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There is no future where data is less important! - DSBoost #14
Welcome to issue #14 of DSBoost, the weekly newsletter where you can discover interesting people in the ML/AI world, get the main takeaways of a relevant podcast, and stay up to date with the latest news in the field!
💬 Interview of the week
This week we interviewed Brian, who is an experienced Data Director. Enjoy:
What did you study/are you studying (if your background is different from DS, how did you end up in the field)?
I got my B.S. in Computer Science with a concentration in Database Management back in 2008 from Johnson & Wales University.
I’m currently working towards my Masters in Business Analytics from Worcester Polytechnical Institute.
I also studied Advanced Python for Data Science at Harvard University in 2020.
What are your favorite resource sites and books (ML/AI)?
YouTube: Corey Schafer and Tech with Tim are my go-to channels for learning Python and sharpening my skills. Recently, I’ve been watching a lot of videos from AI Explained to keep myself up to date on the recent developments in LLMs and GPT. Amazing time to be working in data!
What got you into your current role (portfolio, certification, etc.)?
I’ve been at my current company for 3 years and have been promoted twice in that time, starting as a Data Analyst Consultant and now working as a Director, leading a team of analysts and software engineers.
A combination of my work experience and my portfolio got me into my current role. I’ve been working in data for 15 years and have had a simple portfolio online for the past 5 years or so.
What do you enjoy the most in your work?
I love the challenges and problemsolving aspects of my work.
What tools do you use the most / favorite tools?
As a Director, I have to have a broad understanding of lots of different tools. My go-to tools are Python and Pandas, SQL, and Airflow for data automation and Tableau for data visualization and presentation.
Do you use ChatGPT or other Al tools during your work? If so, how do they help you? Do they change your approach to problems?
Yes! I find myself using ChatGPT just about every day in one form or another. Most recently, I had ChatGPT help me write a complicated function on a new concept I’d never had to work through before (a somewhat complicated business problem of paginating through REST API results).
It was INCREDIBLE! ChatGPT not only helped with the initial coding, but then also helped me troubleshoot and debug when things weren’t quite working as expected.
What is your favorite topic within the field?
I’m currently focused on helping more people across the world master the fundamentals of SQL. It’s a foundational skill that you really have to know if you want to work with data.
The second one would be how ChatGPT and other LLMs are going to disrupt the data analytics career path. I’ve already seen sparks and it will be a huge shift that won’t impact everyone equally.
Which one of the recent AI/ML models will have the most significant impact on the industry in your opinion?
LLMs for sure. But I’m also fascinated by the leaps that have come along with the sub-components of these models like transformers, RLHF, and reflection.
What are you currently learning or improving (topics you are interested in nowadays)?
I’m learning more about sales and marketing. I have a general interest in these areas, but I think many engineers and experts in the field overlook how important these skills are.
I think everyone should learn how to “sell” their work and “market” themselves and their ideas, wether it’s to an actual customer or to your boss or a client or whatever.
What is the biggest mistake you've made? (preferably DS related)
My biggest mistake was leaving the data industry in favor of a more general IT director position. I left because I felt my career had stagnated and an opportunity fell in my lap with an offer I couldn’t refuse.
But my data skills got VERY rusty after basically zero coding for 2 years. I ended up getting back into data, polished up my data and software engineering skills, and now have a flourishing career in data. So it all worked out in the end!
What is your most significant achievement? (preferably DS related)
Developing a fully automated daily executive reporting system. The existing solution was a complicated mess of emails and Excel files.
I developed a new solution from scratch using Python, SQL, and Jenkins and knocked the project out of the park.
Can you share a fun fact about yourself?
I love trail running! I run about 20 miles (32 km) per week and have a very intense mountain race coming in a few weeks. Later this year, I’m planning my first ultramarathon (30 miles / 48 km).
🎙️ Podcast of the week
Data Teams should focus on these areas:
Making internal teams more productive or effective.
Driving a totally new initiative - for example identifying a new customer segment and create a hypothesis around it.
Uncover new opportunities - totally new marketing idea or totally new product feature.
Support scale - though providing tools or insights.
“Successful teams prioritize use cases over technology'“
Successful teams are partnering with the business to identify use cases and they are really working with the business to execute on them. So the use case comes first and they are using the technology to support the use case.
There is no world where like 5 to 10 years from now, data is less important!
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🧵 Featured threads
🤖 What happened this week?
Yuval Noah Harari, author of "Sapiens" has warned that the rise of AI could threaten the survival of human civilization from an "unexpected direction." He argues that AI's mastery of language could threaten human culture by influencing intimate relationships and changing opinions and worldviews, and he calls for the regulation of AI technology to prevent irresponsible deployment in the public domain.
IBM CEO Arvind Krishna recently mentioned that they might need to pause hiring for certain roles because they are looking into ways that AI and automation could take over some non-customer-facing positions. While this could impact around 7,800 jobs, it's not set in stone yet and they're still figuring things out.