The CDA Data Science Research Institute recently successfully held an expert seminar for the first quarter of the CDA certification examination. Mr. Wang An, Chief Data Officer of the Data Element Price Formation and Recognition Innovation Laboratory, Mr. Chen Yuanxiang of Beijing University of Posts and Telecommunications, Mr. Li Qi of Hexi Information Technology, and Dr. Zhao Jianyi and Mr. Xu Yang of the CDA Data Science Research Institute were invited to participate in this seminar.

At the seminar, experts combined their rich experience to put forward unique insights on the outline, textbooks, question bank and curriculum standards of the CDA certification examination. They emphasized the positioning of data work of enterprises in the business chain, including upstream and downstream collaboration, and especially pointed out the importance of combining business scenarios and data analysis. In addition, experts also put forward the training needs of leadership and synergy to help students realize their professional value.

Application and challenges of AI in data-related work

At the seminar, experts discussed in depth the application of AI in data-related work and the challenges it faces. AI technology has greatly improved the efficiency of data analysis, but it has also brought some problems, such as unstable results, the gap between theory and operation, and the expectations of enterprises for zero-based employee training. In order to meet these challenges, the teaching content needs to be closely related to actual business needs, so that students can better understand and apply the knowledge they have learned. By introducing specific cases, we can help candidates apply the knowledge they have learned to solve practical business problems in exams and actual work. Application scenarios such as recommendation system and document retrieval enhancement are gradually maturing, and data transactions and privacy protection have become the focus of the future, which have been integrated into the standard design of the curriculum.

New version of textbooks and career development of data analysts
Experts discussed in detail about the arrangement of the new version of the textbook and its application in data analysis education. The new version of the textbook separates theoretical knowledge from practical operation, which is convenient for the updating of content and the flexible use of learners. The discussion further delves into the actual working scenarios of data analysis, especially in the data pricing of some projects, emphasizing the importance of value orientation and topic selection of data analysis. In addition, it also mentioned the challenges faced by data analysts in their work, such as how to effectively combine data with decision-making scenarios, and the differences in skills required at different professional levels. Experts agree that when training data analysts, attention should be paid to the close combination of technology and business to achieve higher professional value.

Cross-integration of deep learning and knowledge atlas in scientific research and application
Experts have had an in-depth discussion on the integration of deep learning and knowledge atlas in scientific research and practical application. The discussion covers the application of deep learning in the social field, the application of knowledge atlas in social network relationship reasoning, recommendation system and other fields. At the same time, the application of federal learning in data transactions and the different methods of enterprises using large models for training are also discussed, including the use of private data to train large models, and the construction of payment knowledge atlases to enhance retrieval and solve the separation between documents. In addition, it also mentions the prospect of wide application of the future model in enterprises, as well as the requirements for the understanding and participation of participants.

The close combination of data strategy and business analysis module
Two new core modules have been added to this course: one is the basic concept of data, which accounts for a small proportion, but covers important thinking aspects; the other focuses on strategic and business data analysis, mainly starting from the final output and pushing back the required steps and resources. This module includes data requirements, analysis steps and how to produce analysis reports or charts, etc. It is especially pointed out that strategic data analysis and business data analysis are different in terms of operational ideas. For different application scenarios, the course provides detailed explanation and distinction. In addition, the data analysis method partially integrates common business models, making the analysis method more universal in the industry.

Strengthening data ethics and compliance
At this seminar, experts also emphasized the importance of data ethics and compliance. With the rapid development of data science, data privacy and security issues are becoming more and more prominent. Experts pointed out that data analysts not only need to master technical skills, but also need to have a strong sense of data ethics. Chapters on data ethics and compliance have been added to the new version of the textbook, covering data protection laws and regulations, privacy policies and ethical norms for data use. Through the study of these contents, students can better understand how to use data legally and compliantly in actual work and avoid legal risks caused by improper operation.

Project practice and comprehensive ability training

Experts agree that project practice is the key link to improve students' comprehensive ability. Therefore, the new version of the textbook and curriculum design emphasizes the importance of project practice. After each module, relevant practical projects are set up, requiring students to apply the knowledge they have learned to solve practical problems. These projects not only cover all aspects of data analysis, but also data collection, cleaning, modeling, visualization and report writing. Through the training of these practical projects, students can exercise their comprehensive ability in real business scenarios and lay a solid foundation for their future career development.

The expert seminar in the first quarter of the CDA certification examination was successfully held. The experts had in-depth discussions on many aspects of data science education and put forward many valuable suggestions. By continuously optimizing the content of the examination outline and textbooks, increasing the practical links of the project and strengthening the cooperation of enterprises. In the future, CDA will continue to pay attention to the latest developments in the field of data science, and constantly update and improve the content of the examination outline and curriculum standards to meet the needs of enterprises and the market, and promote the popularization and development of digital talent standards.