Click here to subscribe

At INSAID, we create accomplished and empowered Data Leaders. We groom our students to dominate the world of Data Science and Artificial Intelligence and reshape their future. We value what our students bring to the table. We share their vision and support them during their journey and ensure that they carve a niche for themselves.

We’re proud to have tutored exceptional students all across India. Today, one such exceptional student, Madhavi Rao stands in the spotlight.

Student Name: Madhavi Rao 
Batch: GCD – October 2018
Total years of experience: 4 years 
Area of expertise: Technical Consultancy

Malvika: Can you start by telling me everything about your professional journey so far? 

Madhavi: I’ve been working at Oracle as a Technical Consultant; specifically as part of Oracle ERP and fusion applications. Basically, I work on conversions and report building. Those are the main areas of focus at my workplace; if I have to explain conversions, consider that business users store data in some legacy systems. They are all planning to move to Oracle EDS. We convert all that data I previously mentioned.

I have some experience in Oracle Fusion reporting as well, that uses BI Publisher. In that way, I have a good experience in developing reports from data through EDS

Also, recently, I started cultivating an interest in learning data science which is why I joined this course at INSAID. This course actually helped me know all the basic steps required for analysis using new libraries, like all the Python libraries, and learning new algorithms, where we can do some predictive modeling.

Malvika: That’s good to know! So did your current role get you interested in data science and was it something else?

Madhavi: Actually, it’s not my current role but Data Science itself that got me interested in this field. It’s a really essential skill that can be used across all types of platforms and all kinds of applications. I’d like you to just think of an idea and Data Science can be used there as well. For example, basic things that we use daily, like the cabs that we book every day, use data science.

So how do we book these cabs? How are we getting recommendations from the e-commerce sites? All these day-to-day activities that we do buying products on online platforms like Amazon, Flipkart, booking cabs even in the banking sector, so the radius of applications where data science and machine learning are used got me interested to learn the skill, so that we can also apply it in our daily lives once we actually master it.

Malvika: Are there any current applications that draw your attention in the data science field?

Madhavi: You know there is so much news generated every day. A lot of research happening in this area is very stimulating. Driver-less cars got my attention. It is a very interesting concept that you actually train your car to drive on its own; this is built around reinforcement learning.

Also, we can develop cleaning robots such that our household work can be done by them. Initially, you might need to record data to train them- how are our houses built? What are the dimensions and walk through the entire place to learn to do its job. 

These are certain applications where I find that there is a lot of scope for data science.

Malvika: Can you tell me what you think is the goal of data science? Or how have you observed data science evolving over the years?

Madhavi: I think the main goal of Data Science is to analyze our data and make data-driven decisions. In our decision making, we should use a data-driven approach, so that we can be better at alleviating problems, and in our quality of decisions.

For example, let’s consider some startup food delivery business which has started when there is already a lot of competition in the market. First of all, collecting data will be their major focus that is data extraction; data can be from many sources. You need to collate what are all the other options available for delivery, what are all the restaurants nearby, what offers do they have, how are they located in a particular area and so on. 

We need to collect data and then perform some analysis of this data and then we try to make decisions as to how to improve our products. So that way, the main goal of data science is data-driven decision making.

Malvika: Are there any blogs or websites that you follow? 

Madhavi: I follow all blogs on Medium. AnalyticsVidhya is one blog that I follow religiously. Also, KDNuggets is very much informative discussing all machine learning algorithms in an easy way.

Malvika: Do you want to talk about some initial challenges that you faced when you were starting in this space?

Madhavi: I won’t classify it as much of a challenge given my previous exposure to it but learning Python was a little hard because it was a new language for me. 

The biggest challenge was that whenever you’re learning a new algorithm, there is a lot of math behind that! Whenever I go deep into the math, I get stuck. I just take the end-use of the algorithm, and then sometimes ignore the math behind it for a bit.

Malvika: Since you spoke about data visualization, are there any data visualization tools, or ML algorithms that you prefer to use?

Madhavi: We have endless Python libraries that we can use. Pandas, Seaborn and Matplolib come to mind immediately. You can use it for bivariate and multivariate data. I also have hands on practice using Tableau. It is a data visualization tool that does not require any coding. You can use it for dynamic visualization for dynamic data as well. 

One really interesting machine learning algorithm is Support Vector Machines. It uses a unique concept of deciding hypothesis between clusters of data. With two edges of the data, we choose how optimally we can choose a margin between two different sets of data and call them as our support vectors. Then optimize our hypothesis for differentiating. 

For certain smaller data-sets, we can use this algorithm more efficiently. I have personally discovered in one of the letter recognition data-set given to me as a project.

Malvika: At INSAID, students are encouraged to build high quality GitHub profiles. How do you think your GitHub portfolio has helped you?

Madhavi: It helped me a lot. I didn’t have a GitHub profile earlier. In all my projects, even office projects we used other repositories. It is open source so it is great for showcasing the work done by you. 

Also, you can give a gist of what projects you have done and then we can also upload various files, like our actual notebooks or any presentations that we have created. So it’s a very efficient way of showcasing our work.

INSAID has taught us to use GitHub in an easy way, because actually GitHub also includes some kind of coding, which I am not sure of. Even for non-coders, GitHub can be used in an efficient way.

Malvika: Crafting a great Data Science resume is critical for getting shortlisted for these roles. Can you tell me some ways in which you have improved your resume as part of the INSAID data science career Launchpad?

Madhavi: Data Science resumes are a little different than the usual resumes. I had to find ways where I can mold my actual work into that of a Data Science field. 

I have a sound knowledge about SQL and that can always be helpful in working on a database. I have worked on Oracle BI reporting as well. These tools helped me post analysis also, presenting the analysis in the best form of visualization, in the form of reports or dashboards. So even Tableau can be used for creating dashboards. 

So this is one way I found that I can actually transition into this career path. These things in my resume show that I’m not a complete newbie to this world, but I do have a little bit of basic knowledge in SQL and reporting.

Malvika: INSAID’s mission is to groom Data Leaders of tomorrow. What do you understand by a Data Leader and how do you think a Data Leader is different from a Data Scientist?

Madhavi: Basically, what a Data Scientist does is, he gathers the data; that is called a data extraction and then he performs some analysis on that, tries to build some visualization or analysis, which can provide some useful insights. Maybe there is some trend in the data or some useful things which we read we can’t find out definitely just by looking at the data.

He also uses some algorithms to do some predictive modeling in case it is required. That leads to the deployment of those models in the actual production environment. 

A Data Leader is the one who pursues the organization to collect certain kinds of data as well. Data analysis on the available data is one thing, but actually collecting data in various forms and even establishing that any data is important, there is no junk data.

Once we accumulate everything, only then we can find out what is not important for our current business needs, but that can be important at any other time. So she must be able to guide towards getting the right questions. Later you mold that into useful decisions. Along with all the responsibilities of a data scientist has, a data leader should have this one most important trait that is to think about the business and define the right questions.

Malvika: What would be your advice to someone who is starting a career in Data Science?

Madhavi: My advice would be to stay perennially intrigued by the field. You need to educate yourself about the various applications of the field. That is what keeps your interest going in the field. 

Once we feed that interest, it is all about how we learn to implement algorithms and present our analysis. Knowing how to clean data is an art in itself. Developing interest in how to explore data and clean data is very important as that can also be one very rigorous task, which takes a lot of time for any person.  

Extracting the right information is also hard. Data science is not an easy field. It takes a lot of time and effort. So we need to be patient. It’s not formula based; it’s not like when this happens, this happens or something like that. 

There can always be ways to bypass certain methods, right? Choosing the best methodology takes time and experience. So this journey is a little slow one, but a very useful one to the world. 

Malvika: How has your experience with INSAID been so far? 

Madhavi: It’s been an excellent journey for me because for someone like me who is busy with her work outside, I need someone to guide me and show me the right approach to follow. To answer questions like what has to be done? The entire teaching, mentioning the prerequisites to the actual matter and everything else has been done in a very streamlined manner. 

I’m very lucky that I found INSAID very early in my career and I’m able to utilize it to make the right career moves. 

Malvika: We’re very gracious for that feedback. It’s been a great conversation. Thank you for your time!

Author

Content Writer @ INSAID. A machine learning buff who loves to read, write and explain everything AI!

Write A Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.