Top 10 Common Mistakes to Avoid When Building a Career in Data Science and Machine Learning.

When I started my career in Data Science, I spent a lot of time in things that wasted my time and energy. I thought I am on the right track toward my dream job, but I was wrong.
Here are my 10 mistakes that you should avoid when you are starting your career in data science.
First, I spent too much time on the theory because I thought these will be my foundation in my career.  This is draining and took too much time from me. But it was a mistake. Data science is an applied field, therefore, skills are acquired while practicing.
Secondly, I have this futuristic thought of building advance technologies. I was so driven that I forgot the fundamentals. Knowing the basic is important.
Third, I coded too many algorithms from scratch. This is good for learning purposes but not for building a career. It is better to understand the algorithms and their applications and settings.
Also, I tend to grasp everything when I am too excited since each tool has a unique feature. However, it is better to learn tools one at a time.
Keep in mind that a degree in academics is not a passport to your dream job. I thought I could easily pursue my career, but in the real world, things are different. It is a good start to take internships to learn and serve as a stepping stone towards your dream.
With optimism, I looked for a job. I put all the technical details to impress. Don’t do this. Instead, write down your skills and how these will add value to the company. Plain and simple yet conveys a clear message.
Before I got a job, I relentlessly looked into job vacancies with data scientist but only a few posted such. Therefore, don’t limit your search. Try other keywords, look into the required skills and job descriptions. Expand your searches.
Never underestimate the value of domain knowledge. Have knowledge in the industry you are applying for.
Avoid being unprepared during interviews. Review past projects and know every major step.
Lastly, not building my communication skill. It will be best to be able to discuss jargons in layman’s term for smooth communication.
There. I hope this will help you in your career development.
Source: https://elitedatascience.com/beginner-mistakes