But, Data Science acted as a Savior for me.
And like a true savior, it lending me a hand to get me out of that chaos.
The reason why I choose Data Science was not out of gut feelings but because of the massive exponential increase of data.
Data is the fuel that was/is/ will drive(ing) industries.
“It is better to aim high and miss than to aim low and hit.
”I took 2–3 months of offline training in Python and specifically Data Science.
I studied it in technical as well as non-technical aspect but, I was more into non-technical aspects.
It included business awareness, communication skills, and problem-solving strategies.
Gradually, Python became my strength to survive the technical portion [i.
e Data pre-processing and Data analyzing].
I felt that there was more to data science than to what I was exposed to.
I went on to talk to my peers and seniors on how to improve further in this massive field.
Then, I decided to start learning Big Data and Hadoop through online training.
I even had access to live video sessions and covered various projects to practice the same.
It took time to get hold onto it.
Mathematics or basically the statistical part was not at all the problem but the programming did bring me some drawbacks.
For this, I started using various online code practicing website which did bring weight to what I had learned until then.
These rigorous practices on different platforms and projects improved my resume! I started applying for some internships to add value to numbers that I have been learning till then.
Initially, the response was less, rather I should say no response at all.
I learned how to stay motivated and inspire me to achieve my goals.
I Equipped my knowledge with some ‘Technical literature’ such as — Python Crash Course-by Eric Matthes, Practical Statistics for Data Scientists by Peter Bruce, Hands-On Programming with R-by Garrett Gorlemund, R for Data Science and Learning SQL–by Alan Beaulieu.
Though I couldn’t complete all of them, they did add some utility to what I had learned before and introduced me to new technologies like R and SQL.
I didn’t refer any book for Hadoop and Big Data as I was satisfied and equipped with these well due to online courses.
But, if while studying you think that you need any guidance regarding these technologies, you can always look into “Hadoop — The Definitive Guide” and “Big Data in Practice- by Barnard Marr.
”Then, later on, I updated my resume with some new projects that covered the latest technologies I had learned.
Soon, I started receiving calls for internships.
I joined one of them for my “summer training/summer internship”.
Those 75 days brought out the essence of data science and the real-life meaning behind the information gained by then.
I then joined my college’s placement offer.
With all the courses to which I was expected to have the knowledge and the courses which I already have due to my prior studies stimulated my start directly as a junior Data Scientist.
The brownie point here is that it is generally not offered to freshers.
But, my project work and training played a vital role in this boost of my career.
I followed all those steps which I learned through my training period and practiced them here several times.
All this made me more effective throughout the project assigned.
I made my thoughts and ideas of which approaches to use, which tool to apply.
Learned to differentiate between what is important and what is not.
All this knowledge helped me become a more effective data scientist.
I learned that success in few projects or achieving a goal doesn’t give you the right to label yourself “effective”.
Being effective is an ongoing process and lets you learn more humbly and increase your productivity.
All this gave me exposure to new projects and helped me learn something new each day.
I realized this very early in life that there is no such thing as free lunch and if you want something in life than you have to give it your heart and soul.
To get real-world experiences you need to :a) finding a data set you are interested, b) asking and trying to answer questions about it, c) write up the results, and d) rinse and repeat to develop experience.
To call yourself a true Data Scientist and have a “long term plan” that manipulated it, you will have to pave your path and create your own identity[that feeling of happiness is inexpressible :)].
In short, let’s get to work.