Would you like to avoid falling into a very common Data Science shortcut trap?
And trust me, it’s a common trap that limits our potential.
This idea was inspired by the amazing book “So Good They Can’t Ignore You” by Cal Newport. For reference, Cal is a professor of Computer Science at Dartmouth University with a PhD in computer science from MIT.
I discovered some life-changing facts in the book that I couldn’t wait to share with you.
So, join me in exploring some of the key learnings from the book and how to apply them to mastering data science.
The shortcut trap
We see everyday tools, posts, apps, and more designed to skip the difficult part of the process.
Tools that write code for you or that supposedly analyze data for you.
And don’t take me wrong; nothing bad with taking an easy route and making things easier for us. In the end, working smarter and not harder continues to be the best recipe.
But the problem lies in skipping what will make us better, skipping the important part of the process that will lead us to grow and become a better version of ourselves.
Because when it comes to building a fulfilling career in data science, skipping the fundamentals can hold you back in the long run.
And sure, some tools can automate some tasks. But a deep understanding of the process allows you to use these tools effectively, not just as a crutch. It’s like learning to cook before relying on a fancy kitchen gadget.
I realized that what really makes us grow is acquiring the skills and engraining that new version of ourselves without skipping part of the process just because it’s slow, difficult, and sometimes full of effort and sacrifices.
And in the end, skipping the learning process only limits your potential.
This is what you should experience in your own pursuit of “good.” If you’re not uncomfortable, then you’re probably stuck at an “acceptable level.” So Good They Can’t Ignore You by Cal Newport.
Take, for example, this post you are reading right now.
There are tools out there that can write decent text and even mimic my writing tone. But if I went that route, I wouldn’t develop my writing skills or refine my tone. I’ll be losing the connection with you and my true love for sharing tools, inspiring stories, and transformational guidance to transition to a career in DS.
The same goes for data science.
Sure, some tools can automate repetitive tasks, but a strong foundation is crucial.
Decoding the trap
Let’s uncover the DS shortcut trap together.
First, think about a Data Scientist you admire.
What makes them stand out? Is it because of their achievements? Their role? Is it their ability to explain complex concepts clearly? Or their capacity for extracting insightful stories from data?
Now, think about their trajectory to get to their current position.
Do you think they got there by skipping learning the difficult concepts?
Or does that person seem like someone who avoided learning how to analyze data and provide insights?
Do you think that person is just gifted and was born as that admirable person that is today?
Absolutely no.
For sure, that person took on the task of learning, growing, and investing hours and hours to become better in what they do. That person took the path to aligning with their vision of who they wanted to become.
That person likely dedicated countless hours to learning, practicing, and building their skill set.
Or, in the words of one of my favorite organizational psychologists and writers:
The true measure of your potential is not the height of the peak you’ve reached, but how far you’ve climbed to get there.
Adam Grant – Hidden Potential.
How can you avoid the “unintentional shortcut” trap?
First, start by reflecting and answering the following questions:
- Am I avoiding diving deep into coding fundamentals like Python?
- Am I avoiding the hours of coding and learning how to create a DS portfolio?
- Am I relying solely on automated visualizations instead of learning how to create impactful plots?
- Am I trying to skip what will make me better in DS?
Second, follow these transformational tips:
- Embrace the Fundamentals: Learning the core skills will be your foundation in data science. Invest time learning programming languages like Python or R and understanding statistics and data manipulation techniques.
- Focus on the long run: Mastering Data Science skills isn’t just about checking boxes. By acquiring DS skills, you’ll be a better problem solver and someone who can think critically about data. And this knowledge will open the doors to fulfilling career opportunities in the long run.
- Shot to becoming the best: Remember that you can also be the best in DS. You can be your version of the person you admire. Create a worth-reaching goal, and go for it!
Now let’s continue the conversation. Does this trap resonates with you?
Lina Marieth xx
Quote of the week
Impostor syndrome says, “I don’t know what I’m doing. It’s only a matter of time until everyone finds out.”
Growth mindset says, “I don’t know what I’m doing yet. It’s only a matter of time until I figure it out.”
Adam Grant – Hidden potential