PGP-CC course helped me in passing the AWS certification: Sai Venkatesh, PGP-CC Alumnus

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How PGPCC course helped me in passing the AWS certification?

I have completed and passed the AWS Solution Architect Associate Exam in the month of March. Frankly speaking, I couldn’t prepare well and have started preparation just a week before the exam. As far as the little knowledge I gained for the exam, I will give one of the credits to our PGP- CC course along with a cloud guru. Curriculum relevance, quality of faculty and learning material are the things that I liked the most in the program.

This is how the program helped me to achieve AWS Certification-

  • Mentorship sessions – The mentor sessions have provided an opportunity to clear my queries on various AWS services and also got to know about the new issues from my Batchmates.
  • Office support – Program manager was always available and practical issues were taken care of.
  • Curriculum – The curriculum has helped me to get exposure to multiple AWS services.
  • Assessment – Assignments and Projects have enabled to design the architecture and perform various implementations over them.
  • My Batchmates – Mentorship sessions have provided an opportunity to interact with fellow learners and learn new approaches.

I was able to bag an offer from a company even before the completion of the course: Christine, PGP-DSE Alumnus

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Career success is possible, and almost inevitable when you have access to the right kind of guidance, and a program that delivers positive career outcomes.  Read on to find out how Christine leveraged the PGP-DSE program to step ahead in her career.

How did you get into the world of data science and machine learning?

In the past, I’ve dabbled with both data science and design. My first brush with analytics was during my short stint at Mu Sigma and later, I moved onto design and then back to analytics in a telecom service company before taking up the PGP-DSE course to get a jumpstart into the world of machine learning.

What was your motivation to choose the PGP-DSE program by Great Learning?

I love problem-solving, and machine learning and data science progressively drew my interest. After doing comprehensive research, I applied and got offers from two institutes to take up the data science program. I chose Great Learning after reading a lot of blogs and the high ratings on Analytics Vidhya and the positive feedback I received on LinkedIn from the alumni.

What did you like the most in the program?

The quality of faculty is impeccable and the concepts were explained deeply from scratch. I know how each model works under the hood and that was the best part. The practical sessions helped me to see in action what I had learned. Overall it was the best blend.

Did the career support from Great Learning help you in getting a job?

The fact that I was able to bag an offer from a company of choice even before the completion of the course speaks volumes about the support and the rigorous training that helped me become job ready.

Are the learning outcomes of the program helping in your current role?

Currently, I’m working in the machine learning space and building classification models and implementing concepts learnt at Great Learning on live data

Any advice you would like to give to the aspirants of the PGP-DSE program?

The program is very well built to train open and eager minds. I would suggest people who are passionate about data science but don’t know where to start or people who have read these concepts online but have a fuzzy idea of what machine learning is all about to take up the course. I hope to encourage and assure them that they are in good hands having enrolled at Great Learning and this course would be a launching pad for their career just as it was for me.

I got this job through Great Learning’s career fair: Shawrya Sharma, PGP-DSE Alumnus

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He had the ideal start. After securing a job through the campus placement in a reputed company, he was soon working on data science projects. What followed next is a tale of boldness and faith in oneself.

Read on to know how Shawrya Sharma, our DSE alumnus, quit his job and joined the DSE program by Great Learning to upskill himself.

Why did you take up the DSE program?

I used to work on a data science project where I used to do analysis and stuff in my previous organization, that is when I developed an interest and wanted to learn Data science to upskill myself. So I left my job and took up this program.

Why did you choose Great Lakes?

I was considering three programs – Great Lakes, IIIT, and Manipal. IIIT was more of an online course and the duration of the course by Manipal was one year. I found Great Lakes PGP-DSE program to be better compared to the others and that is the reason why I opted for this course.

What did you learn through this program?

For our batch, both R and Python were taught. For me, everything went very smooth. The faculties were very nice.

Shawrya’s hard-work was paying off. In the Great Learning Excelerate career fair, he was offered multiple roles by leading companies.

What career opportunities did you get through the program?

I got into this role because of one of the career fairs that happened in Chennai. It was a great chance to be interviewed by some good companies. I had an opportunity to be interviewed by 3 companies from which, I was offered roles by two companies.

The projects prepare the candidates for industry-relevant problem-solving.

How did you approach your interview with KPMG?

I have actually worked on an analytics project on my own and all the three companies asked about the same. I can say that I got into two companies because of that project and the grooming that happened during the program. My Machine learning skills were tested during the interview with KPMG.

How did Great Lakes help you advance in your career?

Like everyone, even my main concern was placement. But the Great lakes team has contacts with many good companies. If you are sincere and perform well during the program, you will definitely land a job in a good company. I can say that the mock interviews, career fairs, and CV reviews were quite helpful and played a major role in my transition.

Would you recommend taking up the PGP-DSE program?

I would definitely recommend this course to anybody who is interested, but you have to be thorough with all the concepts of stats before starting or else one might find it a bit difficult as this is a 6-months rigorous program.

“I’m moving to Dublin next week, thanks to Great Learning”:Sai Pavan Kumar Bhamidipati, PGP-BABI Alumnus

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It is said ‘Failure is just a stepping stone to success’. In the case of our alumnus Pavan, his persistent attempts at interviews helped him to hone his skills to perfection.

What motivated you to take up this program?

I wanted to get into analytics. While working on data warehousing projects at Cognizant, I developed an interest in data science and that’s when I decided to take up a full-time program. I wanted a program which would add value to my resume and my career as well.

Why did you choose Great Learning?

I went through a lot of reviews before I locked in Great Learning. It is a more flexible and economical option compared to the other courses.

How did the program influence your learning journey?

I can say that the faculty and the curriculum are the best. The sessions were so interactive and faculty (like RL Shankar) were really good. Video recordings assisted for reference whenever we were struck.

Were you able to achieve the career goals you had set before joining this program?

After graduating in Feb’18, I tried rigorously for a transition to a full-fledged analytics role. Though most of the job descriptions demanded 3-4 years of real-time experience, I attempted each and every interview to understand the industry expectations. I would like to mention here that whatever we learnt in the Great Learning BABI course met the expectations of the industry. The knowledge gained has given me the confidence to attempt various hackathons conducted at Great Learning and several other platforms as well. Every time I failed, be it at an interview or a hackathon, I was able to analyze the mistakes well. All thanks to the knowledge gained from the program. Without having a good understanding of what we are attempting, one can’t analyze his mistakes & improve. Meanwhile, I was also able to get my hands on some real-time small scale projects in my current organization. After all such attempts, I am happy to share that I was able to crack Data Scientist Interview with ESB Ireland. I am moving to Dublin next week. All thanks to Great Learning again.

I would like to thank all the extraordinary faculty of Great Learning for such a great course & sharing their rich experiences which helped me in transitioning to analytics as a career.

Sharanya Kumar Gives her ‘Pre-Great Lakes Business Analytics Program (PGP-BABI)’ Self Some Insights

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Dear Sharanya,

It will pan out well in the long run. If you can trust yourself and prepare to persevere for exactly 1 year, your career will take off like magic. This one year will be hard but extremely rewarding and you will see how. But first, let’s fix the way you feel right now.

1. The Time You are Devoting in Your Capstone Project Today will Reap a Ton of Benefits Later – You have been thinking how you have taken an additional amount of time to just choose your domain, topic for the capstone project, and your mentor. But be rest assured and listen to your instincts. You know what you are doing. When you later get a new job at Verizon, you will do not one but two projects closely related to your capstone project. I can tell you that it will help you impress your teammates, managers, stakeholders, alike. Your mentor, Dr. Monika Mittal, will guide you way beyond your capstone project and you will develop an excellent relationship with her.

2. The Professors in the Class Who Seem Intimidating at First are the Most Impressive Set of Faculty Members You Will Ever Meet – You haven’t been in a classroom for a long time and studying may seem disillusioning right now. But give your professors a chance and see how their energy and creativity will grow on you. 60% of the course will be taught by industry experts and 40% by faculty members offering you the best of academia and industry viewpoints. Later when you are done with the program, you will still stay in touch with Mr. Raghav Shyam (Tableau) and Mr. Tushar Sharma (Web & social media analytics) and reach out to them for doubts in your current role at Verizon. You will reminisce about Dr. R.L.Shankar’s Ted talks, Dr.PKV’s support throughout the course, and how Dr. Bhardwaj will make everything simple for you to understand and have the most interesting classes planned.

3. You are Adding More Members to Your Friend List – Networking will be an extremely important aspect of pursuing this course. You may think how one class a month will let you develop any sort of relationship with your peers, industry mentors, faculty, and speakers. But this feeling will be limited to your first few interactions only. You will time and again reach out to these people even after you transition to analytics completely with Verizon. You will call them not only for doubts but because you would have become a strong part of the analytics community that seems distant and detached right now.

4. Focus More on Developing a Business Acumen than Learning ToolsYour professors will say that in the very first session and you will find it bizarre. You already know analytics and its application in the telecom sector. Why wouldn’t tools be important, you would think? But your professors are right! Just learning tools will not help you get into a techno-functional role or switch domains from telecom. It will require exposure to different domains and a deep understanding of how to derive business value by using analytics. You will unlearn to learn and this time it will be worth the effort!


Thanks to Great Learning for helping me change my career path: Murali Krishna, PGP-BABI Alumnus

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Why did you choose to take up this program?

I wanted to get into the field of Data science. As I was a full-time BI professional working on Data Analysis and Reporting, I felt this program would act as a career enhancement, to go beyond BI and extract actual essence of data, which helps in real-world problem solving. So, I took up this program when I was working for IndusInd bank. Once the program got completed, I got into Fractal Analytics.

Why did you choose Great Lakes?

There were other institutes like IIM-B, ISB, Insofe etc. providing similar kind of programs, but I decided to take it up at Great Lakes. The Learning consultant was really helpful in providing the proper information I needed. I am now happy that the purpose of taking up the program has paid off.

What did you like the most in the program?

The classroom sessions and the faculty who taught us were amazing. Professors like Dr PKV, Bappaditya, etc had really helped us whenever required.

How flexible was this program? How did you manage it with a fulltime job?

I could manage my work over weekdays. When you pay a certain amount of fee, that instinct would always be there. I used to travel for 4 hours every day whenever I had residency because I used to stay 25 kms from the centre, but I could manage that because I had the interest to learn. I was handling a team at IndusInd bank who were capable enough to do the work they had. I made good use of my free time.

How did you transition to your new job?

With immense pleasure, I would want to thank Great Lakes for helping me in changing my career path towards the exciting world of Data Science. It has been three months since I have joined one of the coolest Analytical companies in the industry and pursuing an end-to-end analytical project – helping my client to find the most suitable targets for campaign data. My solutions are helping them reduce cost on marketing and improve click-through rate and conversion rates.

Do you have any advice for the aspirants?

Complete transition overnight in 6 months is something that is not possible. A constant effort towards a fixed goal or target on which you can work is something that I would recommend. I don’t see myself as a data scientist yet but as a data analyst I know what to do and extract the result out of the data. If a student can think in that line instead of just changing a career, they will able to showcase the work they have done, which will be an advantage. It really worked in my case.

Why Data Science is a great career option for fresh graduates

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The last decade has witnessed mass digitization of various industries so that they can improve their efficiencies and increase their bottom line. This means that many companies have amassed large sets of data about their operations. Since the data they’ve collected to is so voluminous, the manual tools (like Excel) they’ve been using till now are unwieldy when it comes to garnering insights.

That’s where Data Science can lend them a hand – to organise the data better so that its viable to process it, and to give organisations insights on how to improve their processes. Data science is all set to take the job market by storm, having been heralded as the sexiest job of the 21st century by Harvard Business Review.

Data Science will find application in various fields of the future because no company can afford to adopt a business-as-usual approach to their products and services, because it will cost them their competitive advantage.

It’s already being used by the biggest companies in the world to deliver services such as intelligent agents that can predict consumer behaviour to offer them relevant purchasing options. Data Science is the bedrock on which artificial intelligence and machine learning are built, so such a foundational skill is certain to strengthen the employability of people proactive enough to learn these skills.

Consider this example: TCS offers 3.5 LPA as a starting salary to the freshers that they hire from colleges. Contrast this with candidates (with no work experience) who have Data Science skills, who get offered an average of 6 LPA. It’s clear that data science skills are highly valued in the market right now.

There’s only one problem though – there aren’t enough qualified professionals. According to recent reports, there are more than 50,000 data science jobs in India that are vacant due to a shortage of skilled professionals. This demand-supply gap has two implications. The first one is that data science professionals are being actively snapped up by companies right now, so they are highly valued. This will reflect in the scope of their work, and the remuneration that they receive. The second implication is that market forces always move towards balancing the demand-supply gap, which means that professionals are soon going to flock towards these courses. This gives you a small window of opportunity to learn the skills that you need right now, in order to secure your future.

The fact that we have a skill shortage, and that the future will be built on the back of data will ensure that data science will be a very lucrative and rewarding career option for graduates. It offers ample opportunities for growth and career development because of its wide-reaching potential, which will let you be a part of the new, data-powered future.

If you are looking for a Data Science program to launch your career in the right direction, check out this article on how to choose the right program for according to your requirements.

Rapid Fire with PGP-BABI Alumnus Utkarsh Kulshrestha

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Your Background?

I work as a Sr Machine Learning Engineer at Kloud9. Previously, I worked for a year at TCS as Sr Business Analyst and for around 3 years at Wipro as an embedded systems Engineer.

Why Analytics?

There is an immediate need to upskill to stay relevant. Most of the existing technologies are becoming obsolete and companies across the board are adopting analytics. People who don’t learn new skills will soon be on the way out and that’s why I decided to take up a course in Analytics. Analytics and Data Science are in a growth phase so it can be very exciting to work in this domain. Salaries are higher as compared to traditional roles in the technology sector.


I had evaluated around 3 different programs, but I found PGP-BABI by Great Lakes to be the best one. The profiles of the faculty members and industry guests really impressed me. It is also the most versatile program available and as a working professional, it helped me balance my academic and professional commitments.

What did you like the most about the program?

The Machine Learning module was very well structured. I also felt the domain-wise data sets are a very special feature of the program. This gave in-depth insights into the functioning of various domains. I can’t thank Professors P.K.Vishwanathan & Rajesh Jakotia enough for their wonderful sessions.

How did you bag a role at Kloud9?

After the completion of the Machine Learning module I got calls from 3 organizations for the role of Sr Business Analyst but I decided to join TCS. After working there for about a year, then I was offered a Sr machine learning Engineer role at Kloud9 and took it up. I used to practice with modelling techniques taught in the program on data sets that were available for free; this helped me understand the concepts a lot better and played a key role in bagging the Data Scientist profile at TCS.

Questions You Had to Answer in Your Interview?

Almost 80 to 85% of the questions asked in the interview were on topics covered extensively in the program. Some of the topics asked were ANOVA, modelling techniques, neural networks, Random Forest, Hypothesis testing, classification trees, etc.

Tools and Techniques You Use at Work?

I’m working on 4 different projects and these projects require me to work using R, Python, NLP, and machine learning algorithms.

Advice to Analytics Aspirants?

They should practice the techniques taught on multiple data sets and try to get a clear understanding of various business problems and how those can be solved using data.

Run experiments by building your own Deep Learning Machine

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If you are taking up a Deep Learning or Machine Learning course right now, the best way to learn is by running your own experiments. To run those experiments and train models with large data sets, you’ll need a significant amount of processing power. Theoretically, you could use your laptop to run some of these tests, but it will be a long and arduous process. So how do you get all that processing power necessary to run these resource-hungry tests? By building your own machine.

We can start by making a list of all the components you’ll need to build a functional Deep Learning setup. We are not considering cloud components yet because unreliable internet speeds make it impractical to expect consistent performance. Here we go.


GPUs are built for processing large amounts of graphics in video games. Graphics rendering is an extremely resource-intensive process, and GPUs were built for the express purpose of doing large amounts of computations. This makes them ideal for a variety of purposes which includes cryptocurrency mining, and of course, for running deep learning experiments.

The GPU is the lynchpin in the deep learning machine, so it’s customary for it to constitute over 50% of your overall budget. The more powerful the GPU, the lesser time you will have to spend waiting for all your results to compile. This allows you to quickly adjust the parameters of your deep learning models if you’re not getting the results you expected.

There might be many people differing in this stead but the most powerful GPU you can get right now is the GeForce RTX 2080 Ti. It costs a pretty penny, but its the absolute best that money can buy right now. For a mid-range GPU, Nvidia has the RTX 2070 that’s at half the price of the 2080 Ti.  For just a basic GPU, users can consider the GTX 1050 Ti. To really run large computations, you’ll need a dual or multi-GPU build, so plan accordingly to your budget.

The Motherboard

The motherboard is the foundation for all your different components, and it needs to be compatible with the GPU that you are selecting. It needs to have enough slots to support a dual-GPU build and needs 16 PCI-e lanes to be able to fully harness the complete power of the GPUs. The motherboard will need to support an i7/i8 processor and a DDR4 RAM. You can’t go wrong with the Z series from Gigabyte, or you can also choose from H or B series – just make sure it’s compatible with everything else.


When choosing the processor, you’ll need to pay attention to the number of cores, and the threads per core. The more cores, the better your parallel data computations will be, so choose at least a quadcore processor. The CPU is the conduit between the GPU and the rest of your rig, so we would again recommend a gaming-oriented processor to ensure that it can handle high loads.


Since the amount of RAM available dictates the size of the data that can be held in memory, you’ll need a minimum of 16GB of RAM. Make sure you get a 2x8GB RAM instead of 4x4GB so that you can upgrade your RAM later. You can choose the Corsair 16GB (2x8GB). It also has 3200MHz of clock speed which will ensure that access times for the RAM are kept suitably low.


We assume that the large datasets on which you will operate will have to go somewhere, so you need a big enough hard drive to house all of that. The prices of SSDs are dropping dramatically, but they’re still more expensive than regular hard drives. You can choose to put all your static data in the hard drive, and the data sets that need to get accessed frequently by your Deep Learning programs can go in the SSD. The only constraints here are the size, so choose according to your requirements.

Power supply

The power supply unit delivers the necessary power to all your components. The GPU is a very power-hungry component, so you’ll need to ensure that the wattage for is right. An underpowered PSU can seriously jeopardize the entire rig, so make sure you have enough wattage for the other components that you have chosen.


The most widely used OS for Deep Learning experiments is Windows, but you can save a few thousand by opting for free Linux distros such as Ubuntu.

Computer Peripherals

The quality, size or type of your computer monitor, keyboard and mouse are not going to affect the outcomes of your Deep Learning experiments, so you are free to choose whatever you like depending on your requirements and preferences.

To check for compatibility between the component you choose, you can check out this great resource which will suggest the right components for you:

Now that you know everything you need to build your machine, its time to start setting your rig up. Happy building!