DSB podcast #3

We went through the question “how to get into data science” in the third episode recorder just before Xmas 2024. Audio is in Czech, but AI generated episode summary is in English.

Feedback welcomed: podcast@datasciencebulletin.com

Insights and Sources

We have outlined (Jakub mostly) also online path to grasping enough of Data Science to be eligible for a junior position. This can be understood as a alternative to formal education or complement.

Formal Education

Prague University of Economics and Business:

• Economic Data Science (Bc.)
• Data Analytics Program (Bc.)
• Applied data analytics and AI (Msc.)
• Economic Data Analysis (Msc.)

Czech Technical University – Faculty of Electrical Engineering

• Open Informatics – AI and Computer Science (Bc.)
• Open Informatics – Data Science (Msc.)

There are many more 😉

Online Path

1. ONLINE LEARNING
   • DataCamp (gamified platform for learning python, data analytics, tools and basic algorithms); DS Track
2. ONLINE COURSES
   • Machine learning introduction specialization
   • Deep learning specialization
3. BOOKS
   • Understanding Deep Learning
   • An Introduction to Statistical Learning
   • (hardcore) The Elements of Statistical Learning

Additionally we mentioned Junior.Guru, Czech initiative that helps people switch careers into IT field.

The AI generated summary was bad this time:

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Our hosts discussed the evolving requirements of data scientists in the job market, emphasizing that what was relevant eight years ago may not entirely apply today. Companies are increasingly looking for practical skills over theoretical knowledge. The duo stressed the importance of practical, hands-on experience that can be directly applied to real-world data science tasks.

Advice for Aspiring Data Scientists

Martin and Jakub provided actionable advice for aspiring data scientists:

• Learn by Doing: Engage with practical projects and online courses to build a robust portfolio. This approach helps in mastering the tools and techniques needed in today’s data-driven industries.
• Stay Updated: Keep abreast of the latest trends and tools in the field. The landscape of data science is continually evolving, and staying current is crucial.
• Understand the Business Aspect: It’s important for data scientists not only to manage data but also to derive insights that are strategically valuable to their business.

Looking Ahead

Looking forward, our hosts plan to revisit some of the latest developments in AI and machine learning in an upcoming episode. They aim to sift through the news and trends to focus on what’s truly relevant and durable over time.

In closing, today’s discussion underscores the dynamic and accessible nature of data science. Whether you are a student, a professional transitioning from another field, or simply a curious mind, the field of data science is ripe with opportunities for growth and impact.

Join us next time as we continue to explore the exciting world of data analytics and the many paths you can take to become a part of this thriving field.

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One clarification, when we were studying, python already had version 3.3 out. Barely. Ecosystem non existent, though.