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

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

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

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

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.

Why PGP-BABI?

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

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.

The GPU

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.

CPU

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.


RAM

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.

Hard-drive

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.

OS

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: https://in.pcpartpicker.com/list/

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

 

 

The Great Learning advantage is very apparent now that I work in the industry: Sownthiriya, PGP-DSE Alumnus

1. Why did you take up the DSE Program?

While I was doing my IT engineering degree, I came across a Business Intelligence elective. My friends and I were very interested in it and would discuss current trends which led me to be interested in data science.  I wanted to learn more about data science outside of my college, but I took up a job with Accenture because I was advised that it would look good on my resume. I worked there for 9 months, and it was a monotonous job because it felt like I was doing technical support, even though I was supposed to be an Application Development Associate. Great Lakes is a renowned institute in Chennai, and they offered a 5-month program (unlike Manipal’s, which was longer) I decided to take up this program.

2. Did you consider any other programs before enrolling for DSE?

I considered Manipal, Jigsaw and Edwisor’s programs but almost all the programs are exclusively online. I had even paid the application fee for Manipal’s course, but I did not take it up because I wasn’t sure if I would be regular in attending an online course.

3. What did you like the most about the program?

The Great Learning advantage is very apparent now that I work in this industry. Each module was a week long, so each topic was covered in-depth. The program has imparted skills which I feel are far superior to people who have 6-7 years of experience in this domain, because we are able to explain certain parameters and interpretations better than them.

This program gave us access to industry experts who guided us about real-world applications of what we learnt. I’d like to specially mention my Capstone mentor Ms Sowmya, who is a BABI alumnus. Her guidance really helped us put into context what we had learnt in the past 5 months. I’m working for Mahindra now, and everything she guided us through is helping me in my reports now.

4. How was the program curriculum/structure?

The program is well-designed and the curriculum is based on real industry requirements. Everything from the basics to in-depth concepts are covered in the program.  

5. Were you satisfied with the career & placement support?

The mock interviews that were conducted helped us learn about how to crack real interviews. I was interviewed by Mr Alok Tiwari who’s a BABI alumnus working for Flipkart, which was eye-opening for me. His pointers helped me get shortlisted in many companies such as Mahindra and Mahindra, Kantar, Jana Small Finance Bank, HPE and Merkle – Sokrati. The group assignments and career workshops also were of great help.

6. How did you get into Mahindra and Mahindra?

I had three offers from Mahindra & Mahindra, Jana Finance bank and Kantar. I did a lot of ground research as I have relatives in this field. I chose Mahindra and Mahindra because they just started an analytics team and there’s a lot to learn.  We cannot master everything in a 5-month program, so I wanted to work somewhere where I can learn as well as contribute. I didn’t want to go anywhere where everything is already in place. It’s like a start-up environment with job security.

7. How did the program help you in getting into Mahindra & Mahindra?

I give credit to the program for teaching me all about Data Science. In the interview, the first round was the aptitude test, after that, they had conducted a hackathon which was very similar to what we did in our classroom. After the hackathon, I had given one technical and one HR round and then was offered Associate Analyst role. For now, I am using Qliksense, R and python in this role.

8. Do you have any advice to other aspirants?

You will be leaving your job and taking up the program, so you will be benefited only if you give a 100% effort. Faculties are there to connect for everything and we have program office available also. Make use of everything and get the best out of the program. If I have any doubt, I used to clear it then and there, which helped me a lot.

I had recruiters reaching out to me after this program: Parag Janrao, BACP Alumnus

1. Why did you take up this program?

After my MBA, I’d joined NALCO as a management trainee. I was handling projects related to logistics, supply chain and finance for 2 years. I had some experience with Excel and VBA programming, and one day I got a mail about this Analytics course. My manager and I were looking at available options because we were dealing with huge amounts of data and we were not sure about what techniques to use to get correct outputs. But most of the programs available are classroom programs, the comparison was majorly between Great Lakes and Jigsaw. We tried to reach out to alums of BACP program via LinkedIn about the program. We got positive feedback about the program from them about the mentoring sessions and that gave us the confidence to go with Great lakes BACP program. We really like Alumni feedback and mentors profiles, and that is the main reason I joined GL.

2. Why did you choose an online program?

I wasn’t looking for a full-time classroom or blended program because I know that it will be very hectic to go and attend the classes. Initially when the videos were shared, basics were understood and then during the live mentoring sessions, our doubts were clarified. It depends on personal interest. I was very much interested, so I’ve learned a lot.

3. What did you think about the fee structure?

I felt that it is bit highly priced when I compared with Jigsaw and others. That is when the inputs from alumni convinced me that this has more weightage and value than other programs available in the market. When I had updated in my LinkedIn profile that I am doing this program, recruiters reached out to me asking about the program and they started reaching out me looking at my e-portfolio. 

4. How did you transition to Hasbro?

I have moved into my new role after 6-7 months of completing the course. I had no prior experience in analytics because I was working for the functional domain of planning, supply chain and logistics. That was a major issue for me because most of the recruiters look for candidates with hands-on experience in analytics. I had used forecasting and time-series in my previous role then. Hasbro selected me based on my background knowledge of supply chain and logistics, because the work I am doing now is on data analytics reporting with a  focus on logistics & supply chain. We mainly work on Excel and other tools and PowerBI for visualization.

5. Any advice or tips to the aspirants?

There are 6 modules and candidates don’t have to be perfect in all the 6 modules. They should be perfect and thorough in at least one module that they like, and they can get a job in that. In my case I was really interested in forecasting and time series and so I started working more on those skills. If you really work hard on getting the concepts clear, you should be able to make a transition easily.

6. How was the support from the program office?

Bhumika who was coordinating with us was of great help. She never missed to update us on any submissions, deadlines etc. Mentoring sessions were great and helped us to clarify our doubts.

Transitioning from Application Support to Data Analytics: Nithin Shetty, PGP-DSE Alum

1. Why did you choose this program?

I did my engineering in Computer Science before working for TCS for 19 months. I worked in Application Support at TCS where I had the chance to collect, process, and format data. We would then send it to the downstream team so that they can perform analysis on that. That was the time I got to know about Analytics in MBA, in certain IIMs and top colleges. So I thought I will take up analytics course, so I quit my job and wrote the CAT exam that year. But my CAT percentile was not good enough and that was the time I got to know about Great learning that they are providing a course in Data Science and Analytics. I decided to go for a Data Science course rather than a 2 year MBA. Some of my seniors suggested that the curriculum and the course content that Great lakes provided was very good. So I planned for the DSE Program. I shifted to Bangalore just for this course.

2. Did you consider any other options before enrolling for this program?

I heard about Aegis and other programs as well. But I Knew that Great Lakes has a B School and also well known for Data Analytics programs. All the professors are highly experienced. I had an interview for Great Lakes (for MBA) and that is how I got to know about Great Learning and about Data science course. I spoke to Karthik sir and was convinced to take up this course.

3. What did you like the most in the program?

The way the faculty explain the concepts is very good. They go deep into the topics and even now after I started working, I realized that I can better understand the core concepts than others. This made me more confident in my current role. I can say that Rajesh Jakotia had really helped us understand the concepts. He helped us understand and do the calculations by ourselves so that we get a hang of it.

4. How is the program curriculum/structure? How helpful was it?

Before this, I didn’t know exactly what analytics was. I just knew that we should shape data and get insights out of it. But after I joined, I got to know the exact process that how analytics works and how it impacts the business flow through algorithms, formulations etc. One thing that I like the most is that we get to access the videos whenever we want, for our reference or for clearing any doubts. That was the great part.

5. How is the placement support at GL?

First, we were very much worried because we are the first batch and we didn’t know the exact placement procedure. We were sceptical whether placements will be organized or not. We didn’t expect so many companies to flow in, we just thought that some 4-5 companies will come and most of the companies are pretty good.

6. What kind of job offers did you get?

I had offers from 3 companies Mahindra and Mahindra, Jana Finance and Kantar. I have opted Mahindra and Mahindra because the role is not restricted a particular domain and they are into multiple sectors and I get to learn from all those divisions. Mahindra is currently working on Artificial Intelligence as well so it will be a great opportunity to learn that as well.

7. How was the interview process at Mahindra and Mahindra?

I didn’t feel that the interview was that tough because of the rigorous sessions and learnings I had in the Great Learning program. We had some 3-4 days’ time before the companies came in so I went through the concepts well. We had a hackathon, where we managed to perform very well. After that, we had the code review and then the interviews. Considering the course, we were able to explain the concepts and algorithms better. That is what impressed the interviewers and they offered the role. I already started working on some interesting projects here.

8. Any advice to the aspirants?

Many people have approached me on Linkedin and I’ve referred my cousin to join the program and he will be joining shortly. Every class is important and you cannot take any class for granted. You have to give your best and work seriously on the mini projects. After every course, they will have tests and you get to know your areas of improvement.

From Freelancer to Full-time: Dalon Lobo’s Machine Learning Career Transition

1. Why did you take up this program?

I was self-employed when I took up this program. I was working on a finance project, building an analytics dashboard. I had some Python knowledge through projects like these, and I knew that would be useful in learning Machine Learning too. I decided to take up a course because of that. My projects took up most of my time, and I had to travel to Bangalore for the classes, but it was worth it.

2. How did you get into Videoken?

Our CEO Manish Gupta had come to Great lakes for an industry session. After the class, I had reached out to him and asked if he can offer an internship. He put me in touch with the technical guy, and I was hired as an intern for 3 months. While I was working there, they were impressed with the model I had built, which was put in production. They ended up offering me a full-time job, without even conducting an interview. Everything just fell in place.

3. What is your role at Videoken like?

I am building a speech recognising engine using a model called deep-speech. My current role is to improve this model using the current data that we have and customize it. I am also working on punctuation model, which is in the field of Deep learning.

4. What did you like the most about the program?

Professors like PKV and Mukesh Rao are very knowledgeable and really good. That’s what I liked the most about the program – the faculty.

5. Did you consider any other options before enrolling for this course?

There were few programs at that time, and now there are plenty. When I started, there was a program from Manipal. Before I joined this program, I took a short course in Udemy and realized that many things were messed up and needed a better structure. I saw the curriculum and it looked good. So I opted for this program.

6. Did the course help you to do better during your internship?

Yeah of course even if it is deep learning, the base is all stats and ML etc. Just to talk with these technical people, you need to be familiar with the terms they use to understand what they mean.

7. Advice to the aspirants? How to get the most out of the program?

Just attending the lectures will definitely not be enough. They will have to put in some self-study. Self-study is the main thing which actually gets you through the program. Even though the lecturers are good, if you don’t do anything from your end, it will be wasted.

8. What kind of tools do you use now?

*Python is the base language that I work on

* R

* Linux environment for development

*Couple of GPU’s

*Jupyter or Spyder