The course offered an exhaustive curriculum – Dipankar Neogi, Sr. Data Analyst at Indegene.

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There are times when professionals in a specific domain reach saturation and do not enjoy working in their domain. At such times, one can either succumb to the monotony or rise up to look out for opportunities in other domains, sometimes drastically different from their current role. This is what Dipankar Neogi did when his marketing career reached its saturation point. Read how he transitioned to a data analyst role from being a marketing professional.  

What is your professional background?

I had completed my post-graduation as an MBA from Ramaiah Institute of Management Science in 2010 and worked for 7 years in Marketing with a firm in Bangalore. In order to upskill and upscale my career, I took a course in Business Analytics and Business Intelligence at Great Learning. Presently, I am working with Indegene as Senior Data Analyst.

Why did you choose Business analytics for upskilling and why did you choose GL?

I was not happy with the role I was working in previously. There were no growth opportunities and the remuneration not satisfactory. I had two options; one was to take up a full-time technical course and the other was to pursue an Executive MBA or get through GMAT to ISB or IIM. There was a lot of buzz for Analytics and Data Science and some of my friends suggested some courses. I did research and came across the GL’s BABI course. Since it was more business-related than a tech-driven program, I took this course as it suited me the best.

How was your overall experience with GL?

The course offered an exhaustive curriculum. Now since I am working in an Analyst role, I see how well-versed the course is. It provided detailed learning in Statistics, Regression, Conjoint analysis, etc., all of which I am using in my current role. The classroom experience made me realise the dream of being from a reputed firm. Working with peers and coming together on weekend classes or for projects, is what I recall as the most memorable time at GL.

Share your experience at the Interviews?

I got interviewed and shortlisted by many companies. I realised some patterns in the questions asked during the interviews. I figured out that the interviewer mainly focussed on my learning of the project that I had mentioned in my CV. In some interviews, I was given a real-life situation and was asked to give predictions for them. They judged me not only on my answers but also on my perception of the situation and the approach towards it.

Any advice to future aspirants?

Firstly, you need to focus more on projects. Apart from the Capstone project offered by GL, work end to end on some projects of your own. This will give you clarity and confidence with the subject matter. And secondly, focus on mastering one domain at a time. This will give you a stronghold in the domain.

Upskill with Great Learning’s PG program in Business Analytics and Business Intelligence and unlock your dream career.

Your essential weekly guide to Data and Business Analytics 

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

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

Businesses Face Three Biggest Challenges While Leveraging Big Data

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

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

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

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

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

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

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

Times Techies: Upskilling is Key to Meeting Demand For Analytics

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

Happy Reading!

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

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

4 Challenges with Leveraging Analytics — and How to Overcome Them

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

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

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

What Matters Most in Business Intelligence, 2019

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

The Coolest Business Analytics Companies of the 2019 Big Data 100

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

Top Five Business Analytics Intelligence Trends for 2019

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

 

Happy Reading!

 

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

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

What has your professional journey been like?

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

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

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

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

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

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

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

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

What did you like the most in the program?

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

Share your experience of the interview with Mercedes.

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

Any advice to aspirants who wish to take this course?

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

 

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

 

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

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

What is your professional background?

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

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

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

How was the overall experience with Great Learning?

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

How was your experience at the interview with Indegene?

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

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

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

Any advice for our future aspirants?

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

 

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

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

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

What has your professional background been?

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

How did you develop an interest in Data Science?

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

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

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

What did you like the most in the program?

There were many things that I loved about the program.

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

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

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

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

Share your experience of interviewing with Ugam?

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

Any advice to our future aspirants of this course.

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

 

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

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

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

What is your professional background?

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

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

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

What did you like most about the program?

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

How was your overall experience at Great Learning?

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

Share your experience of Career fair organized by GL?

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

 

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

Big data jobs are out there – are you ready?

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Big data is increasingly becoming part of everyday life. Network security companies use it to improve the accuracy of their intrusion detection services. Dating services use it to help clients find soulmates. It can enhance the efficiency and accuracy of fraud detection, in turn helping protect your personal finances.

Big data” is a catchall term for any data set of exceedingly large volume. It could be transaction information at a credit card company, invoice data at an online retailer, meteorological measurements from a weather station. All these data sets have unique characteristics that make it extremely difficult to use conventional computing technologies and techniques to store and process them for analysis. Their variety is daunting, and high velocity is required to handle them in a timely manner.

Organisations in any field can use big data to enhance their effectiveness, which is why there are seemingly unlimited career opportunities in big data these days. The big data industry is growing fast, with the market predicted to grow at a compound annual growth rate of 23.1 percent over the 2014-2019 period.

So who is going to store, manage and process all this information? Well, why not you? Companies are starved for people with this kind of expertise. Big data is a growth industry and people from a variety of academic backgrounds can find successful careers in this area.

But you didn’t major in “big data”? Don’t worry. Your academic background shouldn’t be an inhibiting factor when you start to contemplate becoming a big data professional.
People working in fields such as physics, bioinformatics, statistics, political science and psychology are already heavy users and analyzers of a large amount of data. The transition from these types of disciplines to big data analytics could be relatively smooth.

If your original education and training didn’t focus on data, that’s not necessarily a problem. Your own discipline-specific knowledge, insights and perspectives can be valuable when figuring out how to leverage big data in the most sensible way. The only catch is you need to be willing and able to acquire the technical skills necessary to either analyse or work with big data.

Despite the unique nature of each big data career, there are common categories of jobs or career paths.

The most fundamental of these focus on data infrastructure – how the data is actually housed and accessed. These infrastructure jobs involve developing and maintaining the necessary hardware and software. A cloud computing environment is especially well equipped to handle big data due to its scalable nature.

Big data management professionals rely on the data infrastructure to actually populate it with data and manipulate them. Conventional database management workers are natural candidates who could be trained quickly to work as big data management experts. They already have general database management knowledge. But they need to get up to speed on dealing with big data. These can be much more unstructured than what you find in a traditional database, where each record conforms to a certain structure in the form of data fields and types. Imagine a student record, with discrete first name and last name fields. Big data often doesn’t have this kind of nice organization: It can be as unstructured as a bunch of Twitter feeds or Facebook postings by millions of users.

Statisticians are essential in the big data industry. They’re the number crunchers who specialize in analyzing and interpreting the data. There are many advanced techniques used by statisticians, which require years of training. They depend on the data infrastructure providers and data management workers to store and retrieve their source data for further processing.

Visualization specialists are also key in the big data industry. One of the most critical aspects of big data analytics is communicating the results of an analysis to decision-makers – and they often lack expertise in data interpretation or statistics. Visualization empowers a layperson to understand the significance and implication of the numbers produced by a big data analytics effort. Think about being presented with a large set of numbers that you’re told indicate a changing climate. It’s a lot easier to understand the data’s significance when shown a graph with a sharp turn upwards, implying exponential growth.

Finally, machine learning experts focus on automating the statistical and visual interpretations of big data. Automation is critical, especially when the amount of data to be analyzed is beyond human capabilities – as is the case in most big data scenarios. Machine learning is based on self-learning algorithms.

These computer programs autonomously enhance their own performance and accuracy through trial and error.
Many curriculam available today through universities provide foundational knowledge in all the technical areas of big data. Students can eventually pick their speciality, which can be further honed in a graduate program.

One of the core qualities often found in big data professionals is the willingness to learn. The big data landscape is dynamic and constantly requires continuing education. To survive in this environment, you should enjoy learning new skills and be unafraid of trying out novel technologies.

And the most successful big data worker isn’t just a numbers geek. People in this area also need to have a business mindset. Companies are always eager to leverage the information stemming from their big data analyses. They’re looking for people who naturally make connections between actionable information and what the companies are striving to accomplish in both the short and long term. If you aren’t interested in linking these two interests, your job security may eventually be at risk.

You could focus on any one of these areas of big data – data infrastructure, data management, statistics, visualization, machine learning – and become an expert. Another option is to become a generalist; you have exposure to all these technical requirements and as a project manager works with the specialist to solve any given problem.

As a university professor specializing in information sciences and technology, I encounter many students who figure out their true passion for big data only during their senior year while doing their job search; by then, they’ve missed a golden opportunity to prepare themselves academically for this thriving emerging profession. The earlier this epiphany comes before graduation, the better. But there’s nothing holding back grad students or adult learners from investing their time wisely and acquiring the necessary skills. This is especially true in the field of big data analytics due to the abundance of learning resources in the form of both self-learning and traditional education.

Career Break? Add these hot skills to your resume to entice the recruiters!

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A lot of working women take mid-career break of 1 to 5 years to look after their children when they are very young. However, once the kids grow up, when lot of women want to resume their careers; they often hit a wall – as their skills (if in a technical field) are considered to be obsolete as they carry no experience with the latest technology or practices mandatory in their respective industries.

Well, actually there are so many things those women can do in order to prepare themselves for the transition back into the workforce. The most important amongst them is to upskill oneself through short term or long term course in the chosen field, networking with ex-colleagues to find relevant opportunities in their industry, or perhaps start with internships or unpaid voluntary roles to get a foot in the door.

Analytics is a fast growing industry today and data scientist is becoming one of the most sought after professions in the past 2-3 years. Enrolling oneself in an analytics course can be one rewarding opportunity to get back to work life after a sabbatical. Few compelling reasons that make Analytics profession a great career option are:

In demand skills

The field of data mining and analytics is growing rapidly, creating a substantial gap of talent, globally. Businesses today trust data mining and analytics for informed business decision making, and look for talent who can work with data and help them in business decision making.

Multi-Industry opportunity

Today, there is hardly any industry, established or growing that does not opt for data driven decision making. Be it healthcare, telecom, sports, ecommerce, finance or government, and across functions like HR, management, operations, sales, and marketing, Analytics does play an eminent role. Whatever be person’s past industry or functional experience, analytics skills can be one good addition to existing skills. For example, a person who has worked in the banking sector can learn about financial analytics, while one who carries retail experience can learn how analytics works in marketing and ecommerce, IT professionals can get into Big Data Processing and analytics, where the requirement is usually of strong IT and programming skills in addition to data crunching knowledge.

Management life-skill:

Often at the time of taking a break, most of the women are in entry level – mid management level leadership roles, and prefer resuming the career at a same level. The competence to make better and informed business decisions on the basis of data is becoming an important part of any management role, irrespective of function. Thus, an inclusion of data analytics skills will supplement wholly supplement the past experience and make resume more alluring to potential employers.

Well, there are many barriers to entry for women looking to get back to the workforce after a long break.  Even after extensive prior experience, women do find it difficult to get back into the job market with no experience of current market tools and practices. Hence, to increase their chances of being shortlisted for roles relevant to their experience and skills, they should update their skill set, especially in areas that are in great demand such as analytics. If you are interested, check out  comprehensive courses at Great Learning, one of India’s most premier learning institute.