Today, I am going to cover the 2nd most frequently question by my readers and followers, How they, I mean you can get into Data Science?
In this episode, I am going to give you my perspective on how you should approach the Data Science field in 2020.
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So let’s get started…
Data Science is an interdisciplinary field, which means it is an amalgamation of Maths, Programming and Business/Domain, I would like to add here Data Literacy as well.
In an end to end solution, many other fields may also come into play like Cloud Computing, DevOps and Data Governance.
Actual proportion and expertise required for each field vary from project to project; organizational culture and data maturity also plays its part.
Hence we often see very different roles and responsibilities with blurred scope in job postings by different organizations.
Let’s have a look at what skills you may need to build in order to get into the data science field.
First and foremost is the Foundation:
-What, Why & How of Data Science (ML/DL/AI)
-An understanding of T-shaped Skillset, which is having expertise in your area of interest and just enough awareness of other related areas.
Second, you need to work on the Core skills of Data Science:
Data Literacy at the very beginning, which is knowing :
- Data Lifecycle (how data moves in an organization)
- A typical flow of how data is Ingested, Stored, Processed, Analysed & Served.
Understanding of Mathematics
- Linear Algebra
- Multivariate Calculus
Various Machine Learning Algorithms & Deep Learning Framework
At last, you need to develop just enough understanding of these fields like Data Engineering, Cloud Computing, Dev Ops, Data Governance e.g.
- General What, Why & How
- Their usage in the context of Data Science
To cover all the challenges a beginner faces, I have built an end-to-end learning framework which smoothly transitions you into a data science professional.
This framework has four phases:
- Navigate is the very first phase of your data science journey, where you need to understand the overall landscape before diving deep.
- Build phase covers all the concepts, processes, tools you need to learn and the resources you need to refer to gain the required knowledge.
- Launch is the phase where you build your portfolio, network with like-minded professionals and start looking for a job.
- Excel phase details out how you can stay up to date & excel in this ever-evolving field.
I will talk about this framework in detail in upcoming posts.
Ankit Rathi is an AI architect, published author & well-known speaker. His interest lies primarily in building end-to-end AI applications/products following best practices of Data Engineering and Architecture.
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