For every data science job posting in the market, there are hundreds of applicants in the competition. So as a fresher how are you going to withstand the competition and make your way into the industry? In this article, I am going to talk about the do’s, don’ts and certain tips and tricks that will help you your profile to standout from your peers.
My Data Science Journey:
Stage-1: “The Confusion”
The year was 2017 and I had just passed out of engineering. I was excited to get into the software field and One of my cousins suggested that data science and AI would be the next big thing. After preliminary research and a few YouTube videos later, I got hooked and wanted to become a data scientist somehow. There was so much of content on open web for learning data science, and like everyone else, I tried going through blogs and YouTube videos. More than learning the subject, I ended up getting lost, as data science is an ocean and I had no clear learning path and started shooting in all directions.
Stage-2: “The Search”
I have now understood that I would need a proper mentor, who could give me direction and make me learn various concepts right from the fundamentals. I had then started looking out for data science training programs. Most of the programs were offered by standard software training institutes and I was again confused with all the options available. All of them pretty much sounded the same.
Stage-3: “The Revelation”
In the process of my research, I have come across Data Science Authority, an academy based out of Hyderabad. This is where I met my mentor, who gave me a perspective on how to approach the field of data science and told me that any sort of good data science training would definitely give me an initial boost, but ultimately it would be me, who will have to put in the hard work and follow a structured learning methodology to achieve success. Stage-4: “The Learning”
Data science is not just a Technology, but a mixture of business knowledge, Applied Mathematics as well. As a Data scientist, one needs to get into the shoes of business stakeholders, solve problems by applying various concepts of mathematics and leveraging various tools and technologies like R, Python, TensorFlow etc. Pick couple of tools and start mastering them.
Stage-5: “Practice before you preach”
The data science aspirant, you would need a lot of practice and for that, you would need many datasets. DSA conducts something called as “Experiences”, through which they simulate various business scenarios and give you an opportunity to solve the problems. This gave me a good hands-on idea and additionally, I have also started practicing the concepts learnt by applying them to various data sets found at Kaggle and cluzters.ai. Both of these are free platforms and have very good content, codes and datasets, which are very useful for any Data Science aspirant.
Stage-6: “The Hunt”
You need to position yourself suitably in the market and with the support of my mentor, I have finally created my Data Science Profile, highlighting all the relevant details. I have also given a mock interview with my mentor before the D-Day arrived, and this prepared me for industry expectations. I have also attended a few hiring hackathons and walk-in interviews
Stage-7: “A dream come True”
I scored a job with Franklin Templeton as a Data Scientist and my learning goes on for ever now. The growth rate at which the filed is moving is huge and once you make it, then your profile would always be in demand in the industry.
Do not spend a lot of money on costly training programs and certifications, Industry doesn’t care if you have a certification from IIIT, IIM or any big brand, as most of them are not full-time programs and are only as valuable as any distance learning program. You would need the initial guidance and mentorship, but from there on, you would already have good grasp on the subject and you can build further knowledge depth with time. everything depends on your hard work, skill and talent. Do’s:
1. Find a good Mentor to start your journey 2. Learn data science from business, technology and Math perspective. 3. Work and practice every day without fail 4. Practice with datasets on Kaggle and cluzters.ai and attend hackathons 5. Get an internship if possible 6. Get your resume supervised by an experienced person (it can be your mentor)
1. Don’t fall for placement Guarantee. Recruitment in the industry doesn’t happen through Institutes, it is based on hackathons and walk-ins mostly 2. Don’t fall for Realtime projects trap, Data is the most valuable asset for any organization and they are not going to expose it to any institute for their student’s practice 3. Don’t worry about certifications. In the world of Data science, there are no industry standard certifications (like SCJP for Java), as all the tool and technologies are open source 4. Don’t delay your learning exercise, with every passing month, there is a steep increase in the competition. You can set it off during your 3rd /4th year of engineering to get an edge. All the Best! Hope to see you in the Industry someday! HAR