What 2023 holds for Data Science beginners?
2022 was an amazing year for all data science beginners and it’s about time we discuss what 2023 holds for Data Science enthusiasts! A big congratulations to everyone who tried learning something new this year and has the zeal to continue their learning journey in the coming year as well!
There are a lot of things you can update in your data science journey for the upcoming year. In this article, we’ll go over some things that you must try and learn in 2023 to stay relevant with Data Science today.
Feel free to skip to any section that sparks your curiosity in this article and do share it with everyone if you think this article will benefit them! Things we’ll cover in this article are;
- Free learning resources and how you can find them
- Quality > Quantity in what you learn
- Coherence / Having a Flow & Incrementality
- Carving your Niché
- Planning for the future
Free learning resources and how you can find them
There’s no doubt that with the democratization of knowledge online and with the boom of the Digital era post the Covid 19 Pandemic, we’re seeing an exorbitant number of courses online (paid/unpaid) that learners can take advantage of and gather as much knowledge as possible.
This calls for notice, the fact that all of my data science education has been online and most of it being free of cost, it is important that a little research from your end as a learner goes into what you need to study and what you can learn online for Data Science.
For starters, you can always find roadmaps like these that help you gather any and all information about what you need to know to have a certain skill or expertise;
To follow up on these roadmaps and skills that you need to learn, you can always reach out to options like the following;
- Coursera’s Financial Aid Program for free learning and certifications
2. Free courses offered by Google
3. Free Master’s In Data Science Designed by Claire Corthell
4. IBM’s Cognitive Class (dot) AI
5. Upgrad’s Free Learning Programmes
Any of these are programs that I’ve personally learned from in my journey and I suggest that you try and look into these or find some programs driven by your research. They carry unimaginable value and are free of cost.
Best for people who are aiming for a Career change into Data Science in 2023! (find my article on how you can transition into data science as a career at the end of this article)
Quality > Quantity in what you learn
There’s always a thing that people in their data science journey try and complete as many certifications or courses as possible and more often than not, these choices become redundant and the added value of these certifications on your resume does not help much.
This is where the quality aspect comes into play. Try to look for a course or program that suits your needs in the best possible way and try to find ways in which you can reduce the time required by you to learn a certain skill by avoiding redundancy.
Another important thing is to ensure that you pay attention to any opportunities in your life where you can apply the skills and show your work instead of having a very large list of certifications. Try working on projects that you can relate to and even if you can do work that can make a difference on a very small scale, it’s appreciated work.
Regardless, use your time wisely in 2023!
Another aspect of this is to ensure that whatever you learn, make sure it’s relevant. As amazing as SAS or SPSS is for Statistics, maybe more companies require people who know Python or R and not SAS. So ensure you cover that aspect too in your journey.
Coherence / Having a Flow & Incrementality
When someone starts their data science journey, it’s best to always finish the fundamentals first and learn the basics of all tools and skills required to decide where your interests lie and grow better in the same area.
Coherence and Incrementality of your learning will go hand in hand and this will also help you apply your skills at your workplace (or projects at your college).
Let’s consider this, if you’re starting in Data Science as a complete beginner, it’s best if you learn the fundamental math and statistics that go into data science, learn how to work on them and data manipulation in Excel, and move to SQL to understand how larger data tables work.
When this completes, you can always work on understanding how visualization works so that you can work on watching data trends and ascertaining things about your data beyond numbers.
Then comes coding, which can be the tool you can use to put all your eggs in one basket and enable you to wrangle with and visualize data while also enabling you to predict new data points with Machine learning.
After this comes the most important element of learning, “Domain Knowledge”. There’s all the data in the world for you to learn from and work on, but to use your data in a meaningful and fulfill the true need for Data insights, you can learn Data Storytelling which will only make sense when you apply your domain knowledge there.
At the end of this, you can choose to grow in any specific role, right from a Data Engineer to an ML engineer to Business Analyst!
For a more profound approach to how you can do this; read this article:
Carving your Niché
You have all the skills needed to be a data scientist and you have everything you need in your arsenal to apply for your dream role.
But that’s the catch if your learning is restricted to these online platforms and all you can learn from there, it’s what every other applicant has done for sure.
The key is to ‘think beyond courses’
There are multiple ways in which you can do this, for example;
- Have your own Data Visualization library (if that’s what you enjoy) with interesting visualizations that show precisely what the audience needs to see.
- Find out cases that you can work on and write compelling data stories that show your acumen to the fullest.
- Approach small companies in your neighborhood and try to understand their data flow and make one improvement in any segment to have a real-world impact.
- Make your website and explain something to fellow learners in a way, unlike the rest.
- Work on projects and coding competitions and prove your skills.
- BE CREATIVE.
Planning for the future
As important as continuity and consistency are in your learning journey, having a plan is equally important. You can always take a break every month or a couple of months to track your progress, ascertain your goals for the coming weeks, and drive your effort on the back of a planned approach.
This will help you plan out how you can reduce any confusion and gaps that may arise out of any new things on your plate. Most people I know in data science have some method for tracking their data science journey and to me, it happens to be my blog.
Find what works for you and track your journey and PLAN your journey.
Conclusion
As someone aware of how the data science journey plays out for most people, in the beginning, it’s easy to start questioning your skills or doubt if you’re ever going to make it into Data Science based on the number of things that one needs to learn.
Don’t fall for the trap and see the ocean of learning as an amazing place where you can learn SO MUCH! That being said, you just need to be good at one thing that makes you stand out and if you can do what you do best, you’ll find that there are more opportunities for you out there than you think.
Stay tuned for more in data science!
Wishing everyone a happy new year and I hope your journey takes you exactly where you want to be or in better places in 2023! Happy Learning everyone!
Let me know in the comments below what you think is going to be the difference-maker in your data science journey ahead. Also check out my previous articles here, if they interest you!
For all my articles:
~ P.S. All the views mentioned in the article are my sole opinions. I enjoy sharing my perspectives on Data Science. Do contact me on LinkedIn at — Yash Gupta — if you want to discuss all things related to data further!
