By the time I applied for TripleTen’s Data Science bootcamp, I already had over seven years of experience across dedicated data and product analytics roles. I felt confident and successful in my role, yet honestly uninspired in my career. I taught myself Python fundamentals years before, but didn’t progress significantly further; I was overwhelmed by the sheer volume of information there was to learn along with the vast number of ways I could learn.
Still, I was aware that a skill gap existed between where I was in my career and where I wanted to be. Over time, my desire to address it only grew, but so did my overwhelm. How would I know if I was learning the right things? How would I know if I was understanding the material correctly? How would I transform theoretical knowledge into practical application? How would I prove and showcase my new skills to prospective employers?
I knew I was asking all the right questions, but it wasn’t until I found TripleTen’s Data Science program that I knew I had finally found my answer.
The skills that would open the next door
Before I began my journey with TripleTen, I was deeply experienced with the suite of traditional analytics tools and skills: SQL, experimentation, event-based analytics platforms, BI and dashboarding software, spreadsheets, statistics, etc.
However, the skill set required to take my career to the next level looked quite different (data science!). I spent time studying the job descriptions for my future dream roles and identified which skills were most relevant and which I was most excited to learn about. Then I looked into how to get them, and TripleTen popped up.
Over the next eight months, I developed Python skills and dove into the world of machine learning, predictive analytics, scripting, and large-scale data manipulation and processing. Within that, the following areas stood out to me as the most valuable or interesting:
- Advanced Python (large-scale data manipulation and processing, data task automation (scripting), statistical and causal inference analysis)
- Software development tools (Git and GitHub, Jupyter Notebook, integrated development environments (IDE))
- Machine learning (feature engineering, supervised learning, unsupervised learning, natural language processing (NLP), neural networks, time series forecasting)
Why TripleTen was perfect for me
I considered applying for a data science bootcamp before, but needed flexibility to balance it with a full-time job and needed confidence in my decision given the large time commitment and investment.
It was also personally important to me to be able to strike a balance between the ability to progress through the bootcamp at my own pace and to have a formal system that both validated my work and offered adequate support. TripleTen offered exactly that.
The bootcamp is organized into three modules across 17 different two-week sprints (a different topic is covered each sprint). So, while the course is paced for you, you aren’t limited by that. When I had a busy period coming up in life, I could get ahead in the course in advance to accommodate it.
There’s a project at the end of each sprint that must be reviewed and approved by the team before you can move onto the next sprint; this ensures you absorb the content fully, can apply it practically, and receive valuable feedback — plus, you walk away with a full portfolio of project work. Lastly, there are dedicated instructors, office hours, and virtual study rooms (to collaborate with peers in) to help you if you get stuck–or just want to discuss the material.
What TripleTen did for my career (and confidence)
Over 700 hours of material later (yes, really), I look back in amazement at just how much I learned. The bootcamp absolutely delivered on all the skills I aimed to develop, but perhaps just as crucially, I deeply reconnected with my love for learning. The bootcamp went beyond what was outlined in the curriculum and dove into each topic with just the right amount of depth. Now I feel confident and empowered to continue deepening my learning on my own within the areas of the course I enjoyed the most or the areas that are currently most relevant to my projects at work.
I immediately saw tangible value from the bootcamp too: I received a job offer for both roles I had applied for that required the use of Python within the interview process. Just a handful of months prior, I never would have imagined that I’d be capable of whipping together a logistic regression model for a company and presenting it live to an interview panel! In my role today, I see more, new, and better ways to arrive at a solution for any given business problem, collaborate more effectively with the data science team and more technical stakeholders, and proactively seek opportunities to put my new skill set to work.
Today, I feel more inspired and aligned with my career than I have in years. All the knowledge I acquired significantly increased my confidence at work and gave me an enormous and highly rewarding sense of personal accomplishment and direction. Graduating from the bootcamp was truly a bittersweet feeling — I was so proud of myself for completing it (and for trusting my decision to dive head-first into it to begin with!), but also genuinely sad that it was over. Simply put, I couldn’t be happier with my decision to enroll, and the momentum it created continues to carry me forward.
Ready to take the next step?
TripleTen’s bootcamp didn’t only help me to bridge my skill gap — it positioned me to grow in step with the direction analytics is heading. As more organizations shift toward automation, predictive modeling, and AI-driven decision-making, having a solid foundation in machine learning, Python, and end-to-end data workflows means I’m not just prepared to adapt, but to drive meaningful impact.
Whether you’re entirely new to the field of data or looking to upskill like I did, I couldn’t recommend TripleTen’s bootcamps more strongly. If you’re thinking about taking the next step, feel free to reach out to me on LinkedIn. I’d be more than happy to answer any questions or talk about my experience.
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