There are many blogs out there, at the same time there are many posts that claim data scientists earning higher than other roles and calling the profile “sexiest” in the century. How to get into it?
For a non tech background person, it can be daunting once they start getting exposure to the amount of coding and maths that is required. My suggestion for working people (say a sales manager who wants to do data science) would be as follows:
- Remove time constraints from your mind, and remove salary hike desperation from the mind, and focus on enjoying the data science learning journey. Understand how data science is able to solve business problems, and also understand it’s limitations. Salary hike should be a by product, some get it in 6 months post learning data science, some take a few years, but mind you our career is 20 years long, so if you are able to solve business problems with data science, that’s when you will actually scale up.
- Don’t be in a rush to jump out from your existing job, (unless you have a great offer in hand).
- In your existing company, see if you can get hand to any data science project. Work on it over the weekends, extra time. No one will initially say, ok stop working on your billable project and join data science. You have to learn it in extra time.
- Take your time to do actual projects, choose a mentor (can be your friend or colleague) discuss your projects. This is crucial as you might end up doing a project not so relevant for data science.
- The above process will take say 1 year, and now you are confident about data science understanding. Highlight this project in your resume.
- Make sure you have understood it’s end to end cycle and how it adds value to the business side.
- Apply for positions that sit interfacing data science and the business team. You’ll grow there the highest being from non tech background. However, there are exceptions that I have seen.
*These are my personal opinions. This is a generalised path. Make sure to reach out to a counsellor if in doubt, or comment below.
If you’re good at mathematics and have good analytical skills, you can surely start a career in data science. Also love for playing with data will take you to greater heights.
First make sure you are thorough in statistics.
Then master a programming language, preferably R or Python.
Participate in hackathons, go for certifications. There are many cheap and best courses available like MOOC.
The more you love data, the more it pulls you into this field to discover new patterns.
Yes, having a technical degree is advantageous, but anyone with a non-technical background can pursue a meaningful career in data science.
Start from the beginning!
Even if you’ve never worked with data before, you may start by learning how organizations use data and how it’s used in the industry. After that, you can create a program to prepare yourself with the necessary technical skills.
Real-Life Projects Can Assist!
Getting practice training and experience is crucial to getting a data science job at a reputable company. To accomplish this, one must develop a portfolio of projects that address real-world bottlenecks and inefficiencies.
Look for a mentor!
Choosing a professional path in data science can be scary and overwhelming, especially when you’re just starting out. On the other hand, finding a suitable mentor can mean the difference between looking for a job, preparing for an interview, and starting work as a data scientist.
If you want to pursue a profession in data science, don’t be scared to test your skills. Determine which persona you belong to and where your gaps are, then take the required steps to advance in a competitive talent pool. Develop the necessary abilities, apply what you’ve learned in real-world scenarios, get honest feedback, never be afraid to ask for help, and never stop learning!
You can try this learning path on data science for guidance
A strong desire to learn and comprehend how technology works can go a long way. Many people believe that cybersecurity is all about breaking into or hacking into things, but it is truly all about understanding how technology (and people) work.
The key is your motivation and ambition to learn how technology works and to never stop playing, not your technical experience. Furthermore, there are an increasing number of sectors in cybersecurity that focus on human-centered concerns rather than technical issues. Soft skills like privacy, security awareness and training, governance, security communications, and cyber law and ethics are required.
While the majority of awareness experts come from highly technical backgrounds and have a deep understanding of technology and human-related dangers, they may lack some essential qualities for success in program implementation, such as strong communication. Indeed, new industries and opportunities in cybersecurity are developing and expanding, with prospects that did not exist five or even ten years ago.