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I get it, the data science field moves at an insane speed and it can be hard to catch up with it!
In order to stay ahead of the curve in data science, it is important to continuously develop your skills.
There are actually very solid ways in which you can develop your data science skills in an organic and healthy manner that will benefit you in the long run.
With that in mind, in this article, I will present 7 tips that will help to improve data science skills and become a better data scientist in 2023!
Read on for more!
How to Improve Your Data Science Skills?
1. Stay Current With the Latest Trends
The data science field moves extremely quickly, and if you’re not ready, it can easily move on without you thinking.
I remember when I had to code R shiny apps all on my own to deploy my data science pet projects in the past. Now, there’s even an R shiny editor in alpha to create your own R shiny web application just with a few simple clicks!
If you’re not keeping up with the latest trends, you’ll quickly become irrelevant and your skills will suffer as a result.
To stay current with the latest trends, it is important to:
- Follow prominent data science figures on social media platforms like Twitter
- Subscribe to data science newsletters or Reddit forums
- Attend data science meetups or webinars
- Constantly learn new things and don’t be afraid to get out of your comfort zone!
By following the latest trends, you’ll not only improve your skills but also show potential employers that you’re always keeping up with the latest advancements in the field.
This is an extremely important skill to have as a data scientist since companies are always looking for those who can bring the latest and greatest technologies to their organizations.
For example, there has been a rising interest in the use of AI for art, video, and content creation.
Many of the popular companies that data scientists are vying to work for desire employees who look to new technologies in data science to solve their business problems.
So, make sure you’re always on the lookout for new trends in data science!
2. Use Different Data Sources
When I first started my journey into data science, I was only using data from Kaggle.
Now, I use a variety of different data sources to help me with my projects.
Not only does this make my projects more robust and interesting, but it also helps me develop new skills that I can use in the future.
Some of the different data sources that I use include:
- Twitter data
- Facebook data
- LinkedIn data
- Amazon reviews data
- Reddit data
By using different types of data, you can learn how to work with various types of formats and structures.
This is an important skill to have as a data scientist since you will often be working with data that is in a less-than-ideal format.
Additionally, by using different data sources, you can also learn how to integrate data from various sources which is another skill that is highly sought after by employers.
In fact, since the majority of a data scientist’s time is spent on data cleaning, learning how to work with all kinds of unstructured and dirty data will train your data massaging skills.
For example, if you were tasked to work with Natural Language, you’ll need to learn how to clean text in the masses using Natural Language Processing.
This process can take a long time to learn because of the complexity of unstructured text such as survey responses, tweets etc. You need to learn how to do handle them eventually.
So, make sure to use different types of data when working on your projects!
3. Practice Analytical Thinking
One of the most important skills that you need to develop as a data scientist is analytical thinking.
This involves breaking down complex problems into smaller pieces, understanding all of the different variables involved, and then finding creative solutions.
This type of thinking is extremely important in data science since it will often be required to solve difficult business problems.
To develop your analytical thinking skills, it is important to:
- Practice solving complex problems
- Take on new challenges
- Constantly be learning
- Be open-minded to different solutions
By practicing analytical thinking, you will be able to develop the skills needed to tackle any problem that comes your way.
This is an essential skill to have as a data scientist since you will often be required to solve difficult problems that don’t have easy solutions.
In order to think flexibly as a data scientist, you need to consider how to use nifty solutions from other industries that helped to solve a similar problem for them.
Then, look to see how they can be applied in data science workflow. For example, Tesla believes in building neural networks (human brain) to process the surroundings with cameras (eyes).
Data scientists need to go beyond just their technical skills and data science skills – we need to learn to look at the design industry on how to do data visualization well.
We need to look at the manufacturing industry and borrow ideas like automation which will make data science pipelines easier to build!
So make sure you are practising your analytical thinking skills!
4. Experiment and Take Risks
Oftentimes, I find myself taking risks in working out a data science workflow.
It’s a ‘risk’ because going down a route and fully committing to it can take a lot of effort to convince your boss and take up lots of your own time (coding late into the night alone).
I encourage you to experiment and take risks when it comes to data science!
This is how you will learn the most and develop new skills quickly.
Some of the risks that I have taken in my data science career include:
- Building my own data pipelines from scratch
- Working with new and challenging datasets
- Trying out different machine learning models outside of my comfort zone
- Optimizing my code for performance
- Going down an unusual analysis route
- Trying out a completely new programming language
- Learning new unfamiliar data science IDEs
By taking risks, I have been able to learn new skills quickly and develop my data scientist skills.
Experimenting and taking risks is how you will learn the most and develop new skills quickly. So don’t be afraid to try something new or go down a path.
For biological sciences majors, I would also recommend purchasing a new laptop for biology majors if your older computer no longer has the capabilities to handle more bioinformatics computing!
5. Be Persistent and Determined
In working in the data science field, many data scientists may start to grow weary from working and not seeing the light at end of the tunnel (or the data pipeline in this case!)
I encourage you to always look to improving your coding skills, learning about new machine learning algorithms, and never stop improving data scientist skills that will land you that dream job!
This can mean taking up new AI courses online to supplement your knowledge.
Data science is a field that is constantly changing and evolving, so it is important to be persistent in your learning.
Some ways that you can be persistent in your learning include:
- Reading data science blogs and articles
- Listening to data science podcasts
- Watching data science webinars and videos
By consuming content related to data science, you’ll naturally pick up data science processes from others that may be more efficient than yours.
Data science skill is not developed overnight, persevere in your pursuit of both technical skills and soft skills – you will get there soon enough!
6. Be Curious and Inquisitive
Data science is a science after all, and where science is practiced, curiosity is highly-valued as a skill!
One of the most important traits that a data scientist can have is curiosity.
In order to be curious, you need to constantly ask yourself questions about the data that you are working with and the problem that you are trying to solve.
Some questions that you can ask yourself include:
- What does this data tell me?
- What are some patterns that I’m seeing?
- Why is this happening?
- What can I do to improve my results?
By constantly asking questions, you will be able to develop a better understanding of the data and the problem that you are trying to solve.
Additionally, you will be able to come up with new ideas on how to approach the problem.
So always be curious and inquisitive when working with data!
7. Collaborate and Learn From Others
In the data science community, it is essential to collaborate and learn from others in order to improve your skills.
One of the best ways to learn is by working on projects with other data scientists.
Not only will you be able to pick up new skills from them, but you will also be able to develop a better understanding of the data science process.
Another way to learn from others is by attending data science meetups and conferences.
At these events, you will be able to listen to talks from experienced data scientists, learn about new artificial intelligence trends in the field, and network with other data scientists.
So make sure to collaborate and learn from others in order to improve your data skills!
Now that you know all the tips on how to improve data science skills, it’s time to put them into practice!
If you’re looking for more ways to improve your data science skills, make sure to check out our blog for more articles like this one.
I hope you found this article helpful and that you’re able to use these tips to become a better data scientist!
Another quick recommendation is also to get a fast and efficient laptop that’s good for data analysis as code can run really slowly on older computer models.
Thanks for reading!
Justin is the founder and author of Justjooz. He is a Nanyang Technological University (NTU) alumni and a former data analyst.
Now, Justin runs the Justjooz blog full-time, hoping to share his deep knowledge of business, tech, web3, and analytics with others.
To unwind, Justin enjoys gaming and reading.