Cómo trabajar en Ciencias de Datos en la era AI

Summary

The video script discusses the high-paying field of Data Science and related roles such as Data Engineer and Data Analyst, emphasizing their continued demand despite industry changes. It highlights the impact of AI, particularly large language models like GPT-4 and Google's Gemini, on data handling and job roles. The speaker advises against the notion of reduced job opportunities in tech, advocating for continuous learning and skill enhancement. The script also covers the importance of understanding business models and the role of soft skills in career growth, suggesting that while AI is powerful, it lacks cognitive thought and cannot replace the strategic understanding of business professionals.

Takeaways

• 💼 The best paid jobs continue to be at the intersection of mathematics, data, and code, with roles such as Data Scientist, Data Engineer, and Data Analyst remaining highly paid even in times of mass layoffs in the tech industry.
• 🔍 The speaker emphasizes that the reality of the job market has changed, with concepts like ETL, data cleaning, and Data Lakes still being relevant, but AI and large language models are now capable of automatically cleaning and interpreting data, which changes the landscape.
• 📚 There is a shift in required knowledge towards understanding concepts like context windows, vector databases, and techniques for embedding private data into large language models, as AI is becoming more integrated into data science roles.
• 🧠 The speaker clarifies that AI does not possess cognitive thought and that there are no current technologies aiming for AI to achieve cognition, highlighting the difference between AI capabilities and human understanding.
• 🏢 Understanding the business model and the levers to move within a company is what differentiates professional data scientists from others, which is crucial for making a real impact.
• ⚖️ The speaker discusses a legal case involving AI and data usage, where The New York Times sued Open AI for copying articles, indicating upcoming changes in how AI handles data.
• 🚫 There is advice against learning programming or data science due to perceived lack of jobs, but the speaker argues that these fields still have more job opportunities compared to many other careers.
• 📈 The speaker encourages continuous learning and self-improvement, stating that not being hired often means one needs to elevate their skills rather than giving up.
• 🛠️ The speaker mentions the importance of learning necessary programming skills for data science, such as Python, and highlights the significance of understanding command lines and using Linux.
• 📊 Statistics, Python, and SQL are identified as fundamental skills necessary for a career in data science, regardless of the level of AI integration.
• 🤝 Soft skills are also crucial for career growth, including communication, public speaking, emotional intelligence, and networking, which can help stand out in a competitive job market.

Q & A

What is considered the best paid job currently?

The best paid job is at the intersection of mathematics, data, and code, which is often referred to as Data Science or related roles such as Data Engineer and Data Analyst.

What is a Data Lake in the context of the industry?

A Data Lake is a term used in the industry to describe a database that is so disorganized that multiple other poorly organized databases are combined to later clean the data.

What is the impact of large language models like BART on the field of data?

Large language models like BART have changed the field as they are capable of automatically cleaning data and inferring numerical information in natural language.

What does the speaker suggest about the current state of artificial intelligence in terms of cognitive thought?

The speaker suggests that as of now, artificial intelligence does not possess cognitive thought and there are no technologies currently aimed at giving AI cognitive capabilities.

What is the significance of the lawsuit between The New York Times and Open AI mentioned in the script?

The lawsuit signifies a major change in how AI is used and managed, as Open AI was found to be directly and blatantly copying public articles from The New York Times for its data model.

Why is it advised not to stop learning, even when facing job market challenges?

It is advised not to stop learning because it puts you at a significant disadvantage compared to others. Learning is the only thing that is 100% within our control and can make a difference, unlike market conditions which are not.

What are the three main careers in data that are in high demand according to the script?

The three main careers in high demand are Data Science, Data Analyst, and Data Engineer.

What is the importance of learning Linux and terminal for a career in data?

Linux and terminal are fundamental knowledge areas for a successful career in data. They are necessary to operate a computer entirely through the terminal, which is a key skill in the field.

What are the three core skills needed for careers in data, according to the script?

The three core skills needed are statistics, Python, and SQL.

Why is it important to connect technical skills with business objectives in data careers?

It is important because if technical skills are not connected with business objectives, one cannot be considered a data scientist. They would merely be a code operator, which is not conducive to success in their career.

What is the speaker's view on the concept of meritocracy in the workplace?

The speaker believes that meritocracy exists but is not just. One must understand the human structure of the workplace and know how to play the game to succeed.

What additional skills are highlighted as important for career growth besides technical knowledge?

Soft skills such as public speaking, effective communication, emotional intelligence, and networking are highlighted as important for career growth.