20 Oct Advice to Aspiring Data Scientists
There has been quite a lot of buzz around data science in recent times and many people now aspire to be data scientists.
In order to help you navigate the waters of data science, get advice from two data scientists who have been in the game and can tell you what it feels like and what you should do to become a successful data scientist.
As an aspiring data scientist, I will highlight briefly two major challenges you will face:
- Information overload
- Impostor syndrome
Due to the plethora of information available now, there are lots of free online courses, training, materials. Personally, I am a beneficiary of several FREE online courses. I have over 23 free courses with Coursera, some with Udacity and several with Udemy, and so on.
As you begin and continue your journey, there is a high possibility of getting swamped in the number of resources at your disposal. You will be faced with the challenge of choosing which course to do. If a new free course or training comes up, you will want to jump on that and this causes distractions.
So what should you do?
Set out your goals clearly for a time. Be focused to attend pieces of training, take courses that align with your goals for that time. However, you can archive some of these courses for another time.
The next issue stems from the first — Impostor syndrome (check out how to overcome the impostor syndrome, written by Mary Abiodun).
Impostor syndrome in simple terms is when you don’t appreciate the progress you are making or have made due to what others are doing. You should get it clear that everyone sets goals for themselves and so, you should not feel intimidated by what others are doing.
Ask yourself these questions:
Am I making progress?
Do I achieve my set goals?
A sincere answer to these questions will see you appreciate more the things you have done and you will forge ahead to achieve more.
Why do you want to be a data scientist? You will get frustrated. You should have good reasons to stay there when you do get frustrated. There are other tech areas you can be involved in, different from data science. Know what you’re in for.
Data Science has 5 main different parts: Big Data Analytics, Blockchain Technology, Business Intelligence, Internet of Things, and Machine Learning/Artificial Intelligence. They have different tools and platforms. I’m into Business Intelligence, I use Power BI, I learned Python and R, which is inclined towards Machine Learning but because I didn’t have a flair for coding, I had to switch up to Power BI and Business Analytics.
You don’t have to be a Machine Learning Expert before you call yourself a data scientist, so long as you are using data to make the insight, predictions, and decisions, you are a data scientist. Data is very important. People should learn to record and keep their data.
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I hope this advice will help you understand your journey as a data scientist and make great strides with it.
Got more advice for aspiring data scientists, please drop them in the comment section. We would love to have them.
Up next, watch out for the next article on data careers you might not be aware of.