Payment Options – Online PG Program in Business Analytics (PGP-BABI Online)

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Great Lakes’ Online PG Program in Business Analytics and Business Intelligence (PGP-BABI Online) provides you with the ultimate payment flexibility so you can plan your year with us without having to worry about financing options. Our program offers the following payments flexibility:

  1. Easy Installments – After the initial admission fee payment, we divide the rest of the course fee into 3 equal installments to minimize your burden of one-time payment. In essence, you pay the fee in a total of 4 installments.
  2. Easy Payment Options – Haven’t got the time to visit a bank? Or Can’t find your checkbook? No problem. Great Learning also provides you the flexibility to pay through debit/credit cards and net banking. We also accept demand drafts and cheques. Fuss-free, right?
  3. Pre-Approved Education Loans (Financial Aid) – We strongly believe that money should not become a roadblock in the way of your learning. That’s why we have tie-ups with several lending partners like HDFC Credila, Avance Education, and Zest Money (0% EMI) providing financial help at a substantially lower rate of interest than the market.
  4. Fee Waiver on One-Time Payment – Great Learning also offers a fee waiver up to INR 10,000/- for candidates who opt for paying the entire program fee in one go.

With the online PGP-BABI’s flexi-payment options, you no longer need to wait for a fulfilling and rewarding career in Business Analytics. Join us now!

Why Should You Pay 4 Lacs for our Business Analytics Program (PGP-BABI)?

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From a hairpin to a house, no matter what you pay for, the price and quality often demand the perfect balance in order to seal the deal. Our PGP-BABI (Great Lakes PG Program in Business Analytics and Business Intelligence) is no exception. We are filled with queries about why our program comes at a certain fee when there are so many programs in the market across the cost spectrum. So, here are 3 candid responses from our top PGP-BABI alumni stating why and how the program made it worth their while!

LoveKush Singhania

Several of my friends had pursued an MBA program from Great Lakes Institute of Management and vouched for the institute’s commitment to quality education and excellence. The foundation to my trust in Great Lakes was laid with such feedback. Personally, I wanted to pursue a course/program that was comprehensive and well-structured to gain sufficient knowledge in Analytics. PGP-BABI is highly structured and it conducts sessions on the applications of analytics in various domains. That helped me gain a lot of knowledge about the applications of analytics in different sectors. The predictive modeling techniques taught were highly relevant and the faculty members are experts in their domains. While 4 lacs is no small amount to pay for a course, I knew if I worked hard, I would be able to recover the cost of the program in less than a year. I would say I made quite a data-driven decision in pursuing PGP-BABI from Great Lakes!

Vaibhav Kukreja

One of the primary reasons for enrolling in the program was blended learning where I could leverage the benefits of classroom learning while continuing with my job. I wanted a real physical classroom experience where I could interact with the faculty and my peers. While classroom is the most popular format in India, leaving your job and pursuing it is not for everyone. A blended format like that of PGP-BABI’s is perfect and the program schedule and assistance in learning was spot on. Also, Great Lakes was one of the first institutions to offer a program in analytics, so I was confident that the program would meet all my criteria of a good program. The second reason was the world-class faculty. I researched about the faculty and realized that they are the best in the business. Last but not the least, the diversity in my batch helped me a lot in peer learning and networking.

Pranav Mohan

My major concern was pursuing a program alongside my job. Honestly, there were several cheaper programs in the market (mostly available in online format) so I did weigh in my options before applying to Great Lakes PG Program in Business Analytics and Business Intelligence. The distinguishing factor was the industry exposure through live industry sessions and guest lectures that convinced me to go ahead. The faculty is also a blend of academic knowledge and industry experience. I knew I would not be studying theory in the typical sense as done in most courses, but apply it to real business problems. The fact that I could interact with industry leaders (with several years of experience) one-on-one during classroom sessions and approach them without hesitation, in case I ran into doubts or had questions was the dealmaker for me. In hindsight, I paid for an overall experience. I had an active analytics resume in the making, thanks to all the industry projects and the longer capstone project I did. All this added to my domain knowledge while preparing me for analytic job roles and interviews. A win-win situation!

Top 5 Examples of How the Aviation Industry is Using Analytics

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In our 5 Ways Analytics is Taking the Aviation Industry to New Heights blog, we drew your attention to the advent of analytics and how it has penetrated the aviation industry right from tackling airport congestion to digitising services across customer and operational aspects to predicting and automating maintenance tasks. In continuation, we present you five case studies that provide insights into how analytics is catapulting growth and innovation in the airline business:

    1. Improving operational performance for an airlines major by segmentation of various airports – Mu-Sigma developed an improved email targeting mechanism for one of the largest US airlines with 700 fleet size and 250 destinations, using a test and learn engine for better engagement. It helped the client by increasing both response and conversion rates by approximately 20%. Learn more about their 3-pronged approach.
    2. Aerospace Company Saves $26 Million – A large aerospace and defence manufacturing group wanted to grow, yet contain costs at the same time. A Gartner engagement helped deliver the vision and exceed cost savings expectations. The company was able to achieve $26 million in cost savings and a $5 million overachievement. Know how Gartner exceeded expectations to make this happen.
    3. Recommendation engine to lift ancillary sales for a leading airline – The marketing team of a leading airlines organisation uses a recommendation engine developed by a third party to offer ancillary services to passengers who check-in through web or kiosk. The team wanted to improve the performance of this recommendation engine in order to increase revenue from ancillary services. Mu-Sigma worked on improving the existing recommendation system leading to a 30% increase in revenue. Read more about their solutions here.
    4. Lufthansa Consulting Projects – Lufthansa Consulting is a leading consultancy for aviation-related solutions that works on a variety of projects across all dimensions of the aviation industry. In the last 30 years, Lufthansa Consulting has worked on several cases, problems ranging from revenue optimisation, commercial drone operations, intercontinental passenger services to developing financial models for airports. The diversity in their projects is testimony to the complex system that aviation operates in. Here are some of the projects that Lufthansa Consulting worked on.
    5. Built a custom framework to measure customer satisfaction and improve revenue for a leading airline – The Customer Experience Team of a leading airline serving more than 300 destinations wanted to accurately measure, track and improve the overall airport experience for its customers. Lack of accurate information/score was hindering the process, thereby leading to potential revenue loss. The self-service dashboard provided by Mu Sigma was capable of helping leaders and ground staff drive customer-centric decisions in time. Know how.

Sources: Mu-Sigma, Gartner, Lufthansa Consulting.

5 Ways Analytics is Taking the Aviation Industry to New Heights

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One domain that has never failed to dazzle us with the wonders of technology is the aviation industry. A testimony to how far human invention can go in breaking barriers, a validation to the successful union of science and creativity, and a symbol of unprecedented growth, the aviation industry is now leaping to the next level by integrating big data analytics with its core. All dimensions of aviation today are benefiting from using business analytics from operations management (flight and ground operations) to maintaining and engineering commercial and business aircraft services to airport profitability and benchmarking. Here are 5 facets of the aviation domain that are seeing rapid transformation thanks to the usage of advanced analytics:

    1. Airport Congestion – Airport traffic is a global trend, increasing by the day, threatening congestion glitches even as major airports across the world expand their operations and use technology to counter the losses incurred due to accommodating overcapacity. Analytical experts and data scientists are successfully using parameters such as terminal capacity, runway bandwidth, flight routes, passenger numbers, types of aircraft, ticket prices, etc. to identify patterns and compare them to the busy but not-so-constrained airports of the world. According to McKinsey, “In Brazil, aviation traffic has been growing fast for the past decade, and annual passenger traffic is expected to more than double by 2030, reaching more than 310 million passengers. Not surprisingly, airspace congestion is a growing concern. To deal with the problem, Brazil is introducing a system that harnesses GPS data to optimise the use of available airspace, enabling less separation between aircrafts and shorter routes.”
    2. Digitising Services – Thanks to big data analytics, several digitising efforts and initiatives are seeing the light of day spanning over a wide range of customer and operational needs. Real-time performance dashboards, unmanned vehicle-to-vehicle communication, predictive maintenance in operations are the many gifts of analytics aiding rapid digitisation in aviation. Building customer experiences through customer relationship management and pricing models is on the rise for all aircrafts trying to make profits and cut costs. However, several roadblocks stand in the way of digitisation. Major ones are a lack of transparency as aviation consists several complex networks with many vendors, a lack of consensus between different players targeting different goals, and regulatory constraints from government bodies.
    3. Aircraft Maintenance – According to Boston Consulting Group, fewer technicians remain on the maintenance hangar floor, as they spend more time on problem-solving. In place of logging inspection statuses compliance documents manually, diagnostic algorithms and inspection robots are being used to record all compliance and maintenance information automatically. Drones and robots are used to not only inspect every area of the aircraft but also reduce the risk that comes with humans accessing danger zones. Part replacements and maintenance activities are carried out faster with consistency improving safety for those handling sensitive activities, accurately detecting defects and predicting maintenance, and eliminating time wastage.
    4. Market Leadership – Every major player wants a piece of the pie but increasing production costs and supply shortage can make it harder to sustain a profit percentage for long. Experts in aviation are most worried about ROI as 70% of the cost goes into airplane design and engineering, procuring and assembling, testing, and simulations. Advanced analytical tools and collaborative capabilities are enabling companies to recover costs by customer preference management, discount targeting, effective pricing flexibility models, etc. Few airports also track their passenger cellphone locations for tailoring flight information and managing operations. A simple example would be analysing individual walking speeds to reduce security queues in real time using predictive analysis. Text message alerts for change in boarding information like departure gates or shopping suggestions using frequent flyer information can drastically impact business for airports and airlines.
    5. Developing data ecosystems – While the aviation industry is the first one to embrace technology, several digital initiatives are met with disdain because of the high cost of digital experiments and often low tangible value. Hence, engineers and scientists around the world are developing data ecosystems to back their digital initiatives with data-based decision making. They are also investing in flexible and scalable IT architectures to run operations and experiment. According to BCG, “Companies that excel in digital adoption deploy a fully flexible and scalable IT architecture that leverages the cloud and digital architecture. Because these companies gain the ability to scale their data infrastructure up or down as needed, they can reduce their data center operating costs and reinvest the savings in other digital initiatives.

Analytics Opportunities in Hyderabad

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According to a NASSCOM report on the Indian IT Product Landscape in Analytics, there is more than a 60% increase in revenue per FTE (Full Time Employee) for analytics. With more than 600+ analytics firms, 400+ start-ups, and 1.3 lakh+ analytics professionals, India is the hottest big data analytics and data science hub in the world. With an influx of talent into this domain over the last few years, and major companies like Cognizant, Infosys, and TCS investing in employee upskilling, 50-60% BPM companies have successfully integrated analytics with operations management (and made it into a service offering). The top cities that have seen the maximum growth in analytics and data science are Bangalore, Delhi-NCR, Mumbai, Hyderabad, Pune, and Chennai.

The dark horse in the analytics domain has been none other than Hyderabad. Thanks to the information technology boom, Hyderabad got a new makeover in the name of Cyberabad. Known for its culture, food, and pearls, the city of Nizams is today also known as a global center of information technology with all major players in the domain having or setting-up offices in Hyderabad. Some of the major global companies located in Hyderabad are Google, Microsoft, Facebook, Apple, Dell, IBM, Cisco, Genpact, AON Hewitt, EXl Services, Oracle, Sapient, Value labs, Hewitt-Packard, Mu Sigma, Deloitte, Capgemini, Absolutdata, Evalueserve, etc.

Learning Analytics in Hyderabad provides you the perfect platform to grow or transition to Analytical roles in companies based there. Professionals based in Hyderabad have the opportunity to work with not only the top IT firms but also several analytics-based start-ups as well. 42% of the total analytics professionals are based in Hyderabad. Roles in analytics have seen with a steep salary raise of around 22% over the last three years. According to a job report by Analytics India Magazine, Hyderabad has seen a steady increase job openings in analytics over the last 3 years.

As per Economic Times, the Telangana government is setting up a Center of Excellence for Data Science and Artificial Intelligence in partnership with NASSCOM in Hyderabad. This joint investment of INR 40 crores will aid the establishment of a public-private partnership model. The Minister of IT, Industries, MA &UD, NRI Affairs, Mr. K T Rama Rao said, “The Government is keen to create a robust Data Science & Artificial Intelligence ecosystem for the various stakeholders to thrive by providing support, mentorship, and other capabilities. This Centre of Excellence, in partnership with NASSCOM and the industry, has been set up to develop Telangana and India as a global hub for DS & AI in the coming years.” He further added, “Hyderabad is emerging as an attractive location for global technology companies. The state government had introduced a special IT Policy and taken up T-Hub for encouraging start-ups. The first phase of the T-Hub has already earned the reputation of being the largest incubator in the country. The government of Telangana realizes the opportunities newer technologies present and has laid the foundation of a Data Analytics Park to tap the innovation and employment opportunities in this rising sector.”

Popular job sites have more than 3500 openings in Analytics as of December 2018 with companies like Wells Fargo, Infosys, ADP, Deloitte, UHG, Capgemini, Amazon, Oracle, HSBC, Microsoft, Wipro, Bank of America, JP Morgan Chase and GE Corporate, Genpact, Bajaj Capital, Accenture, Cognizant, ICICI Bank, Oracle, Uber, Paypal, and Thomson Reuters. The most in-demand roles in analytics and data science are those of a Data analyst, business analyst, data scientist, marketing analyst, and financial analyst.

Great Learning programs like PGP-BABI (India’s number 1 analytics program by Analytics India Magazine in 2015, 2016, 2017, 2018) and BACP (Business Analytics Certificate Program – online format) help you gain an edge among the analytics and data science workforce in Hyderabad – all without disrupting your work schedule. 

Analytics Opportunities in Kolkata

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“An impressive 85% of companies that are succeeding with analytics (i.e. those organizations that consider themselves to be analytics pacesetters in their markets and have an enterprise-wide analytics strategy in place) are seeing revenue growth greater than 7%. Less than a quarter of analytics laggards reach that percentage,” according to Forbes Insights/Cisco research. Hence, an aversion to analytics is a recipe for disaster for big, mid-, or small-size organizations alike. This may not come as a surprise because when it comes to companies reaping the benefits of business analytics and data science, there is really nothing new to say.

However, we still cannot talk enough about the relevance that this all-pervasive data-driven approach being taken by businesses has had for working professionals. Be it any domain you are working in, chances are that adding analytics capabilities to your skillset is inevitable. If you are a marketing professional, someone around you is upskilling and learning analytics or there is a new member on the team especially for marketing analytics supporting your campaigns and performance marketing goals. A similar movement can be seen in all industry verticals.

India Scenario

India is one of the most promising markets in the growth of analytics. According to NASSCOM, the total demand for analytics and AI-based roles in India is around 510000 in 2018 which will further increase to approximately 800,000 by 2021. An unexpected supply-gap in analytics job profiles makes learning analytics highly lucrative for professionals in all domains, IT and BFSI being at the top. That is not to say that other domains like Healthcare, Aviation, Sales and Marketing, Consulting, etc. are in any way behind when it comes to reaping the benefits of analytics. The top 7 cities that have an ever-increasing demand for analytics professionals are Delhi-NCR, Mumbai, Pune, Chennai, Bangalore, Hyderabad, and Kolkata.

Kolkata Kindled!

Truth be told, 2 years ago, the demand for analytics professionals in Kolkata was low, but it has picked up pace since, just like the other top cities. We, at Great Learning, have had several cases where our alumni have moved to different cities to pursue our analytics and data science courses or have had transitions into excellent analytics opportunities with our online programs. Our alumnus, Harshit Mehta (working for SAP at the time) requested a transfer to Bangalore to especially pursue Great Lakes PG Program in Business Analytics. Harshit says, “I actually took a transfer to attend this course. I was located in Calcutta and requested for a transfer to Bangalore so that I could stay there and take up this course. After program completion, I joined the Smiths Group as a Data analyst.”

According to AIM, $ 88 million in revenue for the analytics industry comes from Kolkata, increasing by more than 30% from 2017 and a whopping 60% since 2016. This only means a large number of job openings are taking a very long time to fill and professionals based in the eastern and north-eastern hub of India have an excellent growth opportunity to capitalize on this growing trend.

Job data sites are in sync with these report. The current number of openings in the analytics and data science sector as of Nov. 2018 is more than 1500 in Kolkata with companies like Cognizant, SAP, KPMG, Capgemini, Uber, Accenture, Fidelity, FIS, ICICI, British Telecom, Apollo, Zomato, Quickr, etc. investing heavily in developing their analytics capabilities. Most job openings are for the roles of data analysts, business analysts, data scientists, marketing or financial analyst. These are very exciting times for professionals in Kolkata as Analytics spreads its wings beyond Delhi-NCR, Bangalore, and Hyderabad.

Kolkata is also a major player in the banking sector with the oldest and front-line banks and PSUs headquartered in Kolkata. The BFSI sector was one of the first to embrace analytics, hence there are a ton of opportunities in advanced analytics both in the private and public sector. Some of the largest financial companies and banks like Bank of America, Standard Chartered, HSBC, Magma Fincorp, Bandhan Bank, Srei Infrastructure Finance, and National Insurance Company have corporate offices and big branches in Kolkata.

4 Ways Analytics is Fuelling the Energy Sector

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Thanks to big data analytics, a makeover for the energy vertical is in order to prepare it for the digital economy. Senior leaders at energy companies are committing to fact-based decision making, investing in analytics talent, resources, tools and technologies. Slow to pick up, but fast to implement, the energy sector is beaming thanks to the refurbishing by advanced analytics that has quickly transformed its landscape. According to Deloitte, “Applying analytics to the vast amounts of useful data utilities collect offers an opportunity to uncover new customer usage patterns, to forecast demand better, to manage energy constraints more effectively, to improve compliance with regulatory requests, to prevent fraud and reduce loss, and to enhance customer service.” So, Analytics has served to be no less than an epiphany for the biggest players in the domain and here is how:

  1. Resolving the Oil Industry Hiccups – The oil industry often saw downtime due to pipes being stuck within the wellbore. A common problem like this occurs with changes in the physical state of a well and it becomes difficult to carry out normal drilling operations at the site. Oil sites are particularly vulnerable to land slides or mineral deposits. With predictive analytics solutions, oil companies are able to assess the conditions of a well real-time and build an almost accurate picture to predict if operations in a well would be conducive. Engineers’ expertise with physics-based models is combined with statistical models to predict the most likely occurrences of non-productive time (NPT). Additional insights such as how many new wells to build, the average lifetime of an existing well bore, selecting sites also become help in building reports. Popularly known as survival analysis in the oil industry, this type of analysis serves as an indispensable tool designed to analyse censored data by estimating the lifetime through the failure rate. This also helps the companies to identify wellbore sites most likely to cause problems and delay operations.
  2. Data Integration in Physical Systems – The energy sector has majorly relied on physical systems. But data governance and data integration with these systems are mammoth tasks. According to a survey by Accenture, 63% of energy respondents feel that data integration is a roadblock as the quality of data, and the ability to analyse it are not at par with other industries. But, energy companies are resolving these issues by making sure that data collected is highly relevant to the businesses and proprietary data that helps differentiate between these companies is recorded and analysed effectively. Complete data integration with existing systems will not eliminate them but help reduce the decision making time, increase efficiency and productivity at lower costs with adequate focus on the insights generated through this data.
  3. Energy Crisis and Solutions – The energy sector is experiencing dramatic changes thanks to innovation in the way electricity is generated, distributed, and consumed. Using sustainable sources of electricity generation, storing it to meet consumer demands, and preparing for planned and unplanned outages or unprecedented consumption are all top priorities but the biggest challenge is that all these need to be achieved at a manageable cost. A rise in global energy consumption requires an understanding of the balance between conventional and renewable energy sources. On the consumer’s end, smart metering is required to optimise electricity. Advanced analytical systems help monitor patterns in energy consumption and generation. Real-time data must be captured to predict surges and shortages that can equip companies to automatically respond with consistent and affordable electricity supply. According to IBM, “Such systems would revolutionise this market, manage costs more effectively and provide a cleaner industry overall. The energy providers that employ this level of intelligent grid applications will competitively differentiate themselves for consumers and provide higher profits to shareholders.”
  4. Maintenance Issues- A major pain point for the energy and utility sector is maintenance. Unanticipated pipeline bursts, shut downs, interventions, and outages disrupt service and cost millions for organisations. This is where advanced analytics comes to rescue. Sensory data can be analysed to develop a precise approach and proactive (predictive) attitude to maintenance. Unusual stress or load, equipment failure, and shutdowns can be prevented with better data and advanced analytics capabilities. Companies use smart systems and data models for setting up processes and performance metrics that help prepare better.

Top 10 Applications of Deep Learning

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Think of a world with no road accidents or cases of road rage. Think of a world where every surgery is successful without causing the loss of human life because of surgical errors. Think of a world where no child is underprivileged and even those with mental or physical limitations can enjoy the same quality of life as does the rest of humanity. If these are too hard to fathom, think of a world where you could just segregate your old images (the ones without much metadata) according to your own parameters (events, special days, locations, faces, or group of people). Deep Learning applications may seem disillusioning to a normal human being, but those with the privilege of knowing the machine learning world understand the dent that deep learning is making globally by exploring and resolving human problems in every domain. Here are a bunch of things already done or under process, thanks to the wonders of deep learning:

  1. Self-Driving Cars – Deep Learning is the force that is bringing autonomous driving to life. A million sets of data are fed to a system to build a model, to train the machines to learn, and then test the results in a safe environment. The Uber AI Labs at Pittsburg is not only working on making driverless cars humdrum but also integrating several smart features such as food delivery options with the use of driverless cars. The major concern for autonomous car developers is handling unprecedented scenarios. A regular cycle of testing and implementation typical to deep learning algorithms is ensuring safe driving with more and more exposure to millions of scenarios. Data from cameras, sensors, geo-mapping is helping create succinct and sophisticated models to navigate through traffic, identify paths, signage, pedestrian-only routes, and real-time elements like traffic volume and road blockages. According to Forbes, MIT is developing a new system that will allow autonomous cars to navigate without a map as 3-D mapping is still limited to prime areas in the world and not as effective in avoiding mishaps. CSAIL graduate student Teddy Ort said, “The reason this kind of ‘map-less’ approach hasn’t really been done before is because it is generally much harder to reach the same accuracy and reliability as with detailed maps. A system like this that can navigate just with onboard sensors shows the potential of self-driving cars being able to actually handle roads beyond the small number that tech companies have mapped.”
  2. News Aggregation and Fraud News Detection – There is now a way to filter out all the bad and ugly news from your news feed. Extensive use of deep learning in news aggregation is bolstering efforts to customize news as per readers. While this may not seem new, newer levels of sophistication to define reader personas are being met to filter out news as per geographical, social, economical parameters along with the individual preferences of a reader. Fraud news detection, on the other hand, is an important asset in today’s world where the internet has become the primary source of all genuine and fake information. It becomes extremely hard to distinguish fake news as bots replicate it across channels automatically. The Cambridge Analytica is a classic example of how fake news, personal information, and statistics can influence reader perception (Bhartiya Janta Party vs Indian National Congress), elections (Read Donald Trump Digital Campaigns), and exploit personal data (Facebook data for approximately 87 million people was compromised). Deep Learning helps develop classifiers that can detect fake or biased news and remove it from your feed and warn you of possible privacy breaches. Training and validating a deep learning neural network for news detection is really hard as the data is plagued with opinions and no one party can ever decide if the news is neutral or biased.
  3. Natural Language Processing (NLP) – Understanding the complexities associated with language whether it is syntax, semantics, tonal nuances, expressions, or even sarcasm, is one of the hardest tasks for humans to learn. Constant training since birth and exposure to different social settings help humans develop appropriate responses and a personalized form of expression to every scenario. NLP through Deep Learning is trying to achieve the same thing by training machines to catch linguistic nuances and frame appropriate responses. Document summarization is widely being used and tested in the Legal sphere making paralegals obsolete. Answering questions, language modeling, classifying text, twitter analysis, or sentiment analysis at a broader level are all subsets of natural language processing where deep learning is gaining momentum. Earlier logistic regression or SVM were used to build time-consuming complex models but now distributed representations, convolutional neural networks, recurrent and recursive neural networks, reinforcement learning, and memory augmenting strategies are helping achieve greater maturity in NLP. Distributed representations are particularly effective in producing linear semantic relationships used to build phrases and sentences and capturing local word semantics with word embedding (word embedding entails the meaning of a word being defined in the context of its neighboring words).
  4. Virtual Assistants – The most popular application of deep learning is virtual assistants ranging from Alexa to Siri to Google Assistant. Each interaction with these assistants provides them an opportunity to learn more about your voice and accent, thereby providing you a secondary human interaction experience. Virtual assistants use deep learning to know more about their subjects ranging from your dine-out preferences to your most visited spots or your favorite songs. They learn to understand your commands by evaluating natural human language to execute them. Another capability virtual assistants are endowed with is to translate your speech to text, make notes for you, and book appointments. Virtual assistants are literally at your beck-and-call as they can do everything from running errands to auto-responding to your specific calls to coordinating tasks between you and your team members. With deep learning applications such as text generation and document summarizations, virtual assistants can assist you in creating or sending appropriate email copy as well.
  5. Entertainment (VEVO, Netflix, Film Making, Sports Highlights, etc.) – Wimbledon 2018 used IBM Watson to analyse player emotions and expressions through hundreds of hours of footage to auto-generate highlights for telecast. This saved them a ton of effort and cost. Thanks to Deep Learning, they were able to factor in audience response and match or player popularity to come up with a more accurate model (otherwise it would just have highlights of the most expressive or aggressive players). Netflix and Amazon are enhancing their deep learning capabilities to provide a personalized experience to its viewers by creating their personas factoring in show preferences, time of access, history, etc. to recommend shows that are of liking to a particular viewer. VEVO has been using deep learning to create the next generation of data services for not only personalized experiences for its users and subscribers, but also artists, companies, record labels, and internal business groups to generate insights based on performance and popularity. Deep video analysis can save hours of manual effort required for audio/video sync and its testing, transcriptions, and tagging. Content editing and auto-content creation are now a reality thanks to Deep Learning and its contribution to face and pattern recognition. Deep Learning AI is revolutionizing the filmmaking process as cameras learn to study human body language to imbibe in virtual characters.
  6. Visual Recognition – Imagine yourself going through a plethora of old images taking you down the nostalgia lane. You decide to get a few of them framed but first, you would like to sort them out. Putting in manual effort was the only way to accomplish this in the absence of metadata. The maximum you could do was sort them out based on dates but downloaded images lack that metadata sometimes. In comes, Deep Learning and now images can be sorted based on locations detected in photographs, faces, a combination of people, or according to events, dates, etc. Searching for a particular photo from a library (let’s say a dataset as large as Google’s picture library) requires state-of-the-art visual recognition systems consisting several layers from basic to advanced to recognize elements. Large-scale image Visual recognition through deep neural networks is boosting growth in this segment of digital media management by using convolutional neural networks, Tensorflow, and Python extensively.
  7. Fraud Detection – Another domain benefitting from Deep Learning is the banking and financial sector that is plagued with the task of fraud detection with money transactions going digital. Autoencoders in Keras and Tensorflow are being developed to detect credit card frauds saving billions of dollars of cost in recovery and insurance for financial institutions. Fraud prevention and detection are done based on identifying patterns in customer transactions and credit scores, identifying anomalous behavior and outliers. Classification and regression machine learning techniques and neural networks are used for fraud detection. While machine learning is mostly used for highlighting cases of fraud requiring human deliberation, deep learning is trying to minimize these efforts by scaling efforts.
  8. Healthcare – According to NVIDIA, “From medical imaging to analyzing genomes to discovering new drugs, the entire healthcare industry is in a state of transformation and  GPU computing is at the heart. GPU-accelerated applications and systems are delivering new efficiencies and possibilities, empowering physicians, clinicians, and researchers passionate about improving the lives of others to do their best work.” Helping early, accurate and speedy diagnosis of life-threatening diseases, augmented clinicians addressing the shortage of quality physicians and healthcare providers, pathology results and treatment course standardization, and understanding genetics to predict future risk of diseases and negative health episodes are some of the Deep Learning projects picking up speed in the Healthcare domain. Readmissions is a huge problem for the healthcare sector as it costs tens of millions of dollars in cost. But with the use of deep learning and neural networks, healthcare giants are mitigating health risks associated with readmissions while bringing down the costs. AI is also being exceedingly being used in clinical researches by regulatory agencies to find cures to untreatable diseases but physicians skepticism and lack of a humongous dataset are still posing challenges to the use of deep learning in medicine.
  9. Personalizations – Every platform is now trying to use chatbots to provide its visitors with personalized experiences with a human touch. Deep Learning is empowering efforts of e-commerce giants like Amazon, E-Bay, Alibaba, etc. to provide seamless personalized experiences in the form of product recommendations, personalized packages and discounts, and identifying large revenue opportunities around the festive season. Even recce in newer markets is done by launching products, offerings, or schemes that are more likely to please the human psyche and lead to growth in micro markets. Online self-service solutions are on the rise and reliable workflows are making even those services available on the internet today that were only physically available at one time. Robots specialized in specific tasks are personalizing your experiences real-time by offering you the most suited services whether it is insurance schemes or creating custom burgers.
  10. Detecting Developmental Delay in Children – Speech disorders, autism, and developmental disorders can deny a good quality of life to children suffering from any of these problems. An early diagnosis and treatment can have a wonderful effect on the physical, mental, and emotional health of differently-abled children. Hence, one of the noblest applications of deep learning is in the early detection and course-correction of these problems associated with infants and children. This is a major difference between machine learning and deep learning where machine learning is often just used for specific tasks and deep learning on the other hand is helping solve the most potent problems of the human race. Researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital’s Institute of Health Professions have developed a computer system that can identify language and speech disorders even before kindergarten when most of these cases traditionally start coming to light. The researchers evaluated the system’s performance using a standard measure called area under the curve, which describes the tradeoff between exhaustively identifying members of a population who have a particular disorder. They use residual analysis that identifies the correlation between age, gender, and acoustic features of their speech to limit false positives. Autism is often detected by combining it with cofactors such as low birth weight, physical activity, body mass index, learning disabilities, etc.

What is Deep Learning, you ask again? It might look like the stuff science-fiction is made of – only that it is capable of transforming that fiction into our current reality.

The domain has already created tons of opportunities for professionals with deep learning and other AI expertise. If the aforementioned applications of deep learning has already stirred your interest, now would be the perfect time to upskill. To explore a career in Deep Learning, click here.

What is Business Analytics?

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From manual effort to machines, there has been no looking back for humans. In came the digital age and out went the last iota of doubt anyone had regarding the future of mankind. Business Analytics, Machine Learning, AI, Deep Learning, Robotics, and Cloud have revolutionized the way we look, absorb, and process information. While there are still ongoing developments happening in several of these advanced fields, business analytics has gained the status of being all-pervasive across functions and domains. There is no aspect of our lives untouched by Analytics. The mammoth wings of analytics are determining how we buy our toothpaste to how we choose dating partners to how we lead our lives.

Moving on to a more technical definition of business analytics, Gartner says, “Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user. These analytics solutions often come with prebuilt industry content that is targeted at an industry business process (for example, claims, underwriting or a specific regulatory requirement).”

Business Analytics is interchangeably used with data analytics. The only difference being that while data analytics is the birth child of the data boom, business analytics represents a coming of age that centers data insights at the heart of business transactions. Nearly 90% of all small, mid-size, and large organizations have set up analytical capabilities over the last 5 years in an effort to stay relevant in the market and draw value out of the insights that large volumes of data recorded in the digital age can provide.

Professionals, on the other hand, are also in a rush to bag analytical roles for career success. So, what does it mean for aspiring analytics professionals?

Nearly every domain has seen an uprise in the number of opportunities in the analytics segment but there is still a huge supply-demand gap that exists when filling these positions. This is because of the lack of relevant quality education in graduation (that still continues to teach its archaic curriculum) and also a lack of enthusiasm to upskill especially in the more seasoned professionals with more than 5 years of experience. Slowly, this trend is changing with freshers taking up business analytics and data science courses before entering the workforce and the seasoned professionals taking cognizance of the fact that they may render themselves jobless without upskilling to the skillset required in the digital economy. To make a switch:

  • – Find opportunities within your own firm to move – Every mid to large organization is establishing its analytical capabilities and there are ample opportunities out there for people to switch. If you have experience with reporting or analysis or statistics or advanced excel, chances are that your leaders will be open to you moving on to a more complex role. In the beginning, you may have to juggle between your regular work and new analytical initiatives, but this is one of the easiest ways to get started.
  • – Take up an Analytics Course – Learning things scientifically in a structured format helps you scale faster. Several options are available when it comes to Analytics courses right from MOOCs, weekend programs, hybrid courses (classroom + online) to full-time programs. While traditional full-time programs tend to promise the best results, hybrid courses and MOOCs are more suited to the learning needs of working professionals.
  • – Get Hands-On Experience – A certificate or merely stating on your CV that you know analytics tools and techniques will not help you get through job interviews. What you need are ready projects on your resume to make an impression. Participating in online hackathons, free projects with public data, or solving analytics challenges by Kaggle or Analytics Vidhya will go a long way in giving you the confidence to make this switch.


Difference Between Business Analytics and Business Intelligence

Another million dollar question is differentiating business analytics from business intelligence as BI is often used in place of BA. In the industry, however, there is only a marginal difference in the way these terms are defined. While BI is more about using data collected over a period of time from different sources to create dashboards, reports, and documentation; BA focuses on the implementation of data insights into actionable steps. Hence, BI has a longer loop with BA functions integrated at mostly all steps. As Dr.Bappaditya (Course Director – PGPBABI, Great Lakes) points out, “I believe Business Analytics (BA) and Business Intelligence (BI) are very overlapping so there isn’t a lot of difference. Business intelligence is slightly more generic where you are using data from various sources and then you are analyzing it. That’s also a part of BA but in BI you finally finish the loop by implementing the insights generated from data. So in that sense, BI is slightly all-encompassing. Essentially, whatever be the nomenclature – whether it is BI or BA, we are actually trying to figure out how to capture data, how to make it neat and how to make it talk to each other. We look at unstructured, unusual data to generate insights doing prediction analysis and finally implement it. So whether it is BA or BI, both of them cover the same thing. I don’t think that there is much of a difference whether you take up a Business Intelligence course or a Business Analytics course, because any program (BA or BI) the coursework listed under either will pretty much be the same thing.”

Busting Business Analytics Myths

  1. You Need Programming to Learn Business Analytics – A professional doesn’t require programming experience to learn business analytics as most of the tools and techniques are easy to use. Tableau, a data visualization tool, even has drag and drop elements that make it really easy for anyone to get started. That’s why Business Analytics has found such ubiquitous application in all domains and professionals from vastly different industries like BFSI, Marketing, Agriculture, Healthcare, Genomics, etc. find it to be a great career option and a natural progression in their careers. A good knowledge of statistics will need to be developed though.
  2. You Need Advanced Maths for Business Analytics – Business Analytics is based on the use of common human intelligence that can be applied to solve any and all industry problem. Hence, you don’t need Fourier series or advanced mathematical algorithms to build analytical models. Math learned till 10+2 level is good enough and can serve as a starting base for professionals in all domains. Also, unknowingly a lot of professionals apply math in their day to day work with excel and data interpretation so Math beyond the basic level is not a mandate to learn the principles of business analytics.
  3. Learning BA Tools And Techniques is Enough for Becoming a Good Business Analyst – While learning Python, R, or SAS can get you through the door for an interview, it would be fairly hard for anyone to excel in their role without two things – one is domain knowledge and the second would be a business sense. To understand a client’s (or your own internal) requirement, to formulate a problem statement that needs to be processed, a business analytics professional has a lot under his/her purview. Simply put, learning tools and techniques is only a small piece of the larger picture to get you started in your role. Dr. Bappaditya (Director, PG Program Business Analytics Program, Great Lakes) explains this with an example: “A million records of a customer for credit cards are processed to figure out bad customers from good ones. While a data scientist will crunch that data to find insights, a Business Analytics professional will put a decision rule to it. A business analyst will look at all this data and come to the simple rule that customer is good if his credit score is above this (let’s say 95%)  or his income is above that and the number of dependents is this. Otherwise, a customer is bad. So, Business Analytics is much more applied and with a very specific objective in mind. Business analytics is not about accuracy. It is about what can be implemented or what can be useful to the client. So business analytics often compromise on the accuracy a lit bit as long as the model gives insights that can be acted upon. So, business analytics will require a lot of input and intuition for an understanding of what the results are.”
  4. A Business Analytics Profile Is All About Crunching Numbers – Number crunching or in technical terms – cleaning of data, slicing and dicing of data, converting an unstructured data set into a structured one is all part of the process. However, the profile of a business analytics professional is not limited by these functions. The essence of true business analytics lies in resolving business problems combining domain knowledge, client interactions, business sense, and basic human intelligence apart.

Business Analytics Tools and Techniques

Various business analytics tools and techniques like Python, R, SAS, Tableau, Statistical concepts, and building of analytical models are required to be able to apply for business analytics roles. A working knowledge of business analytics and business intelligence tools is a key differentiator for professionals competing for business analytics jobs. The relevance of data analysis tools is determined by the project and client requirements. Python, R, SAS, Excel, and Tableau have all got their unique places when it comes to usage. According to the Great Learning Skills Report 2018, SQL is the top requirement to excel in the field of data science, followed closely by Python and Java. Hadoop, R, and SAS have also climbed up the ladder to be amongst the top 10 skills required as per data from

Let’s take a closer look at some of these:

Python – Python has a very regular syntax as it stands out for its general-purpose characteristics. It has a relatively gradual and low learning curve for it focuses on simplicity and readability. Python is very flexible and can also be used in web scripting. It is mainly applied when there is a need for integrating the data analyzed with a web application or the statistics is to be used in a database production. The IPython Notebook facilitates and makes it easy to work with Python and data. One can share notebooks with other people without necessarily telling them to install anything which reduces code organizing overhead, hence allowing one to focus on doing other useful work. Python offers several libraries for visualization like Boken, Pygal, and Seaborn which may, in turn, be too many to pick. And unlike R, its visualizations are convoluted and not attractive to look.

SAS – SAS is widely used in most private organizations as it is a commercial software which also ensures that it has a whole lot of online resources available. Also, those who already know SQL might find it easier to adapt to SAS as it comes with PROC SQL option. The tool has a user-friendly GUI and can churn through terabytes of data with ease. It comes with an extensive documentation and tutorial base which can help early learners get started seamlessly. SAS has two disadvantages: Base SAS is struggling hard to catch up with the advancements in graphics and visualization in data analytics. Even the graphics packages available in SAS are poorly documented which makes them difficult to use. Also, SAS has just begun work on adding deep learning support while its competition is far ahead in the race.

R – R is an open source software and is completely free to use making it easier for individual professionals or students starting out to learn. While several forums and online communities post religiously about its usage, R can have a very steep learning curve as you need to learn to code at the root level. Graphical capabilities or data visualization is the strongest forte of R with R having access to packages like GGPlot, RGIS, Lattice, and GGVIS among others which provide superior graphical competency. R is gaining momentum as it added a few deep learning capabilities. One can use KerasR and Keras package in R which are mere interfaces for the original Keras package built on Python.

Tableau – Tableau is the most popular and advanced data visualization tool in the market. Story-telling and presenting data insights in a comprehensive way has become one of the trademarks of a competent business analyst. It offers a free public version but a paid version is required for those who would like to keep their reports and data confidential. Tableau is a great platform to develop customized visualizations in no time, thanks to the drop and drag features. Tableau can be easily integrated with most analytical languages and data sources and visualizations created are platform and screen-size independent. The downside of Tableau is that it comes with a cost especially for large enterprises and there are no version-control options yet.