Shaping Your Own Success: Lessons from You Are a Badass

-

Over the last few weeks, I’ve been starting my mornings by reading for just 10 minutes a day. I recently finished You Are a Badass by Jen Sincero, and it’s been the perfect way to set the tone for the day, getting me into that “go get it” mindset.


I wanted to share some key takeaways that really stuck with me.

  • The Power of Your Thoughts – Focus on what you want, not what you fear.

  • Visualising Success – Train your mind to expect success.

  • Decide, Don't Just Wish – Act like success is a matter of when, not if.


The Power of Your Thoughts

One of the biggest ideas in the book is that we’re all vibrating energy, and we attract whatever frequency we put out into the world. That made me reflect on how I’ve approached things up until now.

For a long time, I had this habit of always preparing for the worst. Even if something only had a slim chance of going wrong, I’d focus on it. I wouldn’t let myself fully believe things could work out because I was too afraid of being disappointed. And when the worst actually happened, I’d tell myself, See? Good thing I didn’t get my hopes up. It felt like a way of protecting myself.

But now I wonder, what if I attracted that outcome by focusing on it so much? The book made me question whether my anxious energy planted doubts in other people’s minds, whether I unknowingly influenced situations just by expecting them to go badly.

I’ve seen this pattern in other people too. Those who constantly complain about life seem to face endless obstacles. Is it just bad luck? Or is their negativity shaping their reality? It’s a chicken and egg situation, but either way, the only thing we can control is our mindset. And maybe, by shifting that, we can start changing our outcomes too.


Visualising Success

One of the key exercises in the book is to take five minutes to imagine your dream life as if it’s already happened. Not just thinking about it vaguely, but really immersing yourself in that moment and feeling truly grateful for everything you’ve achieved.

At first, I was skeptical. How is this supposed to help? This is just indulging in fantasy before coming right back to reality.

But then, by chance, I also heard neuroscientist Dr. Tara Swart talk about the same technique on the Diary of a CEO podcast. When two different sources backed this idea, I decided to give it a try. And it didn’t go as I had initially expected.

Instead of feeling like my goals were distant and just an idea, they suddenly felt possible. By imagining myself already there and practicing gratitude for achieving it, it felt real, like I’d already experienced it and I just had to find my path to get there. I wasn’t just hoping for it anymore. I was getting excited about making it happen. I started thinking about what I could do today to move closer to that goal.


Decide, Don’t Just Wish

One of my biggest takeaways from the book is this:

You have to make an actual decision that you’re going to achieve your goal. Not just want it.

If it’s just a want, every hurdle will make you hesitate. Every setback will make you question whether it’s worth it. And before you know it, you’ve talked yourself out of it.

But when you decide and fully commit, failure just becomes part of the process. You figure out how to get through it, not whether you should keep going.

As Winston Churchill once said: 


"Success consists of going from failure to failure without loss of enthusiasm."


That’s the energy I want to bring into everything I do.

Comments

Post a Comment

Popular posts from this blog

My thoughts on DataCamp's Associate Data Analyst in SQL Career Track

-
Image
I started Data Camp's Associate Data Analyst in SQL career track at the end of March and expected it to take about 39 hours, since that’s what the estimate said. But in the end, it was closer to 45 hours and honestly, it was worth it. Not just because I finished the course or got the certification, but because of how much more clear SQL feels now, especially compared to when I was learning it for the first time during the Google Data Analytics course. Learning the Order of Execution One of the most helpful parts of this course was the way it explained concepts in a logical order. In particular, understanding the order of execution helped me finally get the difference between WHERE and HAVING. During the Google course, I couldn’t figure out why one worked and the other didn’t. It was never clearly explained that the filtering happens at different stages: WHERE filters rows before the grouping, and HAVING filters after. Once I understood the order of execution, it just clicked! M...
Read more

Reflections on the Stanford Statistics Course: How Statistics Can Reveal or Distort the Truth

-
Image
At the end of February, I took the next step in my data analytics journey: the Stanford Introduction to Statistics Course on Coursera. Since I had just completed the Google Data Analytics Professional Certificate Course that same day, I couldn't help but find myself comparing the two. What I didn’t expect was just how different the experience would be. A More Intense Learning Experience The course is labelled as beginner-friendly and estimated to take around 14 hours to complete. In my case, it took closer to 18 hours—partly because the content was quite dense and partly because I wanted to ensure I truly understood each concept before moving forward. With the Google course, I was able to move quickly, watching most sections at 2x speed. That wasn’t an option here. I frequently had to pause, re-watch explanations, and work through my own examples to fully grasp the material. While some concepts were familiar from previous studies, I aimed to go beyond just recalling formulas. I w...
Read more

Clarifying Commonly Confused Stats Concepts

-
Image
In a previous blog post about the Stanford Introduction to Statistics course, I mentioned that I’d be revisiting a few topics that took me a little longer to wrap my head around. This post is exactly that. A way for me to break down topics, not just to clear things up for myself, but also to create something I can revisit whenever I need a quick refresher. So here are the topics I'm going to cover: Law of Large Numbers vs Central Limit Theorem Significance Level vs. P-value Confidence Level vs Confidence Interval Standard Deviation vs Standard Error Z-test vs T-test 1. Law of Large Numbers (LLN) vs Central Limit Theorem (CLT) Both are foundational ideas in statistics and often mentioned together, but they’re doing two very different jobs. LLN says that as your sample size increases, your sample mean gets closer to the true population mean. CLT explains that regardless of your population’s distribution, the sampling distribution of the sample mean becomes approximately normal a...
Read more