If you want to find the secrets of the universe, think in terms of energy, frequency and vibration. — Nikola Tesla
I have been working in Data Science and ML field since past 4 years and I have learnt a lot through my work and personal experiences. Data Science is not straight forward but if you give enough time, you will learn the tricks of what this job demands and based on your interest, you can carve out your own journey.
How can I become a data scientist if I am new in this field?
Being a data scientist involves wearing a lot of hats in any organization that you’re a part of — creating ML models, experimenting with data based on business problems, putting ML models in production and running them on frequent basis, monitoring model performance, etc.. The list goes on and on.
From my personal experience, it is easy enough to point that Data Scientist needs to have following tools in his arsenal in order to deliver results -
- Math behind ML algorithms — may not be easy but we will try our best
- SQL : useful for data collection and ad-hoc analysis
- A programming language of preference — Python / R / SAS etc
- Basic exposure to cloud technologies — AWS,GCP etc
Getting gradual experience in these 4 years can enable you to start thinking about how data science can be leveraged across different industry verticals to solve different use cases such as Natural Language Processing, Fraud Detection, Real Time Recommendation, increasing ROI etc.
In the next tutorials to follow, we will cover each of these branches in thorough detail so that we can be well equipped with the tools. I also plan to learn many of the concepts in depth and explain it to the larger audience here so that everyone can benefit from this.
See you soon !!