Journey into the World of Data Science

Basty’s Scrapbook

Hi, everyone! My name is Basty, and I’m a data scientist and educator from Metro Manila. If there’s something I value so much, that is education. Education has allowed me to not only dive into the amazing world of code and data, but also to encourage and inspire others to do the same. Read more about me here.

The theme for the month of September revolves around “Journey”, and with the recent start of the Data Science Fellowship Cohort 10, it is timely to talk about how you can start your journey in data science. Now there are a lot of articles already out there teaching you how to start in data science, and some of them have the habit of bombarding you with a bunch of concepts, links, and fancy words. So instead of focusing on the “what,” let’s focus on the “hows” and “whys” that craft an effective way to start one’s journey into the world of data science. Let’s get started!

“Blueprint”

There’s really no one solid blueprint for learning data science. Everyone you’ll meet will have their own story of how they started in their career, and most likely how they even got lost at one point (It happens to everybody!). From my own experience, when I researched on the things I needed to learn, all I could find were a list of lists of data science courses to take, videos to watch, and books to read. As someone who believes in project-based learning, I learn more by doing rather than reading books.

So how does one effectively learn data science, and start their journey in this vast and exciting field?

Find your Why

First, choose what ignites your passion for learning data science. Why? Data science is such a big and broad field and it's easy to lose track of what you’re learning because of the endless amount of information, and this often leads to burnout.

The secret to overcoming this vast amount of information (and possible burnout) is simply by having a reason to learn. Identify your why, and use it as motivation and as a guide to your journey.

Focus on the Fundamentals

There’s a high chance that you got hooked into data science because you’ve seen some of the fancy concepts such as “machine learning, image recognition, and artificial intelligence.” As tempting as it is to dive right into these hot trends, you’ll immediately get lost without understanding the basics. How will you run if you don’t learn how to walk first?

In reality, almost 90% of the time as a data scientist, most of your work will revolve around the fundamentals such as data cleaning! By mastering the basics, breaking down the complexities of advanced topics would be a breeze!

So what exactly are the fundamentals? Start by learning these:

A. Python Basics
B. Basic Statistics (descriptive, inferential, etc.)
C. Pandas, NumPy, Matplotlib, Seaborn
D. Linear Regression
E. Logistic Regression
F. K-Means Clustering

To actually master and practice these fundamentals, a great way to do it is by making projects! Projects are a critical part of becoming a data scientist because it shows if you know how to do data science in the first place and how well you can do it.

Soft Skills == Technical Skills

I’m telling you now that even though data science might sound like a very technical field (and well… yes it is), the soft skills are just as important. In your entire career as a data scientist, you will be constantly presenting results and insights of your analyses to different stakeholders. Knowing how to effectively communicate the findings and story of your analyses is what differentiates a mediocre data scientist from a great one!

Always remember that a data scientist is only as valuable as the insights they generate. In short, you have to learn how to be an amazing communicator because you want your analyses to be accessible to the whole organization. 

Now you might be wondering, “How do I present and communicate something that is complex and technical in nature?.” I totally agree with your question, and here are some tips that might be able to help you find the balance between the technicalities and story:

• Make sure you understand the domain (topic) of your analysis. You have to understand the topic you are presenting or else you’ll never be able to explain it.
• Always assume that you are presenting to people who have no idea what you are doing, even if they are in the same discipline.
• Always focus on the story you want to communicate.
• Practice presenting it to non-tech savvy people and get feedback.
• Be clear, concise, and straight to the point as possible.
  

Find a Mentor

You can learn a lot when you work/reach out to others. Often, you will hear that you only need one mentor, but I’d like to say otherwise. I like to think that we need 3 types of mentors in our career:

• Someone who has been in the field long enough - This type of mentor is the most important mentor because they will help guide and solidify your journey in data science. They are also your “go-to” mentor when you feel you’re stuck in your journey.
• Someone who is on the same journey - This mentor will be your partner in your journey because they will be the one who you go through the same hardships, excitement, and milestones with. 
• Someone who just started - This type of mentor doesn’t necessarily apply to you as of now since you’re still starting your journey in data science, but generally this mentor is someone who will keep you up to date with the recent trends in data science.

Increase the difficulty

I know that I’ve highlighted the importance of mastering the fundamentals, but overtime you will have to increase the difficulty of concepts that you are taking. This will help you improve and move further into your journey because often people will slow down or even get stuck in their journey when there aren’t any improvements in their learnings.

Just ask yourself every now and then, when was the last time you felt challenged in a project/topic? 

If you’re feeling too comfortable already with the fundamentals, here are some tips you can do to level-up your data science game:

• Take more advanced data science courses/topics such as Natural Language Processing, Network Analytics, Image Recognition.
• Work with larger datasets, wherein you’d have to use big data tools (PySpark).
• Improve your understanding of the theory/math behind machine learning algorithms.
• Teach others. This is an underrated way of increasing difficulty, but if you try it out, you’ll see how valuable and rewarding teaching is. This, in return, also helps your communication skills!

"Journey"

I truly believe that anyone can learn data science as long as they are equipped with the right mindset. And now that you know what mindset to bring into this never-ending journey of learning, the next step is to go out there and begin your journey by doing!

If you want a more structured approach on learning data science, make sure to check out the 15-week Data Science Fellowship of Eskwelabs. I hope you found this blog helpful, and that it sparked motivation to start your journey.

Never stop learning, and see you in the industry!