My Career transition from Teacher to Data Scientist!

Induraj
Induraj
Sep 6, 2020 · 6 min read

It is one of the difficult decisions to quit a well-paying career and follow an interesting passion. We as humans are bound to various things that might make us not switch careers and feel secured in the path we currently are. But to be a dream follower, one needs to come out of his safe zone and explore. So my journey to pursue this interest and passion for data science started in the end of 2016. Until then I never heard of this data science field.

An artist must not train only his eyes but also his soul

The above quote fits for any aspiring data scientist as well. My journey towards data science started because of unanimous Blogspot. Yes, I was very much fascinated by some unanimous blogpost posted a few days before the U.S election results were announced in 2016. The blog post claimed that Mr.Donald Trump will win the election. The post claimed to have predicted it using twitter sentiment analysis. I didn’t really pay attention to this post, Initially thought it to be some random post we encounter in everyday life, but once Trump’s victory was confirmed, I searched for this same blog post to read and understand the twitter sentiment analysis. (It seemed easy to read, but it was not while I tried twitter sentiment analysis practically in 2016). I was as a person without any accessories just standing below the Himalayas with the desire to touch its peak with no accessories. I googled various articles to know about data science, Found that “R” language is used, so I started learning R and gave a try to perform such analysis. Was it easy? Not at all. Can one become proficient in martial arts in a couple of days? No, right? Without even a little domain knowledge, it was way more complex to do a data science project with enthusiasm alone. However, due to curiosity, I started exploring theoretical concepts and consistently was learning R.

Realizing that skills in R and python don’t mean one is a data scientist!

To convert logic in mind to working programs in the system, these languages help, But what about data science concepts such as what algorithms to apply for which situation, How to deal with time-series data? what to do if some stochasticity is involved in the problem, what are Statistical procedures used to test the significance of the hypothesis on population? How to perform dynamic price optimization?

so from where to start learning the concepts of data science? from statistics lessons or from basics of machine-learning was the biggest puzzle. I figured that learning Python and R alone was not sufficient and was not helping to know the concepts of data science. Still, something was missing, I was not satisfied, online courses were brief and not detailed. So I decided to pursue a full-time master's in data science that gives more weightage to mathematics as well as machine learning. To learn data science from very basics to the core both time and effort were needed. So I decided to quit my job and search for a college that fulfills my thirst for knowledge on data science.

One needs to devote himself to learn to the field of data science to know these concepts. After becoming familiar with these data science concepts and programming, getting benefits from data becomes familiar. To start learning the basics, online courses on machine learning and data science are sufficient, but one will be missing the greater part which is statistics, mathematics, and data visualizations.

Are online classes enough?

Believe me, you don’t want to end up posting like this on Facebook pages. The field of data science is vast comprising skillsets from statistics, mathematics, machine learning, databases, deep learning, and more... I am grateful that I made a choice of pursuing masters that give skills on maths, stats, machine learning, and many more…. What is taught online is very less, In industries, not just python and R are used in every scenario’s, Pyspark, Hadoop, and many cloud-based services and different types of database systems are used. The field of data science is growing with new tools emerging now and then. To land a career in the Tech industry one needs to know notions of all these trending concepts.

Picking a curriculum to study?

Ya, I didn’t pick the college based on its reputation alone but also based on the curriculum taught. For 2 years of education you don’t want to sit at home free at least three days a week, do you?

The way pot is shaped depends on the potter

I picked a curriculum that will be my potter to shape me into a usable craft. I wanted the curriculum that gives a strong foundation in Big data, SAS, advanced visualization, relational database, OLAP cube, advanced database, non-relational databases, optimization (deterministic, stochastic), Monte Carlo techniques, heuristics, metaheuristics, constraint programming, Machine learning, Time series analysis and forecasting, Deep learning and Text mining. Been as a teacher, I wanted to know about many subjects in this field so I decided to join a curriculum that teaches all these concepts that are very important in the life of data scientists. So what's your motivation? what do you want to learn? if your focus is only to become a machine learning engineer or a data engineer or data scientist or business intelligence or data analyst, pick a curriculum and college very wisely.

Was I foolish to quit my job paying $2.2k per month?

Having good pay and still quitting the job is a complex decision. However, the desire and enthusiasm to learn data science were preceding the comforts I got from my salary. So I prepared financially as well as mentally to dedicate 2 years in France learning data science. Don’t enter into this field just with the mentality that machine learning and data science are the coolest trending topics and that you can earn more as soon as you complete the education. Develop a passion towards data, There is not just one approach to deal with data, a multitude of ways prevails. So unless one develops a passion it might be difficult to see through data and approach in a multitude of ways. There is always new to learn in this field. So sharpen your mind for different techniques, tools, and technologies.

So how is my life as a data scientist?

The ability to be work autonomously and as part of a team comes readily, To deal with different peoples of various age groups, knowledge on research methodologies, quick ability to understand research papers and research domains are special qualities of a teacher. I enjoy my passion for data science with the skillsets of teachers. Each data is different, the approach to solve each problem is different, and each time seeing new data and new approaches to solve problem brings happiness and exits. This excitement comes only when you have a passion for this field. To uncover hidden truths behind data and build a system that can learn without explicitly being programmed is a very interesting domain to work on. As a data scientist, the First thing I see is that data science is an iterative process. The single solution created will never satisfy neither you nor the clients. The best solution is found by iterating again and again through various approaches to the problem. One must be prepared not just with machine learning or data analytics, but be prepared with skills to develop API’s, interactive dashboards using javascript as well. One common approach that helped with to deal systematically with the dataset is the “CRISP”- Cross industry-standard platform. Following the guidelines of “CRISP” helps to organize and achieve the goals of building a data science project. Reinforcement learning, computer vision, transformer-based models, Streaming analytics, online learning models, etc are creating wonders out there, There are many things to learn, so the journey of data scientist does not stop by just graduating and entering a company, it continues with continuous learning.

A samurai emerges with immense training and practice not by birth

Follow your dreams and passion, the success will reach you eventually. Keep striving to learn consistently.

Data scientist aspirants from non-statistics or non-computer field, keep in mind that to become a data scientist develop the passion and undergo rigorous training like samurai to become a skilled data scientist.

For any help, Reach out to me @ Linkedin. I would be happy to help you.

https://www.linkedin.com/in/indurajpr/

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