The program covered all topics asked in interviews: Pugazhendhi, PGP-DSE Alum

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Even modest support can accomplish a big dream. Pugazhendi, our PGP-DSE alumnus’ story starts with his parents supporting him to pursue data science. Read on to know more about his data science journey with Great Learning.

Why did you choose the PGP-DSE program by Great Learning?

I did my engineering in physics and nanotechnology. In my final year project, I had to work on data. It took me a lot of time to get the data, clean it, and work on it. At that time I had no clue about data engineering, but my seniors told me that there is something called data science and asked me to look into it. That is how I came to know about data science and I developed an interest in it. I was planning for higher studies but I felt that there are unexplored opportunities in the field of data science, so I took up the PGP-DSE program.

When I told my family that I wanted to take up this program, they had no idea about it. They knew about the IT courses like Java, so I explained to them the field and opportunities in it and they supported me after that to pursue the course.

Did you consider any other program before enrolling?

I had considered Imarticus but I felt that the program lacked credibility in what they were promising and it is more expensive as compared to Great Learning’s program.

Did the program curriculum prepare you for interviews?

I feel that the program had covered almost all the topics that were asked during the interviews. I knew about the things that were asked. I worked on a lot of different projects during the program. The mini project which I had worked on helped me a lot in the Mahindra and Mahindra Hackathon.

What did you like the most in the program?

The peer group is really good, we used to sit and discuss things. As we had people from different backgrounds, it helped us to exchange what we knew and learn together.

Faculty were too good and they made it very simple for us to understand the concepts.

Did the placement support help you prepare for the interviews?

We had many companies coming in and we were groomed well before the placement process began. The mock interviews and CV reviews gave me an idea of what I was good at.

How was the Interview process?

There was a hackathon in Mahindra and Mahindra, which I had won. After that, there was a code review followed by multiple telephonic interviews. The course made it easy for us to explain the concepts and algorithms better. That is what impressed the interviewers and they offered the role. I have already started working on some interesting projects here.

The program didn’t require me to leave my job: Divya Tandon, PGP-BA Alum

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Divya Tandon, PGP – Business Analytics alum gives us a quick look at her Great Learning experience, in her own words.

I had worked in a market research company for 1.5 years as a research analyst before joining Great Lakes PGP-BA Program. My decision for choosing Great Lakes was influenced by 2 important reasons:

1.The Institute was ranked amongst the top programs for business analytics

2.It did not require me to leave my current job.

The professors at Great Learning are extremely helpful and provided regular guidance when required. Even during the off-residency time, we could communicate with them about any doubts through the knowledge portal. The assignments were also designed based on real problems which further helped us strengthen our foundation in analytics.

This program helped me add the needed skills and tools in my resume for transitioning to business analytics and earned me interview calls from various companies. Also, my enhanced/improved knowledge and skills after completing the course helped me clear the interview at my current company (Nepa AB) and allowed me to shift to core analytics. I would definitely recommend this course to any person who is looking to make a shift to analytics without leaving their current job.


Weekly Data Science Round-up: March 27th, 2019

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5 Ways to Check if Data Science is the Right Career Option for you: Do you love problem-solving? Do numbers excite you? Do you possess the patience of a saint? And lastly, do you love data?

Can Outsourcing Reduce the Jobs Shortage in Data Science?  Outsourcing data can help companies save 50% of their budget as the company do not have to bear office cost, equipment and their maintenance costs.

Why Python is More Relevant in 2019 Than Ever Before: Owing to its versatility, simplicity, flexibility, and a plethora of available libraries Python has the largest community of programmers.

Watch this IPL with a dash of Data Science: ESPNcricinfo will use data science, big data, and cricket intelligence, to produce a set of numbers that will help fans understand and appreciate the game better.

5 Crucial Data Science Skills to Learn in 2019:  Machine learning, statistics, quantitative analysis, mathematics, and programming languages are broad areas in which you’ll need to build expertise. If we were to delve a little deeper in what exactly these skills entail, you’ll come across these tools.

Weekly Artificial Intelligence Roundup: March 27th, 2019

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The ethics of artificial intelligence: Teaching computers to be good people: As algorithms become more ‘intelligent’, researchers are working on new ways to regulate the extent to which AI is able to grasp and mimic human intelligence.

Spot and fix supply chain problems with artificial intelligence: Due to the colossal volume of data at the disposal of organisations, deriving insights from data can reduce the error percentage as close to zero as possible.

How Will AI Create More Jobs by 2025?Reports suggest about 9% of the workforce will be employed for roles which do not exist today. These new jobs will be powered by artificial intelligence and machine learning.

AI in Healthcare to Become a Billion Dollar IndustryThe integration of AI in healthcare will automate many processes. Approximately 21% of healthcare facilities have a specific purchase plan of AI tools that is predicted to be executed in the upcoming years in Europe alone.

How Amazon is Dazzling the World with Artificial Intelligence?: Amazon has been investing in AI for more than 2 decades now to predict and mould customer experience. The current state of AI and machine learning promise an exciting future and Amazon is undoubtedly at the forefront.

It would have been impossible to transition to Analytics without guidance and help from Great Learning: Ashwaraj, PGP-BABI Alumnus

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Analytics provides opportunities for people from a diverse set of professional backgrounds, and Ashwaraj is a shining example. As a mechanical engineer who was working in the Automotive and Education industries, he decided to make his mark with an Analytics career. Here’s how PGP-BABI helped him in his journey. 

Why did you choose the PGP-BABI program?

I come from a mechanical engineering background and after graduation, I worked in the automotive industry for a while. After that, I worked in the education industry and then decided to shift into analytics. I was looking for programs that were available in the market and based on my peers and friends suggestions, I realised that the PGP-BABI program by Great Learning was one of the best programs that I could have gone for.

Why data science?

This happened when I was in the education industry. I used to manage a territory where I had to develop good business outcomes. Basically, the target was to get more business in less time. And practically, I couldn’t find any feasible solution in front of me. The only thing that I had access to was loads and loads of data. When I started crunching the data using Excel, I was unable to get faster outcomes and I realised that maybe I would need other tools that could help me to turn things around faster. And moreover, I also lacked the required knowledge. As I started digging deeper, I realised that I should go for formal training that could help me in turning around the data into goldmines.

What did you like the most about the program?

PGP-BABI program is a perfect blend of both knowledge and skills. Every concept was followed by a practical assignment, case study, or a quiz. The program enabled me to think beyond the simple learning tools of the trade and inculcated the ability to think from a business perspective. Being taught and mentored by some of the best individuals in the industry and academics ensured that my knowledge and skill grew simultaneously. I can humbly say that without the guidance and help from Great Learning, my transition from Education to Analytics would have been impossible.

How did you land your current job?

When I was joining this course, I found a relevant contact in the automotive industry. I also spoke to a few professors who came to Hyderabad. There was a marketing professor, he mentioned that Ford was looking for analysts and I should try for it. When I was about to complete my course, one of the alumni with whom I had worked in Mahindra offered me a job in Ford and the rest, as they say, is history.

How did you manage to learn with a full-time job?

It was a little challenging in the beginning because my job required me to be on the field most of the time. But after proper planning and talking to my company stakeholders, they were cooperative as I was able to show immediate benefits that I was getting out of learning. I also became more organised as time passed. In a way, I was able to learn time management as well with Great Learning.

What advice would you give to the aspirants?

I would say, one should keep three things in mind. First, take the course very seriously because the more serious you are towards the program, the better are the outcomes.

Second thing is to have an open mind when classes are happening, even online classes. Just go through the recordings again and get an understanding of the content.

The third and the most important thing is that when you are given assignments and quizzes, do them deliberately and only for yourself, not for others. These three things will definitely help in churning out the best outcomes from the program.

Weekly Data Science Round-up: March 22, 2019

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Top Data Science use cases in Gaming: Data science is making its benefits apparent in game design, monetisation, visual effect and much more.

The difference between Data Science and Machine Learning: Here’s how you spot the biggest differences between machine learning and data science.

How to negotiate your data scientist salary: Building the right skillset, having the right tone and demeanour can go a long way in getting the salary that you want.

Data Science generalists vs. specialists: A “T-shaped” data science professionals with broad general knowledge and specialisation in certain skills is the most valuable.

5 Qualities to Look for When Hiring a Data Scientist: Companies look for candidates with statistical thinking chops and data intuition when hiring, among these other skills.


Weekly Artificial Intelligence Round-up: March 22nd, 2019

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Here’s our weekly round-up of the most essential reads on Artificial Intelligence from across the Internet:

Why Every Company Needs An Artificial Intelligence (AI) Strategy For 2019: Companies need to define the specific use cases for AI to leverage this technology to meet their business goals.

Preparing The Human Workforce For The Machine Workforce: The nature of the workforce is changing, and working professionals need to educate themselves to remain relevant in the future.

China: Tech Cold War With U.S. Continues: China is signing laws into place that limit the influence and dependence on American technology.

World’s biggest AI startup exploring self-driving cars: SenseTime is actively seeking out investments in fellow startups that can benefit from its own technology.

How will India contribute to AI innovation in the world?: India is in a unique position; will we leverage it to innovate in AI?

An algorithm that can recognise handwriting: How to build a framework for an AI model that is able to recognise handwritten text.

Data Science Round-up: March 20th, 2019

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Here’s our round-up of the most essential reads on Data Science from across the Internet:

Data Science is now bigger than Big Data: Data Science is now commanding the hype that Big data used to have, as organisations are looking for new ways to leverage a large amount of data they have to produce insights.

Why Data Science Teams Need Generalists: Data Science generalists have improved autonomy and are empowered to make better business decisions because they have better knowledge of end-to-end business capabilities.

Europe is better for Data Science compared to the US: Europe’s heavy investments in education and infrastructure and education has paid dividends in better skills.

How Data Science is powering Nvidia’s business: Data Science has become the new driver across the computing landscape, encompassing analytics, AI/machine learning, and inferencing

Secrets of a successful Data Scientist in 2018: A report on the most important skills that successful data scientists have and other key information.

9 Applications of Data Science in E-commerce: Interesting applications of Data Science in e-commerce that are driving results.


Artificial Intelligence Round-up: March 20, 2019

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Here’s our round-up of the most essential reads on Artificial Intelligence from across the Internet:

How AI is changing science: Automation algorithms are assuming the role of a scientist and are crunching numbers at a cosmic scale, but they still lack the spirit of scientific enquiry. We still have some way to go before that can discover physics or mathematics that the brightest humans alive are not able to do on their own.

A Robotic Leg Learns to Walk Without Prior Knowledge: Researchers have developed a bio-inspired algorithm that can learn a new walking task by itself after only 5 minutes of unstructured play, and then adapt to other tasks without any additional programming.

AI generated poetry: A retrained version of an older algorithm has finally learned how to make rhyme and meter work to generate poetry based on old classics. The results are middling, but its a promising step forward.

DeepMind and Google; Controlling AI: Google acquired DeepMind for $600 million, and is now looking to monetise the company. But the company’s founder is resisting attempts with a bid to focus on AI research and producing path-breaking technologies.

5 most promising roles for Artificial Intelligence in India: Roles such as AI architect, AI product manager and more are defining the growth of AI, and offering professionals a fulfilling and rewarding career.

Everything you need to run Deep Learning experiments: Learning by doing is the best way to understand Deep Learning better, and you need some serious computing firepower to run experiments. Here is a list of all the components you’ll need to build your rig.