Photo by Alex Radelich on Unsplash
Next year is your year.
This time next year, you are going to look back and be amazed by how much you have grown as a data scientist. I don’t doubt it. And neither should you.
To do so, though, will take work and dedication. To start you off right, I want to discuss 5 New Year’s resolutions to help you get to the next level.
For you, next year is a year of growth. Commit to spending at least 30 minutes of learning every day.
Learning can take any form you want. It could be reading a book, starting a new online course, going to a meetup, or starting a blog. If you need some ideas for great books, check out this post:
The Top 3 Books to Get Started with Data Science Right Now
_And build a great foundation of knowledge_towardsdatascience.com(https://towardsdatascience.com/the-top-3-books-to-get-started-with-data-science-right-now-3f9945d78011)
For this goal, don’t overthink what to learn, just start learning. From my experience, almost all data scientists are motivated by learning, but I find we can often spend too much time thinking about what to learn. We want to pick the best possible subject, so instead of spending time learning, we spend time thinking about learning.
That is not to say that you shouldn’t spend some time thinking about what you would like to learn, but once you have an idea, just run with it. Maybe you have always wanted to learn more about reinforcement learning. Go do it! Your first few days of learning might just be discovering the best resources on reinforcement learning. Then you can start digging in.
Also, don’t forget that this is your time to learn. If you are dreading this time, then you need to change it up. Pick a different subject, find another learning technique, or get a friend involved to mix it up. The key is to find a way to consistently spend time learning every day. If you can do that, you will look back on 2020 as a year of immense growth.
Be an Owner
No matter your role, in 2020, people will look to you as an example of how to drive significant value for the company. Instead of just accepting a project “as-is” and checking the boxes asked of you, you will develop an ownership mindset by asking the following questions:
- How does this project move the company closer to its goals?
- Do I understand the vision of the project, so I can be empowered to think creatively about potential solutions?
- How can I take the initiative to ensure this project delivers value? Even if some of those items are not part of my “job description.”
There are many other questions you could ask, but these 3 should be a good start to shifting your mindset from a doer of tasks to a deliverer of value. You will be amazed by how much this will change your work. It will push you to be a better collaborator, learn new skills, and think outside the box because your work isn’t done until it has provided value.
I find this skill is especially important for data scientists because data often spans so much of the company. This often means that you, as a data scientist, have to work across a lot of the company to ensure your work delivers value. If you can master this skill, you will become invaluable.
In 2020 you are not going to be sucked in by the hype of the latest and greatest just for hype’s sake. Sure, there are complex models which can deliver spectacular results, but when starting a project you will ask yourself:
How do I build my first model in 1 day
If you need some help on how to do this, take a look at this article:
My Potent First Day Routine for Phenomenal Success on Your Next Data Science Project
_The best way to spend your first 8 hours on any project_towardsdatascience.com(https://towardsdatascience.com/my-potent-first-day-routine-for-phenomenal-success-on-your-next-data-science-project-c96874f4bf16)
Building your first model in 1 day will force you to focus on staying simple and only solving the most critical problems. Your model will most likely be pretty bad, but you will now have a baseline upon which you can build. It is significantly easier to add complexity to than to remove it. Following this one simple rule will push you as a data scientist to ignore the fluff and center in on the core problems you need to solve.
As the saying goes, “You learn more in failure than success.” I 100% believe this is you fail smart. Just failing doesn’t mean you learn or grow. Failing and then taking the time to understand why and setting goals for improvement can help you grow significantly.
This year, you will set a goal of being better at failure than anyone else.
You will set goals that push you to your limits and will lead to some failures. And when you fail you will take the time to grow.
For example, maybe you set a goal of delivering value with a type of machine learning model you have never used before. That could be something like reinforcement learning or probabilistic deep learning. This type of goal will push you outside your comfort zone and force you to grow.
Be careful, though, that you fail appropriately. Taking a large risk on a mission-critical project which is time-sensitive is not a good idea. The same kind of goal on a tertiary project, though, could make a lot of sense. This goal complements the “Start Simple” goal well as it ensures you continue to push your boundaries. Otherwise, there could be really valuable algorithms or technologies you are not leveraging because you never branched out.
Finally, in 2020, set a goal to give back to others. Find time to mentor, teach, guide, or help others grow. Next time a junior data scientist reaches out on LinkedIn looking for advice, reply back and try to help. Adam Grant makes a great case on the value of being a giver and I couldn’t agree more. Not only does it help others, but it also helps you.
I am sure that no matter your experience or skill level, you can find ways to assist others on their path. You just have to look.
I hope my 5 New Year’s resolutions will help you start your year off right and allow you to look back on 2020 as a tremendous year of growth, learning, and success.
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