If you want 2022 to be an awesome year for your data science career, follow some habits that successful data scientists have.
1. Collaborate in data science communities
Do you know what’s the fastest way to learn something? Collaboration! You need to be part of a community to grow faster as a data scientist.
If you’re new to the field, there are a lot of things you can ask in data science communities to keep learning. If you already have some experience, you can enter Kaggle competitions, answer questions on Stack Overflow, and share knowledge in a blog post or video tutorial.
Regardless of the experience you have in data science, consider joining a community to give and receive help from other people.
2. Set coding standards
As a data scientist, you also need to learn good habits from successful programmers and one of them is to set coding standards.
You need to keep a well-defined and standard style of coding when working for a company. This gives a uniform appearance to the code written by many people, improves readability, reduces complexity, and helps to easily detect errors.
You should do this even if you don’t belong to a big organization. This will help you adapt to the standards, conventions, and rules already followed by others.
3. Create a roadmap to shape your project
Have you ever started a project, finished a task, and then felt unsure about what’s next?
I used to have this problem until I created a roadmap that shows every stage of the project with all the things and people involved in it. This helps you have the big picture of the project, so you can easily recognize the project’s goals as well as the inputs and outputs of each stage.
Fixing mistakes becomes easier when it’s clear where the data and scripts come from.
4. Understand business problems
It doesn't matter how skillful you’re at coding or creating models, if you can’t understand the business you’re in, you won’t succeed as a data scientist.
No one is going to value your work if you don’t help the company you work for reach its goal — no matter how good your model performs. Do some research to know more about the company you work for, the industry they’re in, and take some time to think how a data scientist like you can help reach some of the goals the company has.
Asking questions to your boss and colleagues helps a lot too. They can help better understand your role in the company.
5. Stay up to date
Every year a new technology rises, tools get obsolete and code becomes deprecated. This is why you should always keep an eye on the new stuff in the field by reading blogs, research papers, and books.
Staying up to date will help you take advantage of the latest advancements in data science. As a result, you will be one step ahead of the rest and in most cases get the best performance possible in a project.
Being open to learning how to use new tools and adapting to change is a habit that will help you in your career (this leads us to our next habit)
6. Be open to change
Things change on a daily basis, but we’re reluctant to adapt to some particular situations.
I can’t tell how many people (without any special reason) kept using Python 2 after Python 3 was released. I was reluctant to learn seaborn after spending many days learning matplotlib and I was unwilling to replace Plotly with Pandas for the same reason.
Sooner or later you will realize that tools should increase your productivity. If there’s something out there that makes your life as a data scientist easier then it’s worth learning it.
7. Avoid distractions
It’s impossible to solve problems without focus.
I know you have lots of emails to answer, messages to reply and plans to make but they will distract you from doing your tasks and, as a result, your performance as a data scientist will decrease.
Multitasking is tempting, but, believe me, the benefits of focusing on a single thing are amazing! I can say I learned this the hard way when I lived without a phone for more than six months.
But you don’t need to live without your iPhone to avoid distractions. Turning off notifications on the phone and laptop should be enough.
8. Keep your code simple
Less isn’t always more (especially in coding).
Reducing the lines of code might not help other people easily understand what your script should do. In contrast, reducing the lines of code just for the sake of it can make it more complex to understand even for you!
Try to make complicated code simple. The simpler the code, the easier to understand it. I learned this after making video tutorials, guides and creating my own courses. This helps put yourself in other people's shoes when reading your own code.
Share your code! If others can’t understand your script, probably there’s still room for improvement.
9. Document Your Code
Have you ever opened up old scripts and thought, “What in the world was I thinking?”
If you had a hard time trying to understand your own code, imagine how others would struggle to understand your script. Always remember this phrase.
“Code is more often read than written.”
— Guido van Rossum
This is why documenting your code is important. All good data scientists I know have the habit of commenting and documenting their code. Comments help better understand code, its purpose, and design. On the other hand, documenting code describes its use and functionality to users.
If you use Python, check this guide to learn how to properñy document Python code.
10–12. Listen, focus on solutions and choose your words carefully (effective communication habits)
According to Forbes, these are the 3 habits of highly effective communicators. Why is this important? Well, as a data scientist, you need to develop your communication skills.
Remember that data science is not only about creating the best model but also communicating your findings to non-data scientists. This will let others know that you clearly understood the company’s goals and that you’re applying the knowledge you have as a data scientist to help reach those goals.
13. Ask open-ended question
Asking questions is good but do you know what’s better? Asking open-ended questions.
Questions that start with “Why, Where, When, Who, What, Which” will help you have a better understanding of a new topic. This will also help you be seen as a data scientist with good communication skills, which is important in your career.
Of course, there are some situations where a simple yes-no question fits better, but whenever possible ask open-ended questions to enrich a conversation.
14–15. Take care of your posture, drink more water (Healthy habits)
Being in front of the computer from 9 to 5 isn’t good for your health. I’m not telling you to quit your precious data science job, but to acquire some healthy habits in the office.
Simple things like taking care of your posture and drinking more water will help you stay healthy, which is key to being at your peak performance at work. That’s the minimum you should do, but things like spending time outside, going for a walk, and waking up early are other healthy daily habits that you can start today.
Listen to your body and do whatever you need to be at your best.
16. Learn a new thing each week
We already mentioned how the data science field evolves over time. This is why you should consider learning something new each week. At the end of the year, you’d be amazed at how much you learned each week.
Be curious to learn new things and you’ll advance in your data science career. To have a better idea of this, check my 52-week curriculum to become a data scientist in 2022.
Probably you already know most of the stuff listed there, so take it as an example to create your own roadmap to organize when you want to learn something new.
17. Organize your desk and computer
If you want to boost productivity and optimize your workflow, make sure you have everything in the right place on both your desk and computer.
I can’t tell how many times I stopped coding because I couldn’t find the dataset I use in a project or forgot the right syntax to make a visualization with Python. I could solve this by giving proper names to files and folders and using a Python cheat sheet for data science (which, by the way, you can download here).
The same goes for your desk. Don’t let your laptop charger stop you from finishing your data science project. Use your drawers to save things you might suddenly need and only leave things you frequently use on your desk.
18. Read research papers
As someone who has written a couple of research papers in the past, I can say that reading papers from Masters and P.h.D. holders helps anyone keep up to date with the latest trends.
There is a lot of information out there, but in some cases, you need to be extra careful about the sources you’re extracting information from. That said, the insights and information provided in a research paper are highly reliable.
Each week or month read at least one research paper relevant to topics you’re interested in to grow in your career.
19. Give yourself credit
Data science projects tend to belong, so give yourself credit after finishing a task.
Don’t wait for your coworker or boss to give you credit for everything you do. Be proud of every little accomplishment in your data science career.
20. Take a break regularly
As a data scientist, you might spend hours collecting, cleaning, or transforming data. There’s nothing wrong with dedicating a lot of time to your job as long as you take breaks regularly.
Overworking might lead you to silly coding mistakes in the short run and health problems in the long run. This is why you should take breaks from time to time.
This will give you a fresh perspective when you resume your work.
21. Ask yourself whether what you’re doing is worth it
A good habit to keep growing as a data scientist (and in life) is to ask yourself questions about things you do on a daily basis.
Some data scientists have had the same role for many years. This creates a comfort zone that you’re unaware of until asking yourself, “Is this worth my time?” You can be more specific by listing the everyday tasks you have at work.
If you answer “yes” multiple times, you’ve probably reached a plateau in your position or are doing tasks that are no longer relevant to your career, so consider getting a new job in a different department or company.
22. Don’t Get Stuck With One Programming Language
Python is my favorite programming language, but you know what? I don’t wanna get stuck with this language.
You never know what’s coming in the future. Now Python is widely used in data science but it might lose its charm any time. This is why is important to keep an eye on the new trends and at least familiarize yourself with them.
You don’t need to take intensive courses to learn multiple programming languages. Instead, try new things here and there and analyze what’s convenient for you to learn seriously in order to develop further in your career.
#Source: medium
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