Data science is such a huge field that whatever you do to excel in it, might seem to be insufficient.
- In an attempt to share the sure-shot success formula for Data Science, the Chief Data Science Mentor at Accredian, Manvender Singh, or Manav as he is affectionately known, shares the evolution and trends of the field, insights about the various Data Science programs at Accredian and the recipe to succeed in the field in 2019.
Ankita: Manav, how has Data Science evolved over the last few years? How was it perceived and what has it changed?
Manav: I’ve been in the Data Science industry for a little more than half a decade and have seen this industry grow rapidly.
There are three key things that I’ve seen happening over the last couple of years until 2019, which were noteworthy.
The first one is the boom in data science and both companies and professionals realizing that this is something that can give them massive ROI; ROI for companies is in terms of making revenues and solving the tangled business problems.
From a professional point of view, it is about a faster career growth, more salaries and essentially being in the field which is very exciting. So, this is one thing that I’ve seen happening. I have experienced that there’s immense growth in the recognition of Data Science around industries.
The second thing that I witnessed over the last couple of years is a slight change in the data science tools. For example, until five years ago, R and Python were almost neck to neck in this industry. But post that, Python has overtaken R by quite a huge margin.
If you ask any Data Scientist in any industry, most of them are using Python.
Think R is obsolete?
No!! It’s still in use; its extent has gone down or stagnated. This is an instance to show how tools have changed.
The third trend is that the resources for learning Data Science have increased considerably. While there were a very limited number of resources earlier, now you have a very good number of resources to learn from; people you can follow and institutions that you can learn with.
These are essentially the changes that I have seen over the last 5-6 years I’ve been in this industry.
Ankita: Now that you have talked about the tools and other things which changed in this data science field over the past few years, would you like to talk about the current trends that will become big in the future?
Manav: There are a lot of interesting things happening but I am particularly interested in two trends.
One is what I call the Democratization of Data Science.
Until 10 years ago, only very few people could do Data Science, because it was complex. Today, more people can do Data Science because we have several collaborators to our aid- Python and phenomenal ecosystem, libraries and packages.
Several companies like Microsoft have made Data Science smooth sailing and approachable for all. Do you know the Azure platform?
With this cloud computing tool, Data Science operations can be performed through a drag and drop interface; even if you’re not a techie, you can do data science.
The future of Data Science is when it is within the reach of the non-IT professionals too. This trend is visible in the popularity of tools like Azure for non-programmers. These tools are making Data Science more intuitive and less programming heavy.
The second trend that I’m particularly excited about is what I call the automation of Data Science.
In the current scenario, Data Scientists spend approximately 60% of their time in data cleaning, gathering and other phases of data preparation in the critical phases of a Data Science project. Ideally, this is not the best use of a Data Scientist’s time.
With automated machine learning tools/ platforms like H2O, a lot of these steps like data cleaning, transformation and manipulation etc. are getting automated so that the focus is more on the machine learning, analysis and insights, instead of the cleaning tasks.
Another example of automated machine learning is the choice of algorithms getting automated. In the present time, a good data scientist is someone who effortlessly decides what kind of machine learning algorithm to use. But in the future, a great data scientist is going to be someone who can efficiently solve a problem.
Figuring out the right algorithm will be the task of automated machine learning tools. They will do it for the Data Scientists.
Automated machine learning is one big trend that greatly interests me.
Ankita: AI is also creating quite a buzz and is rapidly gaining popularity, just like data science. Every time you pick up a newspaper or are browsing the web, you come across so many AI applications all over the industry, irrespective of the field or sector. What according to you are some of the industry-specific applications of AI that are useful to mankind?
Manav: That’s an interesting question because the whole goal of Data Science and AI is to be able to drive business value.
If I start speaking about the applications of AI and Data Science in different industries, it will become an hour-long session. But we need to look at AI, first of all, from a technology standpoint; what does AI mean?
It might be in the form of computer vision and NLP et al. Let’s take banking and BFSI as examples, wherein a lot of top companies operate, and refer to a problem like fraud detection.
Earlier fraud detection was being solved through machine learning. Now, AI is being deployed to solve the same problem.
The specific areas of AI and Data Science are being used in different industries to solve different problems.
Let’s take another example of manufacturing, where processes are turning towards predictive maintenance.
Now what happens is that even before a machine breaks down, you get to know which machine is going to break down. Do you know how does AI take it even further? Predictive maintenance is the answer; Data Science and machine learning can also be used to achieve this but what makes the use of AI even more interesting is integrating speech and vision to predictive maintenance.
This is like your system is telling you please change this machine immediately!
These are some of the industry applications of Data Science and AI combined, driving a change across industries.
Ankita: Moving on to the programs offered at Accredian, the students enrolling in them must be from diverse backgrounds. Would you like to share something about the Accredian students; their field, years of experience they have or any other peculiar thing about them?
Manav: Together with teaching Data Science at Accredian, we also do a lot of analysis about the kinds of students we get at Accredian.
70%-80% of our students come from the IT industry. These are professionals working in top IT companies like TCS, IBM, Accenture, Wipro, and Infosys; all the companies that you can think of.
Then we have around 20% of the students, working in the IT departments of multinational banks, like American Express, Citibank, JPMorgan, etc. and rest 10% are from other industries.
The common thread between all these professionals is that they are either working in some or the other domain, like testing, ETL, Java or any other technology, or working as a project manager and want to upscale and upgrade their skill set, in sync with the market demand and where they perceive the future and opportunity to be.
Why do you think there is a wave of change among the professionals?
I will tell you why.
You must have heard the figures that the Analytics, Data Science and AI industries in India will multiply many fold over the next couple of years. This is in stark contrast to the regular legacy technologies on which a lot of professionals work, wherein the growth is almost stagnant.
This is why these professionals are looking to move to next-generation technologies for a better career, a better future and more exciting job opportunities.
This is exactly the kind of profiles we get right now. Most of our students are from India. The average work experience of our students ranges anywhere from 2 to 30 years.
Ankita: Right. So now that we have discussed about the target group of Accredian, would you like to talk about the Accredian programs? How are they top-notch? How do you ensure that anything and everything that goes into it is student-centered? How do you differentiate it from the prevalent ones in the industry?
Manav: This is an important question. I’ll detail on four specific factors that make Accredian programs unique, top-notch and student-centered.
The first factor is its biggest strength too- Accredian is among the very few institutions in India that deal in Data Science and AI.
Even our mission- to groom Data Leaders of tomorrow is unique. This is why our approach towards achieving it is also very different.
The second differentiating factor is our sole focus on Data Science and AI. Team Accredian- our researchers, faculty and everybody else is focused on Data Science and AI only. It is this focus that is missing in other institutions, wherein they are teaching horde of courses without any focus and aim.
The third reason why our programs are top-notch is that they are practical. Our students should feel confident after attending one month of classes that they are ready to apply their learning.
What should not happen is that you have learned a bunch of theories and packages but you don’t know how that library or package is used; then your learning is of no use. This is exactly what we ensure- all the programs are as practical as possible.
Assignments, quizzes, term projects and Capstone projects are the mediums to ensure we keep the spotlight on the functional aspect of the program. The crux of it is that everything is focused on applied Data Science.
The fourth way in which we ensure that our programs are top-notch is roping in some great brains as our faculty. I’ve done my MBA from ISB, which is one of the world’s premier business schools, and one of the distinctive features of any Top B-school is that you have really great professors.
This is the same model that we have taken at Accredian; renowned experts teach the students.
These experts are the ones who can articulate ideas well, easily comprehend the target students and know how to simplify a concept in a way that the student feels motivated and excited to dig his or her feet deeper into this field.
We keep our programs simple and refrain from any tall talks, focusing completely on our students.
And the stimulating conversation continues with the Data Science Expert…. A few engaging questions still remain which Manav answered in an unprecedented way, just for Data Science enthusiasts like you. Do read the Part 2 of this conversation and you’re sure to walk away with some great information.
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