อธิบาย Data Scientist, Data Analyst และ Data Engineer ใน 15 นาที | Skooldio Support EP.1

Summary

The video transcript features a discussion about the role of data scientists in organizations, emphasizing the importance of understanding business needs and creating value from data. The speaker, Taweerong Jirapattanakul, a manager at Studio Na, addresses common questions about data science careers, the skills required such as coding and statistical knowledge, and the potential for job opportunities. He also touches on the evolving nature of data science, the impact of technology on job roles, and the significance of continuous learning and adapting to new tools and technologies.

Takeaways

• 😀 The speaker emphasizes that organizations should not just focus on creating data models but also understand how to utilize data to drive business value.
• 🔍 The script discusses the importance of data scientists having a deep understanding of business needs and not just technical skills in creating predictive models.
• 💼 It mentions that data scientists often need to have skills in coding, statistics, and mathematics to analyze data and build effective models.
• 📈 The speaker highlights the significance of data scientists being able to communicate their findings and insights to business stakeholders.
• 🌐 There's a mention of the growing demand for data scientists in the job market, especially in large companies that are increasingly reliant on data-driven decision making.
• 💡 The script suggests that those interested in becoming data scientists should learn programming, statistics, and consider taking courses to build a strong foundation in data analysis.
• 🎓 It points out that a background in various fields can be beneficial for a data scientist, not just computer science or statistics, as understanding the business context is crucial.
• 📊 The speaker explains the role of a data engineer, which is different from a data scientist, focusing on managing and processing data to make it usable for others.
• 💬 There's a discussion about the importance of continuous learning and adaptation in the field of data science, as technologies and tools are constantly evolving.
• 📚 The script encourages aspiring data professionals to not only learn technical skills but also understand the business aspects of the industry they are in.

Q & A

What is the main role of a Data Scientist?

A Data Scientist primarily focuses on creating value from data by analyzing it from new perspectives and building models for predictions to help organizations utilize data more effectively.

What are the key skills required for a Data Scientist?

Key skills for a Data Scientist include coding proficiency, in-depth knowledge of statistics and mathematics for data analysis and model creation, and a crucial understanding of business to create models that benefit the business.

How does the process of data analysis typically begin?

The process of data analysis typically begins with data collection, followed by managing and analyzing the data to understand its potential benefits to the business.

What is the importance of creating models in data analysis?

Creating models is important as it helps in making predictions and decisions based on the analyzed data, ultimately aiding the business in making informed decisions.

Why is it necessary to measure the outcomes of data analysis?

Measuring the outcomes of data analysis is necessary to ensure that the models created are solving real business problems and functioning correctly.

What is the job market like for Data Scientists?

The job market for Data Scientists is abundant, as large companies are increasingly seeking professionals who can leverage data to drive business benefits.

What advice is given for someone looking to become a Data Scientist?

It is advised to start learning the basics such as coding, statistics, and then progressively delve deeper into data analysis and model building.

How can understanding the business be beneficial for a Data Analyst?

Understanding the business is beneficial for a Data Analyst as it allows them to create models and analyses that directly address the business needs and contribute to its growth.

What is the difference between Data Analysts and Data Engineers?

Data Analysts focus on analyzing data and creating models for business insights, whereas Data Engineers are responsible for building systems to manage and process data for analysis.

Why is it important for a Data Engineer to understand the end-to-end data pipeline?

A Data Engineer must understand the end-to-end data pipeline to ensure data is processed and stored efficiently, making it readily available for others to use for modeling and analysis.

What programming languages are commonly used in data roles?

Python is a commonly used programming language in data roles due to its versatility and the availability of libraries and frameworks for data analysis.