February 17, 2022

Learner Spotlight – Ashray Adappa

Ashray started his professional journey in chemical engineering, but when he felt like he had lost his way, he discovered data science, taught himself on the Dataquest platform, and landed a job as a data analysis consultant. This is the story of how he decided to change his life, step by step.

1. First, what are your preferred pronouns?


2. Alright Ashray! Could you tell us where you live?

I live in the quaint and sunny state of Goa in India, in its capital city of Panaji.

3. Tell us a little about your background. Where do you come from? What and where did you study?

Growing up, I was encouraged by my grandfather to read newspapers, short books, and articles. Reading helped me to build my communication skills in English, which as we know, is instrumental to success in data analytics. It also began my interest in quizzing and trivia. My mother (who was the first postgraduate in her family) helped me to understand the basic math that was being taught to me in primary school. This started the virtuous cycle wherein I tasted success in math and then began studying math on my own and scoring well in my classes in school. I also had some great teachers in public school (shout-out to Mushtifund High!). Thus, close mentorships and positive influences can help one to gain confidence in their quantitative abilities.

My interest in science and technology also came from reading science articles from borrowed encyclopedias and Tinkle magazines, and watching Beakman’s World on the television. As a child, I wanted to become a biomedical engineer (partly because I enjoyed science and partly because biomedical engineering was a combination of doctor and engineer). I resolved to study hard for the entrance examinations to gain admission to the best colleges in India. Preparing for the engineering entrance examinations was a transformative experience, because only then could I truly comprehend the all-encompassing nature of math and science. So many phenomena could be explained by the scientific method. I also credit my engineering entrance exam coaching classes (Aryaan Coaching Classes) with providing me the clarity of conceptual learning (where previously I relied on rote-based learning). I did perform fairly well in the entrance exams but I could only do well enough to gain admission to a chemical engineering course.

Once in engineering college, I lost my way in terms of internal motivation (was I doing this for myself? or was I doing what society seemingly wanted from me?), and I became especially disillusioned with the math being taught in engineering. It seemed to be very narrow and obscure. At this time, I was also gaining interest in more human affairs, like the worlds of economics, business, social sciences, and humanities. Still, I really wanted to be able to apply math to real-world phenomena. There was one part of applied math that could be used for that, namely Applied Statistics and Probability (even though this was one of the weakest points in math). As I read more about how to apply statistics, I came across an emerging field — data science. It fits my desires and the world’s needs perfectly.

In college, I took a few courses related to data analytics, but for my own reasons, I did not apply the quantitative rigor that was sorely needed. After graduating from college, I decided to try to learn the basics of analytics from online sources. And that is where I found Dataquest.io, which helped me a lot with its structure and lucid exposition of statistical, calculus, and algebraic concepts, Python programming and introductory software engineering. After completing the Data Analyst and Data Scientist paths, I had a lot more confidence in applying for data analysis roles and was eventually able to join Fractal Analytics as a Data Analysis Consultant, where I continue to work to this day.

4. Can you share more about your position at Fractal Analysis?

I work as a Data Analysis Consultant (similar to a senior data analyst), in Fractal Analytics. I help businesses use their data to study their market performance. It mostly involves data cleaning, data harmonization, and data visualization with some machine learning as well.

5. Can you describe the hiring process for this position?

After finishing college, I came back home and studied analytics through Dataquest.io. It took me around three months to complete both the Data Analyst in Python and Data Scientist in Python paths. Gaining the completion badges were very fruitful for me because I could attach them to my LinkedIn profile, which added much needed legitimacy. Also, the guidance that I received in the Dataquest courses to set up my portfolio on GitHub was extremely helpful to showcase my motivations to the wider public.

I applied for my current job through a LinkedIn posting, and I had the opportunity to interview for it in two weeks. The interview consisted of four rounds. The first round was an online technical assessment of Python and SQL. I had to write code to answer five questions in Python and five questions in SQL, within an hour. The Python questions were of a high difficulty, and the SQL questions were of medium difficulty. I remember that I could not pass all use cases for three of the Python questions, but that I could answer all of the SQL questions properly. The coding environment at Dataquest did help prepare me to type out code into the web-based editor in the technical assessment.

I was through to the second round, which was a telephone interview with a data analytics manager at Fractal. She tested me on my quantitative aptitude by asking guesstimate questions. She also asked me how I could improve sales of a brick-and-mortar toy store (i.e., what were the kind of analytical techniques that I could apply, to help the store improve their sales?). Then, she asked a couple of questions related to SQL (for example, “How can you display a subset of a text record in SQL and how can you output the second-highest quantity in a certain column, using a SQL query?”). I was able to answer these questions well enough to proceed to the third round, which was a video call interview with a data scientist at Fractal.

Here, he asked me about my portfolio projects, about how and why I prepared the data the way that I did (normalization, imputation, dropping very empty rows or columns, etc.), and what the accuracy scores I used really meant. Then he asked me a case-study question that involved deciding which ML algorithm would best apply to deciding to place perfume fragrance salespeople in a set of shopping malls across the country, given their sales data (for malls with and without those salespeople) and geographical data. I cannot recall the specific details of how I answered that question, other than stating that this would be a clear classification exercise and recommending a K-nearest neighbors algorithm. It was not the best answer, as he was expecting me to mention the SVM algorithm instead, but I think that he credited me for my thought process and determined me fit to move on to the next round.

The last round was a straightforward personal interview with a Human Capital manager from Fractal, and she asked me questions related to my motivation in data analytics and asked me to describe my journey. One week later, I received an email stating that I had passed the interview, and so I started my first job in data analysis at Fractal Analytics, Mumbai.

6. What excites you about your job?

I am excited to find patterns that no one else may have noticed before — be it in how the data is being taken in, or what conclusions are drawn from a summary analysis. I also love working with my teammates and brainstorming with them.

7. Is there something about this job that you’re very good at?

Contrary to the popular tech stack of the Anaconda distribution that analysts use for personal projects, there are many data-related applications produced by companies for companies, which I am good at picking up on-the-fly. Often, I find that I can help my colleagues learn them.

8. How did you feel before starting your Dataquest journey?

There were a mix of emotions before beginning my Dataquest journey. Mostly, I was excited because I was enamored with the split-screen interface of lesson text and the coding window, which I thought was pretty neat. I was not that anxious as I would have been before any other data analytics course because a) I had a chance to look at the topics of each lesson and felt that the gradation in difficulty was well paced and b) I did have some prior experience in coding using Python and C languages, back in college.

9. What do you like about the Dataquest learning platform?

I was immensely impressed by how well the math was taught at Dataquest. Only the most important concepts were taught; they were broken down so well that I think anyone who has basic arithmetic skills could start learning. Also, at the time, the in-browser coding was state-of-the-art. It helped me to then move on to code editors with much more confidence.

10. How was your journey with us? 

My journey with Dataquest started off very smoothly. Due to my previous technical experience in college, I was able to complete the Analyst path with ease. Once I progressed halfway through the Data Scientist path, is when things got (predictably) tricky.

11. What new skills did you learn with us?

I learned how to find data sources, clean and prepare data, decide the data-manipulation strategy best suited to the ML algorithm being applied, visualize data to find correlates and other relationships and then to use the sklearn library to apply machine learning. I also learned how to communicate my findings in an accessible manner. Once I mastered these skills with Dataquest, I really felt like I was a true data analyst — like I wasn’t an outsider anymore.

12. What was your learning process?

It took me . . . I think about three months to complete the Data Analyst and Data Scientist paths. I would log in every day in the morning, just like attending school, and I would log off in the evening, around 5 pm. I would take notes by writing them down, so as to really ingrain the lessons into my mind.

13. Do you have any advice for someone considering Dataquest?

Take copious notes, and go over them periodically. Practice coding as often as you can on your local machine. Try to understand every line of code in the lessons. And be patient.

14. Tell us what you are most proud of after learning with Dataquest?

I’m proud of the personal projects that I completed with Dataquest. I’m proud of the real-world skills that I could apply to start a data analysis project or to apply to my work in the corporate analytics space.

15. Finally, where can people find you on the Internet?

My LinkedIn – https://www.linkedin.com/in/ashray-adappa/My Github – https://github.com/ash-adappa



About the author


Dataquest teaches through challenging exercises and projects instead of video lectures. It's the most effective way to learn the skills you need to build your data career.

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