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

Skooldio

22 Jul 202214:53

Summary

TLDRThe 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.

Outlines

00:00

💡 Introduction to Data Science and Career Opportunities

The speaker, Tavirojn Kirapakul, introduces himself as a manager at Studio Na and a former Delta Side Facebook page admin. He emphasizes the importance of not just being a data scientist but understanding business aspects to create value from data. He discusses the skills needed for data scientists, such as coding proficiency, deep knowledge in statistics and mathematics, and the ability to understand and analyze business models. The speaker also addresses the high demand for data scientists in the job market and encourages continuous learning and skill development in data science.

05:01

🔍 Data Analytics and the Role of Data Scientists

The script delves into the specifics of data analytics, with a focus on the role of data scientists in extracting insights from data to aid business decision-making. It touches on the necessity for data scientists to be agile in their approach, to quickly analyze data, and to derive meaningful conclusions. The speaker highlights the importance of understanding business needs and being able to communicate these insights effectively. There's also a discussion about the versatility of data scientists, suggesting that they can come from various educational backgrounds and that their skills can be applied across different industries.

10:02

💼 The Evolution of Data Engineers and Their Impact

This section discusses the evolution of the role of data engineers and how they differ from traditional software engineers. Data engineers are described as professionals who not only understand computer systems and software but also have a deep understanding of data and its applications. The speaker emphasizes the importance of data engineers in managing and organizing data to make it accessible and useful for others. There's also a mention of the growing demand and higher salaries for data engineers due to their specialized skills and the increasing importance of data in business operations.

Mindmap

Keywords

💡Data Science

Data Science refers to the field of study concerned with the processes and systems to extract knowledge and insights from data in various forms, either structured or unstructured. In the video, the speaker emphasizes the importance of data scientists who can analyze data from different perspectives to create new models for better decision-making within organizations. The script mentions that data scientists should not just be technically proficient but also understand the business context to create meaningful models.

💡Data Analysis

Data Analysis is the process of inspecting, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. The video script discusses how data analysis is crucial for businesses to understand patterns and trends. It is highlighted that data analysts must manage data, analyze it, and understand its potential benefits for the business, such as predicting sales or customer behavior.

💡Data Engineer

A Data Engineer is a professional who specializes in designing and building systems for creating, deploying, and managing data pipelines. In the script, the role of a Data Engineer is mentioned alongside Data Scientist, emphasizing their focus on the infrastructure that allows data to be collected, stored, and analyzed. The video suggests that Data Engineers work closely with data scientists to ensure that data is readily available and in the correct format for analysis.

💡Machine Learning

Machine Learning is a subset of artificial intelligence that gives systems the ability to learn and improve from experience without being explicitly programmed. The script touches on machine learning models and the need for professionals who can build and refine these models to predict outcomes and make decisions based on data. The speaker mentions that these models are not 100% accurate, hence the need for continuous evaluation and adjustment.

💡Business Understanding

Business Understanding is the ability to comprehend the commercial aspects of a company, including its operations, competition, and market dynamics. The video script stresses that a deep understanding of the business is essential for data professionals to create models that align with the company's objectives. It is noted that technical skills alone are not sufficient; professionals must also interpret data in a way that adds value to the business strategy.

💡Data Modeling

Data Modeling is the process of creating a simplified, abstract representation of reality for a database or information system. In the context of the video, data modeling is discussed as a critical skill for professionals who need to structure data in a way that supports analysis and decision-making. The script gives examples of creating models to predict sales or customer behavior, which are then used to drive business strategies.

💡Statistical Knowledge

Statistical Knowledge involves understanding concepts such as probability, inference, and hypothesis testing, which are essential for analyzing data and drawing valid conclusions. The video mentions the importance of having a strong foundation in statistics for data professionals to analyze complex data sets and build accurate predictive models. It is highlighted as a key differentiator between entry-level and advanced data science roles.

💡Python

Python is a high-level programming language widely used in data science due to its readability and the vast array of libraries available for data manipulation and analysis. The script mentions Python as a primary language for data-related tasks, including building machine learning models and automating data pipelines. The speaker suggests that while Python is popular, professionals should not limit themselves to just one language and should be adaptable to new technologies.

💡Data Pipeline

A Data Pipeline is a series of processes or steps involved in moving data from its source to its destination, where it can be stored, processed, and analyzed. In the video, the concept of a data pipeline is mentioned in relation to the work of a Data Engineer, who is responsible for creating systems that can handle the extraction, transformation, and loading of data for analysis.

💡Career Transition

Career Transition refers to the process of moving from one job or career to another. The script discusses the possibility of transitioning into data-related fields, suggesting that individuals from various backgrounds, such as marketing or social sciences, can augment their skills to work with data. The video encourages continuous learning and practical application of skills to prepare for a career in data science.

💡Data Analyst

A Data Analyst is a professional who collects, processes, and performs statistical analyses of data to help organizations make informed decisions. The video script positions the Data Analyst role as one that is in high demand, with companies seeking individuals who can interpret data to guide business strategies and operations. The speaker provides insights into the skills and knowledge required for this role, such as proficiency in statistical analysis and data visualization.

Highlights

Emphasizes the importance of organizations being data-driven and the role of data scientists.

Mentions the potential class divide within the field, but expresses a desire not to categorize people by class.

Introduces himself as a manager at a studio, previously known for his work at Delta Side on Facebook.

Discusses the current focus on teaching and improving data skills.

Addresses the most Googled questions about data science and engineering.

Explains the concept of data science direction and its varying definitions across companies.

Describes the typical skills required for a data scientist, including coding proficiency and deep knowledge of statistics and mathematics.

Stresses the importance of understanding business to create business-oriented models.

Outlines the data science process from data collection to creating and evaluating models.

Talks about the abundance of job opportunities for data scientists in large companies.

Advises on the necessity of coding skills and the importance of being adaptable in the field.

Recommends learning the basics of coding, statistics, and mathematics to excel in data science.

Discusses the best learning process, emphasizing practical application and continuous improvement.

Talks about the challenges of understanding business nature and how it affects data science applications.

Mentions the importance of defining data science and analytics terms accurately across different contexts.

Describes the role of a data engineer and the skills required, such as database management and system architecture.

Highlights the significance of data integration and the challenges of working with disparate data sources.

Discusses the importance of data scientists understanding the business side to create actionable insights.

Talks about the increasing demand for data engineers and the high salaries they command due to the importance of their role.

Mentions the importance of continuous learning and adapting to new technologies and languages in the field of data.

Concludes by inviting questions and further discussion on the topic, encouraging engagement with the audience.