and SQL. The material is self-sufficient and includes in-depth content and curriculum. The placement assistance is superb and helps everyone in getting placed. So there is no need to panic for anything. Also, focus on your project as all my interview questions revolved around my Capstone Project.


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I got to interview with 3 companies – Pushpendra Nathawat, Programmer Analyst at Cognizant

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Finance has evolved to position itself as an important business function. Given the nature of this domain, it overlaps with analytics in many areas. Finance professionals and executives are finding new ways to leverage from this overlap and increase the value of this vertical in their organizations. 

What is your professional background?

I had completed my MBA from Tapmi School of Business in the year 2015. I then joined Vodafone and worked as a Relationship Manager for 10 months. I switched to HDFC and worked for over 1.75 yrs as an Assistant Manager. Currently, I am working with Cognizant as a Programmer Analyst.

Why did you think of upskilling? Why did you choose Great Learning?

I did an MBA with Finance as my specialization and while working with HDFC, I enrolled myself in Financial Risk Course with IIM Kashipur. Though I had good knowledge in Finance Domain, I had no understanding of Coding or Data Science. I felt the need to upskill and checked for the courses. While searching I found high recommendations for GL. So I left my job in Jaipur and moved to Bangalore to pursue a full-time program in Data Science Engineering with Great Learning.

What did you like most about the program?

The management, staff, and the faculty, everyone was very helpful. The faculty took a great deal of interest in teaching students and gave a good explanation of every topic. The management was very supportive in providing any assistance whenever the batch needed extra sessions or special classes for having a better understanding of the programming subjects. 

How was your overall experience at Great Learning?

Since I was from a non-programming background, initially it was a bit difficult to follow the specific modules. But later with the help of faculty, I could cope up with the subjects and it became easier to understand and manage. The faculty was very helpful in providing material and guidance, especially in my lacuna. They took extra effort in organizing classes over those areas during the weekends. Since I was very new to Data Science, I had to improvise a lot in terms of my CV & Interview performance. The Career assistance provided by GL helped me prepare an impressive CV & mock interviews prepped me to crack interviews.

Share your experience of Career fair organized by GL?

I got to interview with 3 companies; Kinara Capital, Credi India, and Cognizant. I cleared the interview with CTS which involved 3 rounds; 2 in Technical of 45 minutes duration each and 1 HR round of interview on the same day. The technical interviews involved testing my knowledge of Machine learning. I got the job confirmation on the same day.


Upskill with Great Learning’s PG program in Data Science Engineering and unlock your dream career.

Big Data Analytics Skills To Boost Your Salary

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Are you wondering what the buzz about big data is? Should you make a transition to a career in big data analytics? Well, whether you are already in data analytics or IT, you have come to the right place. As the buzz around big data gathers momentum, more and more professionals are gearing up to improve their employable skills in big data analytics.

Why so, you may wonder.

It is a data-driven world today where more and more businesses are implementing analytics in a big data ecosystem. A Gartner survey shows that more than 75 % of businesses are investing in big data. Whether it is the banking and financial industry, insurance, eCommerce, health sector or travel, all of these are grappling with voluminous data in real-time. Making sense of this data is the domain of the big data analyst.

As the always-connected environment in everyday life gains traction with more and more connected devices, data is generated in huge volumes at great speeds in real-time. Big data analytics has emerged as a leading disruptor in most sectors, powering data-driven insights for transforming customer experiences. Big data is here to stay, and the best way to carve a high-paid salary in analytics is by walking the big data path!

Why is there a need for Big Data Analytics skills?

With the rapid implementation of data analytics in critical business operations and decision making, there has been an increase in the demand for big data experts among other analytics professionals. Individuals with the right set of Big Data analytics skills will have an immense opportunity to pursue a rewarding career in analytics across industries.

The top-paying industries for data analytics professionals globally are:

– Financial Services

– Manufacturing

– Software as a Solution (SaaS)

– Healthcare

Source: RHT’s Salary Guide, 2019

For some organizations, big data analytics plays a vital role in decision making. Other areas of application include customer relationship, enabling key strategic initiatives, risk management, and improved financial performance among others.

The soaring demand for big data analytics professionals shows that the market is ready for such technologies. The growth in job opportunities against a lack of supply has created a gap that needs to be filled by skilled professionals and organizations are not shying away from highly rewarding the professionals with the right mix of big data analytics skills.

According to the latest industry reports, big data analytics salaries are at an all-time high and expected to get better. Entry-level salaries begin at Rs 7.5 lakhs p.a. and go up to Rs 12.10 lakhs p.a. at the senior levels. According to an Edvancer-AIM study on analytics career scope, the median salaries offered are Rs 10.5 lakhs p.a, while 40% of advertised analytics jobs offer salaries of more than Rs 10 lakhs p.a. The percentage by hiring across tools and skills records the highest for R statistical tool at 36 %, followed by Python at 30%; and Hadoop and SAS at 20% each.

At the same time, it is about your data analysis competency and logical reasoning. The more you learn the technical and open source frameworks dedicated to handling big data analytics, the better is your chance to get a salary that you have always dreamed of.

There are multiple job roles that require professionals adept with data analytics skills in demand. If seen from the career point of view, there are a lot of opportunities for big data professionals in different domains and type of work.

The most common job roles under big data analytics umbrella

– Big Data Analytics Business Consultant

– Big Data Analytics Architect

– Big Data Engineer

– Big Data Solution Architect

– Big Data Analyst

– Analytics Associate

– Business Intelligence and Analytics Consultant

– Metrics and Analytics Specialist

Here are the top five big data analytics skills that will likely boost your salary:

1. Apache Hadoop

The Apache Hadoop is an open-source project that allows fast processing and insights into huge volumes of structured and unstructured data. The Hadoop has emerged as the driving force behind the growth of big data analytics, with spin-offs in a BI environment. The powerful big data platform offers the Hadoop expert the ability to leverage the central elements of the Hadoop stack for deep and fast analytics. So learning the Hadoop framework, together with the underpinning programming models, such as the Map Reduce, Hive, Pig, and HBase is critical to earning analytical brownies in big data analytics.

2. Apache Spark

In-memory computing enables high-performance algorithms for faster processing. The capability of end-to-end insights in real-time with sophisticated ‘what if’ simulations, has seen increased adoption of Apache Spark by big data practitioners. As mastering the Spark is considered a challenge for many analysts, the professional who is equipped with skills in Apache Spark is sure to command a salary on his terms.

3. Machine Learning and Data Mining

Machine learning is an AI technology that powers big data analytics by learning rules iteratively for improved analytics. Getting trained in machine learning algorithms and rule defining for spotting anomalies and patterns is a popular demand in sectors like banking, finance, and trading. The ability to build predictive analytics in a big data engine for personalized customer experience is a highly sought-after skill.

Data mining is a technique that integrates structured and unstructured data from multiple disparate channels for big data analytics. It can pull any data, including social media, for a 360-degree view of the customer. If you are armed with the skills of data mining, you can bring value to a business. In turn, you can demand the top salary for your data mining skills.


4. NoSQL

Distributed, scale-out NoSQL powered databases continue to be the rage. NoSQL database skills power big data analytics from huge data, with quick iterations and coding. Its many advantages over the traditional RDBMS have pushed NoSQL to the forefront. The distributed architecture of NoSQL framework allows high-performance big data analytics at massive scale, making it almost indispensable in recent times. So the analytics professional armed with working knowledge of one or more NoSQL databases can certainly boost his salary.

5. Statistical Tools

The power of statistical reasoning cannot be underestimated, especially for analytics presentations for businesses. Statistical tools like R, SAS, Matlab, SPSS, or Stata are long-time favoured tools for enterprise big data analytics. Analytical tasks with massive data can be handled with ease, such as coding with social media analytics; data mining, clustering, and regression models. The advantage of R as an open-source model lends its ability to be paired with other technologies and big data products. The statistical tools are a great learning curve for those who want to work as quants, or in the field of retail, insurance or market research.

How to develop big data skills?

After knowing everything about the scope of big data, the available job roles and the most useful tools to learn, the most important question that arises is how to learn all the skills needed to become a successful big data analytics professional. Professionals need to carve out a clear cut roadmap for themselves and follow it ardently.

Along with the technical skills, certain business skills need to be learnt too, which include:

– Problem-solving techniques

– A strong base of theoretical knowledge

– Hands-on implementation ability

Other skills that would help professionals to perform better in their respective roles are:

Data Visualization skills: The ability to correctly interpret data by visualizing it is crucial. A certain understanding of scientific and mathematical concepts gives an edge to better interpret the data and communicate it to stakeholders. The sense of creativity while visualizing data is an added advantage.

Analytical Skills: Having a strong knowledge of analytics tools is great but being familiar with the business domain is beneficial for analytics professionals to better understand the data and analyse it accurately keeping in mind the end goal and vision.

Programming Knowledge: The ability to code along with statistical and quantitative analysis is an important skill in the realm of big data analytics. A fair knowledge of object-oriented languages, data structures, and algorithms go a long way.

The best way to learn big data analytics skills is to upskill with relevant programs provided by reputed ed-tech firms like Great Learning.


In the race to secure the best salary in big data analytics jobs, single skill development is not enough. Each of the above skills has its own merits. So plan a gradual scale-up of big data analytics learning, with all the five skills, to fetch you a good analytics position. The salary package and career graph in the big data analytics landscape have never been better! So go ahead, and arm yourself with the necessary technical skills. Learn big data with big data certification and online training programmes on-the-go, together with your studies or present job. Get trained on the most relevant big data analytics tools that best fit your career trajectory. Strengthen your analytical skills in a big data environment with suitable weekend programmes, and domain-based management skills for that salary package booster. So if you are a student or analytics newbie, there is no time like the present.

7 Domains Benefiting from Big Data and Machine Learning

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The wonders of Big Data and Machine Learning are already dazzling the world. Whether it is driverless cars, quick-wit from robots, or Facebook chatbots that had to be shut down, it is safe to say that no aspect of our life stays untouched from AI and Machine Learning. Having said that, there are several fields that are making key progress in leveraging Big Data to revolutionize the way they function, conduct business, and more importantly the way they envision the future. Here is a glimpse of each:

Manufacturing For a long time, the manufacturing industry was associated with a slew of problems – health risks, worker unions, poor optimization methods, and what not! However, technology and Big Data Analytics (BDA) to be precise emerged as a game changer and is now taking the factories and production units to the next level. Big Data Analytics and Machine Learning are making factories smarter with computerization of manufacturing processes, optimizing quality checks, improving accuracy and quantity of production, and promoting 3D printer factories and MaaS (Manufacturing-as-a-Service) with better collaboration. Read more

Healthcare Apart from helping hospitals and companies to cut down on costs and increasing profits, analytics has been instrumental in improving the quality of life by helping to diagnose diseases, determining the most effective course of treatments, and decreasing the overall mortality rate. As Charles Doarn, director of the Telemedicine and e-Health Program at the University of Cincinnati puts it, “Our healthcare system is in desperate need of reform, and technology is one of the tools that can help. It can be a paradigm shift in how we practice medicine.” Read more

Government Initiatives Thanks to the Union budget 2018, the NITI Aayog will initiate a national program to direct efforts in Artificial Intelligence, and the Department of Science and Technology will launch a Mission on Cyber-Physical Systems to support the establishment of centers of excellence for research, training, and skilling in robotics, artificial intelligence, digital manufacturing, big data analysis, quantum communication, internet of things, etc. Find out how government initiatives will help the government revamp manufacturing and commerce, banking, healthcare, cybersecurity, and even town planning. Read more

Finance and Banking How do machine learning and artificial intelligence impact the financial industry? The financial industry in India or the Banking, Financial Services and Insurance (BFSI) sector in India is a fast-evolving one. How then, do banks and associated organizations save time, costs and yet add value to their operations for smooth functioning? In India, Artificial Intelligence (AI) has begun to play a major role in solving some of the most vital problems faced by both companies as well as customers. Not just banks, but nearly every company whether public or private in BFSI has started using AI for Robo-advisory, risk management and fraud detection, sophisticated high-end trading, and providing superlative customer experiences, etc. Read More

Delivery of Public Services The nation is on a massive-path digitalization. It is, currently, being realized through the Digital India mission. Today, more than 980 million Indians have AADHAR cards, 700 million own mobile phones, and more than 300 million have access to broadband internet connection. This transforms into a massive data set that has the potential to actively transform the public services delivery system. Find out the top 3 to-dos for the Indian government to transform its public delivery system.

Supply Chain Management Supply chain is a natural choice when it comes to Big Data finding its applications. From improving delivery times by synchronizing shipments to identifying better ways to reduce the communication gap between manufacturers and suppliers, today, Big Data Analytics is working as an evolutionary catalyst for the supply chain management to analyze consumer behaviors and habits, improve customer experience by personalizing it, streamlining e-commerce, and managing and distributing inventories exceptionally. Read more

E-Commerce While many industries are still at the nascent stages of figuring out what to do with the huge amount of data at their disposal, e-Commerce is one industry that is already reaping the rewards of their Big Data initiatives. Major players in this industry rely heavily on their team of data scientists to compete in this fiercely dynamic space. The key ways e-Commerce companies are deriving value from Big Data analytics are by personalizing offers, running promotions or big discount days, inventory management, and optimizing pricing by introducing real-time pricing. Read more