Why Machine Learning is Being Termed As the Next Big Thing

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Have you wondered what powers the highly personalized recommendations on your mobile device from Amazon? Do you wonder how Uber determines arrival times of your booked app cab? I am sure most of you have wondered at some point in time, how Snapchat uses filters or influences stories.

These are everyday examples of machine learning algorithms at work. The common thread is acting quickly on business intelligence for an edge in highly competitive industries.

What is machine learning and why does it matter?

Machine learning is an application of artificial intelligence (AI) which allows computer programs to progressively learn and improvise from its experience with the data. It automates analytics by using algorithms that learn iteratively, to make predictions. Its simple technique of self-learning rather than rule-based programming has found a wide application across multiple scenarios. So this technology has pervaded everyday lives, whether bringing ease of living with navigation recommendations or warning you of market volatility for best investment decisions. Therefore, machine learning matters; as it shapes your ease of living or decision-making. It has integrated so deeply into daily life that you will most likely not notice its application. For instance, the active filtering of your spam messages by Gmail.

How does machine learning bring value to a business?

Machine learning is for both, problem-solving and adding value to a business.
On the marketing front, machine learning analyses historical and real-time data for modifying marketing strategies, instant upsell and cross-sell recommendations, and making predictions of customer behavior. This in turns adds value to marketing and segmentation strategies for personalized recommendations. Machine learning models based on various marketing metrics help predict the prospects of conversions. The unsupervised learning technique of machine learning algorithm further identifies buying patterns, by clustering products to make better product recommendations.

In the financial world, the advantages of machine learning are phenomenal. The most significant use is fraud detection, with its ability to learn from data and spot anomalies and suspicious patterns. Other uses are algorithmic market trading, loan underwriting and regulatory compliance with anti-money laundering laws. In manufacturing and logistics, machine learning helps identify the gaps and weak nodes for devising predictive maintenance. The same ability of learning algorithms to spot patterns can help report security breaches in a database as and when they occur.

The use of machine learning thus spans across industries and applications, enhancing customer experience, and adding business value for higher returns on investments (ROI). Online searches with intuitive results are perfect examples of ML use to cut downtime by making predictions. Algorithms using natural language processing (NLP) are used in AI chatbots, to act as powerful self-learning customer agents. This optimizes resources and builds an additional channel for customer analytics.

Real world applications and use cases

Some of the most prolific users are in the banking and financial industry. HDFC Bank has begun rolling out its technology stack with ML and AI. The focus is on enhanced services across the entire spectrum like loan disbursement, transactions, hiring, customer experience and personal banking. Additionally, HDFC has started deploying chatbots for customer engagement. The conversational interface offers a personalized and seamless customer experience.
Major eCommerce platform, Flipkart implements over 60 machine learning models to generate insights – “how a sale is going, which deals are working or not working, at which point customers are dropping off”, and so on.

Nebula, an agro-based company, is leveraging ML to solve problems in Indian agriculture. The testing of agricultural products is done using deep learning and image recognition technologies for speedier and quality results, enabling farmers to get better prices for their products.

In the HR marketplace, Aspiring Minds has an assessment-based job search platform for adding value to merit-based recruitment. Innov4Sight Health and Biomedical Systems is a healthcare start-up that builds intelligent solutions for medical diagnosis using machine learning techniques. SkinCurate powers its diagnostic and therapeutic research for customized treatment in the skin, using state-of-the-art ML techniques.

Trends and possibilities shaping a machine learning-driven future

Key technological trends powering machine learning – data flywheel, algorithm marketplace, and cloud-hosted intelligence – are expected to shape the future deployment of ML. The advantage of increased user-generated data for flywheel impact will be used by companies for rolling out future products and programs like Tesla is planning for its self-driving cars. Scaled-up machine learning algorithms have created an algorithm marketplace, for reaping benefits of shared algorithmic intelligence. Hosted machine learning platforms are offering pre-trained models as a SaaS delivery, for economies of scale.

Marketing, financial services, and healthcare; are the sectors expected to see prolific and innovative applications of machine learning. It helps structure marketing insight for demand forecasting and predictive recommendations. In banking and finance, ML will be indispensable for the two key challenge areas, fraud detection, and risk management. The field of healthcare will emerge as the most significant application of machine learning innovation, as the results have the power to transform human lives.

The future possibilities of ML are limitless. Imagination, problem-solving and professional expertise in machine learning skills; are expected to drive innovations in business strategies and new product offerings. ML is the future of AI. Just as the future of big data analytics and digital marketing is machine learning.

5 Types of Business Analytics Jobs

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Much like business administration, business management and other types of business programs, business analytics jobs run the gamut from database administrator to data scientist and everything in between.

According to a recent study by India Jobs, data science is the fastest-growing job field in India.  Companies ranging from manufacturing to retail to healthcare are all becoming technology companies, and the need for skilled data analysts has never been greater.

Fortunately, there exist new and innovative programs across India to help with this pressing need. You can now go beyond just a business analyst course in Bangalore or a business analyst course in Pune to get the full benefits of a BA certification in less time than you may think. As compared to the scope of MBA in business analytics, these programs stand much ahead in terms of quality of education, reasonable fee structures, duration of the course, and industry exposure among others.

Here are a few of the best high-paying business analytics jobs worth pursuing with the right kind of business analyst training:

Data Scientist/Data Analyst

A data scientist or data analyst collects and interprets data using qualitative analysis.  From their analyses, they can predict, evaluate and share information that impacts a variety of departments and operations within the company. They often use technologies such as SQL, R, SAS and Python, so getting data analyst certification in these areas will help you tackle even the most complex data sets with ease.

Data scientists are perfectly at home analyzing large and complex data sets and leverage a wide range of machine-learning and visualization tools to help them coalesce such information into a more meaningful form.  Proper business analyst training helps teach these tools using cloud-based software, hands-on teachings and online mentorship from around the world.

And the timing has never been better. Many well-known companies are starting to set up the foundations for data sciences in these emerging markets, including Walmart, PayPal, Mercedes-Benz and more.  New research from the Everest Group shows that India holds as much as 50% of the global market in analytics services, and as the need for skilled data scientists rises, so too does the global demand.

And because data science tools are always changing and evolving, you’ll need to be adaptive and pragmatic about using different tools to solve varied problems. In short, becoming a data analyst helps give both form and function to the information collected, so that the company, in turn, can make impactful decisions with measurable results.

Data Engineer

Today’s companies make considerable investments in data, and the data engineer is the person who builds, upgrades, maintains and tests the infrastructure to ensure it can handle algorithms thought up by the aforementioned data scientists.

The good news is that the need for data engineers spans many different types of industries.  As much as 46% of all data analytics and data engineering jobs originate from the banking and financial sector, but business analyst jobs can be found in e-commerce, media, retail, and entertainment industries as well.

The best business analytics courses in India make use of all of the skills needed to become a proficient data engineer, including getting hands-on training in Hive, NoSQL, R, Ruby, Java, C++, and Matlab.  Many data engineers are also expected to have experience working with popular data APIs and ETL tools as well.

For those that have these skills, you’ll be glad to know that the job market in analytics is growing by leaps and bounds.  From April 2016 to April 2017, the number of analytics jobs almost doubled, following a pattern of nearly doubling the previous two years.

And all of these jobs rely on a data science engineer to make sure the data infrastructure is solid, sound and scalable.

Database Administrator

The database administrator oversees the use and proper functioning of enterprise databases.  They also manage the backup and recovery of business-critical information.   It doesn’t sound like much on the surface, but when you realize that everything about the business’ operation relies on properly functioning databases, you’ll see just how critical this job really is.

Because of the sheer demand for database administrators across all sectors, you can take a business analyst course in Bangalore or a business analyst course in Pune and still get the hands-on skills you need to succeed.  Learning about data backup and recovery, as well as security and disaster management, are crucial to moving up in this field.

You’ll also want to have a proficient understanding of business analyst courses like data modeling and design.

The best part is, the market for database administrators is wide open. Bangalore accounts for around 25% of business analyst jobs according to an India Jobs survey, while Delhi isn’t far behind, with 22%.

But even if you don’t live or work in one of the major cities, you’ll be glad to know that even smaller cities are experiencing a surge in business analytics jobs thanks to many start-ups establishing their respective presences there.

Data Architect

The data architect is responsible to creating the blueprints that help with data management, thereby allowing the databases to be centralized, integrated and protected.  The data architects provide the data engineers with the tools and platforms they need to able to carry out their tests with accuracy and precision.

Data architects typically have experience in data modelling and data warehousing, as well as extraction transformation and loan (ETL). Proficiency in technologies like Hive, Pig and Spark are often required.

If you’re looking for a job as a data architect, you’ll be glad to know that captive centers, GICs, and back offices have seen the highest analytics growth in India in recent years.  Over half of all analytics demand is found in captive centers and even though they typically use analytics for their own internal use (and thus spread the data to their respective global businesses), they nevertheless need knowledgeable data architects to help them manage it all.

Analytics Manager

Last, but certainly not least, is the analytics manager.  An analytics manager oversees all the aforementioned operations and assigns duties to the respective team leaders based on needs and qualifications.  Analytics managers are typically well-versed in technologies like SAS, R, and SQL.

But beyond the technical requirements, analytics managers also need to have good interpersonal and social skills as well as good leadership qualities.  They need to be creative, “outside the box” thinkers who can easily manage a team with far-reaching abilities and skills.

Getting Started with a Business Analytics Job

You may be surprised to learn that over half of those recruiting for business analytics jobs are looking for candidates with less than five years of experience.  On the flip-side, there’s also a definite surge in the need for senior analytics professionals as well – reaching a high of 20% in 2017.

The scope of business analytics is expanding and companies both large and small are scrambling to hire qualified individuals with the right set of data literacy skills. Now is the perfect time to leverage the rewarding career opportunities that span business analytics and other types of analytics jobs.

How the Fashion Industry is Using Data Science

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Retail has historically been one of the slowest sectors to adopt new technological advances, but when Amazon came along and beat them at their own game by using things like machine learning and artificial intelligence, they started paying attention.

The truth is, data science and big data analytics play a crucial role today in helping trendsetters pinpoint the ever-evolving shifts and changes present in fashion, and in helping everyone from manufacturers to models tackle the runway and the real world with style and finesse.  Here’s how they’re doing it:

The Problem with Traditional Retail Analytics

Traditionally, fashion houses and brands kept vital information like sales records and inventory details in-house.  But this also meant that they worked in a silo — that much of the colors, style, fit and other decisions for their garments were mostly scattered, unstructured data.   They lacked other crucial pieces of the puzzle such as competitive analysis, pricing, trends, insights and other must-have details.

How Fashion Companies Stay Relevant in the Digital Age

With the fashion industry, every possible facet of a piece of clothing is under scrutiny. From the fabric to the closures to the sizes and the style, everything is collected and analyzed. For those with the right data science degree, this presents an eclectic challenge — how to stay focused and on top of trends before they’re forgotten.

Thanks to the explosion of social media, people are tweeting, liking, sharing and pinning all sorts of fashion ideas together, breathing new life into the industry by pinpointing precisely what customers and prospective customers are talking about. This has created an urgent need to go beyond in-house retail analytics and delve into things like consumer sentiment and preferences to help forecast individual trends that won’t break the bank.

Capitalizing on a Continuous Feedback Loop

For example, fashion rental service Le Tote collects data about the styles its customers prefer.  This, in turn, helps its team of designers create items customers will love that are both hip and affordable.  From the moment a customer signs up for the service and selects their favorite clothing options, the system goes to work, analyzing their choices and suggesting relevant items accordingly.

great learning BDML program

Customer preferences are also sent to clothing designers working with Le Tote, while machine learning analyses the written feedback that customers leave after receiving their clothes.

Another well-known fashion-forward retailer, Stitch Fix, uses data science to predict styles that customers would like – even if the clothes themselves haven’t been designed yet.

What happens here is that AI algorithms cull through Stitch Fix’s inventory and put together a list of suggestions based on broad style categories. The system then goes through the second tier of clothing options to create nine different data-built designs which are then sent to the design team as blueprints.

Imagine how much money, time and effort the company and its designers have saved by using the underlying data they collect to forecast trends based on customer preferences, rather than making the products and sending them out to retailers only to have them lose money.

And that’s only scratching the surface of what data science can do for the fashion industry.

Actionable Product Intelligence

One of the biggest issues that continuously dogs the fashion industry is the risk of new product introductions. Whereas in the past, companies would rely on traditional focus groups, according to Forbes, major brands are now using predictive analytics to create what they call “actionable product intelligence”.

Well-known fashion brands like Ralph Lauren, Lucy Brand, Sperry and True Religion are all using this type of predictive intelligence to discover how different changes in product fabric, design details, colors and price all affect customer response to an item.  When they find a winner, they can zero-in on it and create similar products with greater speed and precision.   What’s more, the propensity of creating a product that flops with the target audience is minimized.

If you were a fashion brand and you could leverage data science training to consistently create winning products that are a hit with customers, why wouldn’t you?  In doing so, you, in turn, engage the fiercely loyal customers, influencers, and trendsetters who can, in turn, make or break a brand’s perception as a whole.

The Future of Fashion and Big Data

In addition to using data to understand customer needs and shopping behavior, data science is also being used to forecast a product’s “shelf-time” on the website, and advise the customer if it’s going to sell out soon.  This, in turn, helps retailers and manufacturers alike estimate production and dispatch within a given market.

In short, advances in machine learning, artificial intelligence, and other crucial data science sectors is showing no signs of slowing down, making it a highly exciting time to make an entrance into the world of data science.

great learning BDML program

Industrie 4.0 Decoded

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Industrie 4.0 popularly known as the fourth industrial revolution, is a concept and a trend that aims at taking the manufacturing industry to the next level using futuristic technologies such as the Internet of Things, Data Analytics, and Cloud Computing, etc. Although initially a product of the German Government, today Industrie 4.0 is hailed as a revolutionary idea that will establish an industrial roadmap for others to follow.

The following facts shed light on some of the most important and intriguing characteristics of the Fourth Industrial Revolution:


According to the analysts, the increased adoption of industrial automation will cause a jump in the global sales, taking it to €195 billion by 2018 from the recorded €160 billion of 2013. That will be accomplished through data-driven manufacturing units which rely on cloud computing, data analytics, and densely interconnected plant machinery. In fact, according to Forbes, 47% of manufacturers expect big data analytics to be a major factor affecting their performance.

Key Insights:

  • – Data-oriented technologies will secure a prominent spot in manufacturing industries
  • – Demand for data scientists and analysts will significantly increase with time


Industrie 4.0 lays the foundation of the “future-factories” that are highly automated and technology-driven. One of the likely applications of this would be an infrastructure that is capable of self-organizing and self-optimizing. These factories will be able to self-fix supply chain anomalies, leaks, etc. and reassign the delivery systems for increased optimization.

Key Insights:

  • Monitoring of product development processes will reach a new level of accuracy and perfection.
  • Anomalies occurring during manufacturing will be dealt by the AI and advanced robotics for the most part.


One of the key objectives of smart factories is to save upgrade costs. This is where mapping of the physical world over a virtual world can be of great help.

Siemens PLM software (the same software that was used to simulate the landing of the Mars Curiosity Rover in 2012), for instance, is today used by “smart factories” to test different products in the fabric of virtual reality. This throws the weak methodology of “hit and trial” out of the window, and helps identify possible errors before the actual physical work starts in the production facilities.

Key Insights:

  • From the development of product prototypes to the real development process, all can be simulated beforehand by the manufacturers so that the risk of roadblocks is mitigated once the production incepts.
  • Plant layouts, when designed from scratch or adapted for new production requirements, can also be created and tested in a virtual world first to prevent expensive “post-setup” changes.


Additive manufacturing has a tremendous potential that can be easily integrated with the “future-factories” opposed to subtractive manufacturing. Additive manufacturing uses fewer resources and raw materials and can produce some of the most complex designs easily and quickly. General Motors is already ahead in this segment and plans to make 100,000 different parts by 2020 using the technology.

Key Insights:

  • Minimum wastage of material in future factories.
  • Medical and scientific equipment, aircraft manufacturers, etc. will be able to materialize powerful and intricate designs, thanks to the remarkable flexibility achieved through 3D printing.


Exponential technologies such as advanced robotics, Artificial Intelligence (AI), and sensor technology will act as accelerants and allow for enhanced flexibility and increased cost savings.

Key Insights:

  • Data collected by sensors, cameras, networks, etc. can be used for AI learning and infrastructure enhancement.
  • Minimum human intervention will protect lives and minimize health hazards around machinery.


The word “manufacturing” used to conjure up images of mechanical, unimaginative and dull factories a few years ago. However, as technology grows by leaps and bounds, we can look into the window of the future that showcases “futuristic” factories where machinery is smarter than ever and helps produce marvellous products in unbelievable quantities with Industrie 4.0.

5 Interview Questions Aspiring Big Data Analysts Should Be Able to Answer

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IBM predicts demand for Big Data Analysts and Scientists will soar to 28% by 2020. It is reason enough for many young professionals to up-skill themselves to pick up Big Data concepts. However, that wins only half the battle. The other half is to crack an interview for such a role.

This post looks at some common questions that a Big Data Analyst’s interview would entail:

1. How would you go about a Data Analytics Project?

A candidate must know the five key steps to an analytics project:

  • Data Exploration: Identify the core business problem. Identify the potential data dimensions that are impactful. Set up databases (often using technologies such as Hadoop) to collect ‘Big data’ from all such sources.
  • Data Preparation: Using queries and tools, begin to extract the data and look for outliers. Drop them from the primary data set as they represent abnormalities which are difficult to model/predict.
  • Data Modelling: Next start preparing a data model. Tools such as SPSS, R, SAS, or even MS Excel may be used. Various regression models and statistical techniques need to be explored to come up with a plausible model.
  • Validation: Once a rough model is in place, use some of the later data to test it. Modifications may be made accordingly.
  • Implementation & Tracking: Finally, the validated model needs to be deployed through processes & systems. Ongoing monitoring is required to check for deviations; so that further refinements may be made.

2. What kind of projects have you worked on?

Typically, a candidate is expected to know the entire life cycle of a data analytics project. However, more than the implementation, the focus should be on tangible insights that were extracted post implementation. Some examples are:

  • The sales data of an organization – Perhaps there was a problem regarding under achievement of targets during certain ‘lean periods.’ How did you pin the sales outcome to influencing factors? What were the steps you took to ‘deflate’ the data for seasonal variations? Perhaps you then setup an environment to feed the ‘clean’ past data and simulate various models. In the end, once you can predict/pinpoint problem factors, what were the business recommendations that were made to the management?
  • Another one could be considering production data. Was there a way to predict defects in the production process? Delve deep into how the production data of an organization was collated and ‘massaged’ to conduct modeling. At the end of the project perhaps some tolerance limits were identified for the process. At any point, if the production process were to breach the limits, the likelihood of defects would rise – thereby raising a management alarm.

The objective is to think of innovative applications of data analytics and talk of the process undertaken; from raw data processing to meaningful business insights.

3. What are some problems you are likely to face?

To judge how hands-on you are with data and technologies, the interviewer may want to know some of the practical problems you are likely to face and how you solved them. Below is a ready reckoner:

  • Common Misspelling: In a ‘Big Data’ environment there is likely to be common variations of the same spelling. The solution is to identify a baseline and replace all instances with the same.
  • Duplicate Entries: Often a common problem with master data is ‘multiple instances of the same truth.’ To solve this, merge and consolidate all the entries that are logically the same.
  • Missing Values: This is easy to deal with in ‘Big Data.’ Since the volume of records/ data points is very high, all missing values may be safely dropped without affecting the overall outcome.

4. What are your Technical Competencies?

Do your homework well. Read the organization profile carefully. Try to map your skill sets with those technologies that the company uses in terms of big data analytics. Consider speaking about these particular tools/technologies.

The interviewer will always ask you regarding your proficiency with big data and technologies. At a logical level, break down the question into a few dimensions:

  • From the programming angle, Hadoop and MapReduce are well-known frameworks generated by Apache for processing large data set for application in a distributed computing environment. Standard SQL queries are used to interact with the data.
  • For the actual modeling of the data, statistical packages like R and SPSS are safe bets.
  • Finally, for visualization, Tableau and variants like Apache Zeppelin are industry highlights.

5. Your end user has difficulty understanding how the model works and the insights it can reveal. What do you do?

Most big data analysts come from diverse backgrounds belonging to statistics, engineering, computer science, and business. It will take strong soft skills to integrate all of them onto a common page. As a candidate, you should be able to exhibit strong people and communications skills. An empathetic understanding of problems and acumen to grasp a business issue will be strongly appreciated. For a non-technical person, the recommended solution is not to have the Analyst delve into the workings of the model, instead focus on the outputs and how they help in taking better business decisions.

To conclude, Big Data Analytics as a domain is new and fast evolving. There are no set rules or defined answers. A candidate, who is confident, alert, has an acumen for problem-solving, and has knowledge of some Big Data tools, will be a hot commodity in the jobs’ market.

The Way Ahead – Opportunities for IT Professionals

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The current IT landscape is in a phase of transition with strong emphasis on ‘Scale to Skill’ changes in business requirements. Hari Nair, Co-Founder, Great Learning and Director Admissions, explains why it’s time to evolve and re-skill yourself for a promising future and how you could use this layoff as an opportunity to move up in your career.

Some of the estimated changes in the IT industry will include predicted job cuts and redundancy to grow at a significant rate, prompting professionals to upskill themselves or face a threat of layoff.

India's IT Work Force

Scale to skill: Reasons for the shift

Hari delved into the reasons behind the mass shift from ‘scale to skill’. He states that a large reason behind the layoffs is that the workforce is not trained to manage the competencies and technologies of the future. Some significant forces affecting the layoff include:

  1. Rapid adoption of automation, machine learning, and artificial intelligence in businesses worldwide has contributed to increasing redundancy.
  2. Shift from old technology to new frontiers and changing technological landscape has led to a need for professionals to update their learning and skill sets.

Technology Landscape changes in online Learning

The Good News

The good news with regard to the changing demands in the IT sector is that there is job creation on new frontiers. It is estimated these requirements may be just as large in number as job losses, and here is proof:

  1. The analytics sector in the country is growing at a rate of 30% per year in contrast with the IT sector which is growing at 5%.
  2. Professionals working in Analytics and Big Data are getting paid 25%-30% higher than their peers in the IT sectors, and their professional growth is much faster as well.
  3. Professionals must make an active choice to upskill and retrain themselves for the future and make continuous learning a norm.

Upcoming opportunity in Executive MBA, BACP and BDA programs

The 3 Mantras for Career Success

Hari elaborates on how IT professional can prepare themselves for the future by banking upon the 3 mantras for career success.

  1. Data proficiency is now a mandatory skill for knowledge workers who wish to grow in the IT industry. Data and Analytics is becoming a big part of all facets of businesses which means that it will be a highly important skill for the coming future.
  2. The current demands of the IT market means that professionals with specialised competencies are rewarded for their hard skills. Owing to the need for these specialised capabilities, professionals should try to excel in a certain domains which they can own and use to build future proof competencies.
  3. Work environments are increasingly getting intertwined and complex and it is critical to be people effective. If you have to grow in your career, understanding the context of business and learning how to be an effective manager and leader is critical to success.

Career success with BDA, BACP and PGPM-EX programs

4 Ways to Climb The Curve

Hari says that in today’s world, there is no right time to start learning and one must continuously make learning a part of their trajectory to be able to grow and thrive. Learning is a critical aspect of climbing the career curve and it is important now to follow the following rules to succeed:

  1. Pick a competence you want to build on and demonstrate your expertise in it. Correctly research the demand for this skill set and nurture it with a knowledgeable and immersive learning approach.
  2. Hari stresses upon the need to learn hard skill sets and gain the entire knowledge of a new domain rather than just learning tools, which he says are purely a means to an end and have short term benefits.
  3. Don’t go for shortcuts – emerging areas like Analytics, Big Data, Machine Learning are hard skills and require focused time and energy to master.
  4. Talk to peers, managers or mentors to understand why you should be building additional and relevant competencies and if they will help you grow in your career.

Climb the success curve with data and analytics

Ways to Make Yourself More Visible

Hari Nair says that it is important to make yourself and your work visible at your workplace. He recommends building a body of work to display your strengths. Make sure your current organization and potential employers recognize your value through your projects.

  1. Build a body of work and a ‘personal brand’ to differentiate yourself from competition.
  2. Participate in competitions, forums and online meet-ups such as Kaggle to show an active presence and skills.
  3. Involve yourself in projects within your own organization by participating in in-house projects.
  4. Join a community and contribute to start gaining recognition in the industry. There are many online portals and forums where one can begin their journey.

Make yourself visible with success

The Great Learning Impact

Great Learning’s mission in delivering quality education has translated into a space where we seek to enable every professional to be data-proficient.

  1. Our analytics, data science and big data programs are taken by several thousands of professionals every year.
  2. Collaborations with Great Lakes Institute of Management, Chennai and Illinois Institute of Technology, Chicago have translated into a strong global presence.
  3. Learning centers in six cities such as Bangalore, Chennai, Gurgaon, Pune, Mumbai, Hyderabad.
  4. Alumni presence across all work experience brackets and leading MNCs and Indian companies.
  5. Over 60% of our alumni have transitioned to roles in analytics within 6-12 months of graduation.

Great Learning impact in analytics and big data

Choose Your CAREER Path

Great Learning offers a plethora of blended learning programs with industry-relevant curriculum and award winning faculty. You can choose from a mix of our specialized programs based on your requirements:

  1. PGP – Business Analytics: 1 Year weekend classroom and online program that trains you on analytics and data science.
  2. PGP – Big Data: 1 Year weekend classroom and online program if you want to build your career in Big Data technologies and Machine Learning.
  3. Executive MBA: 1 Year weekend classroom and online program to become a data driven manager and specialize in analytics, product management, operations, finance or digital marketing.
  4. Business Analytics Certification: 6 Month online program where you learn from India’s best analytics faculty and get 40 hours’ worth of personalized mentorship from analytics professionals.

Choose your career patch with great learning's executive MBA, BACP, BDA and business analytics program

Also Read: Harish Subramaniam’s 7 Things You Need to Know About the IT Slump

IT Layoffs: The Hire and Fire Scenario

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Economists have stated time and again that in India, growth is owed not to the creation of jobs, but is instead driven by capital deepening – an analysis that seems to be backed by the numerous articles on mass IT layoffs. But by digging a little under the surface, it is clear there is a solution available within, one that requires a tweak in the game-plan of potential hires.

Fire and Hire

The National Skill Development Corporation (NSDC) has predicted a clear shift, from the need of minimally skilled hires to the ones that have kept up with the changes in technology and software. This shift has assured that the looming IT layoffs are now synonymous with ‘fire and hire’ – the lack of skill or proficiency being rapidly replaced with better resources for maximum results. As larger companies scale up their operations and update technologies, it is expected from employees to learn and upgrade their skillset. Let’s find out how you can turn the tide in your favor.

What is the Reason for the Shift?

With a clear shift in the required competencies, the issue has now become an inevitable problem for both companies and employees. It is essential to start a dialogue at the root of the issue – the need for providing training to employees and calibrating the resources to perform at an advanced level while they pursue their careers.

With evolving business processes, a focus on specialization, and profiles becoming obsolete due to automation, graduates fresh off the boat need to keep training themselves once they enter the industry’s workforce.

How to Move to the Top of the Pyramid?

While some companies have gone head first into the cycle of firing and hiring in order to maximize their efficiency, there are others who have chosen to retain their workforce, hire young blood, and tackle the issue through on-the-job training and delegation.

The best place to start is to check if you fit the criteria that HR departments are on the hunt for – the ability to adapt to new technologies and a drive to re-skill within newer frameworks such as data science technologies and programming languages.

The fire and hire movement makes it important for an IT worker to try and retain his post by focusing on gaining cross-functional expertise, upskilling & adapting newer technologies, to cut through the intense competition.

While your company may offer in-house training programs, there is an emerging need or rather an opportunity to go beyond the basics. Employees will need to look for certificate courses and training institutes for a specific skill set. It is recommended that you utilize your IT skills in new domains. There are many upcoming fields in IT that are both lucrative and promising. For example, Big Data Analytics and Business Analytics are both sought after courses.

Freshers and Transition Seekers

For all those who are beginning a career in IT, there is much to be hopeful. Every chart and table points to the fact that young blood is in high demand, but expertise and aptitude are still most important. On the other hand, transitions may seem frightening but it can be translated into an opportunity to grow. If you acquire the correct skills, it will help you to move towards senior positions and specialized job roles. Constant learning is now an important part of the game.

The situation may seem difficult, but the opportunities for jobs within the IT sector are intact, it is only the need of the hour that has altered. New fields have emerged, ranging from robotics, big data analytics, and artificial intelligence to cyber security, internet of things, business analytics and cloud computing. Choose a way to best utilize your skills and aptitude, take up internships and training courses, and once your skills are up-to-date then you’ll see that the job market hasn’t changed all that much.

Also Read: 

How IT Professionals Can Prepare Themselves to Deal with Layoffs

How to Get Your Way Around the IT Layoffs: “Scale to Skill”

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4 Reasons Why Professionals Are Moving to Business Analytics

Reading Time: 3 minutes

A decade back, professionals lived by the conventional career rules and followed a predictable job trajectory. It was commonplace to see a large number of professionals continuing in the same job position for many years together and even more common was sticking to the same job profile with little or no addition to responsibilities. Changing careers or going back to education was highly unusual as a person could see success staying in one profile over the years.

This is not the case anymore. A look around and we see a considerable number of professionals, especially with 3-4 years of experience, enrolling in professional courses or shifting careers. So what has led to this transformation? The answer to this question is multifaceted — from increased awareness to volatile business requirements, evolving technology to thriving startups offering high salary packages have all played their part in bringing about this change in approach.

Let’s look at the key factors that are motivating professionals to take up executive education or shift to business analytics:

Fast-Track Career Growth:

Moving up the corporate ladder in a straight line through promotions is a very slow process and takes professionals certain amount of years to reach leadership positions. Completing an executive education program helps professionals catapult their career and reach top positions faster in a shorter span of time. Equipped with the new competencies gained through the executive education program, most professionals are moved up the hierarchy level by their present organizations. Shift to analytics is also driven by the need to boost the career. With analytics emerging as a business necessity for every organization, professionals see immense potential in the space as a career choice.


Today’s dynamic workplace requires constant upgradation of skills and keeping abreast with changing technological advancements. To get recognized in their organizations, professionals need to constantly upskill themselves and look at expanding their scope of expertise. Most organizations look for specialized skill sets like decision making, leadership capabilities, teambuilding, and communication apart from comprehensive knowledge of job function in employees at higher positions. This is where executive education comes into the picture.
Talking about skills, business analytics capabilities are currently most sought-after. With every organization looking at data-driven decision making, there has been a spurt in the demand for professionals with analytics skills in the last few years.

Soaring Salaries:

A lot of professionals enroll in executive programs to get a significant hike in their salary through placement opportunities provided by institutes. On similar lines, a number of professionals are shifting to business analytics due to higher pay packages. While there is massive demand for analysts and data scientists, there is a huge scarcity on the supply side. According to staffing solutions company, TeamLease Services, India is expected to see a shortage of nearly 2 lakh data analytics professionals in the coming years. Organizations are thus ready to spend big bucks for the right skills.

Starting an Entrepreneurial Journey:

Business analytics and executive programs are increasingly being considered by entrepreneurial enthusiasts. While the focus on developing leadership and managerial skills are the biggest advantages of executive programs, networking opportunities are a huge plus too. A typical classroom of an executive program includes a diverse pool of professionals from varied profiles and industries. This enables professionals looking to venture into entrepreneurship to increase their contacts alongside gaining knowledge about all aspects of business.
Business analytics is also high on the list of entrepreneurial enthusiasts to tap the potential of niche technology and growing market. According to a report by IDC, the big data and business analytics market will grow to $203 billion by 2020.

In the coming years, as the companies continue to expect broader skillsets from employees at leadership roles, both business analytics and executive programs will see further increase in popularity. The key for professionals to maximize the benefits lies in choosing a high-ranking program from an institute of repute.

Big Data and Workforce Analytics: Powerful and Underutilised

Reading Time: 2 minutes

Workforce data analytics can drive performance and help businesses stay ahead of the competition, according to WFS Australia, the Workforce Software Company.

WFS says businesses that implement automated workforce management systems have access to large amounts of data on employees and business processes. These sets of big data can then be combined and analysed to deliver ongoing improvements to business performance, the company says.

Leslie Tarnacki, WFS VP of human resources and GM ANZ, says, “Businesses must move away from relying on simple metrics and anecdotal evidence, and become more data-driven.

“Automated workforce management systems with integrated deep analytics tools give organisations insight into where business processes can be improved to drive bottom line results, and stay ahead of the competition.”

WFS Australia has identified four key ways workforce management data capture and analysis can improve business performance:

1. Fill knowledge gaps

Data analytics can help businesses make better informed decisions, WFS Australia says. Data offers unbiased insights and reduces the influence of human emotion in workforce management. The depth of insights delivered by analytics tools grows as data is continuously captured by automated systems. Businesses should start to implement workforce management analytics as soon as possible to achieve a competitive advantage, according to the company.

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2. Identify patterns and best practices

Automated systems can capture, combine and analyse data on workforce scheduling and labour activities, and other enterprise systems, such as financial applications. This deep analysis can provide detailed snapshots of business processes and help identify best practices.

Tarnacki says, “Visual reports make it easy for businesses to identify patterns in organisational processes and outputs, and put them into action. This lets companies better assess how current practices need to change to meet business goals.”

3. Drive value from employees

Analysing big data from workforce management systems can provide a wealth of information to improve employee productivity and reduce staff absenteeism.

Tarnacki says, “Understanding employee preferences and customer demand patterns can help the business improve scheduling. Balancing the demands of customers and employees improves satisfaction, which is linked to the bottom line.”

4. Visibility into skills shortages

Workforce management systems that are integrated and cloud-based help businesses gain visibility into where skills gaps exist in an organisation. Analysing the big data generated within workforce management systems helps to identify and develop appropriately targeted employee management programmes and improve the business value of human capital, WFS Australia says.