Research on the Construction of the Path of Cultivating Practical Ability of Digital Talents in New Business Disciplines Driven by Big Data

Along with the prosperous development of the digital economy, the enterprise digital upgrading and transformation is accelerated, and the digital upgrading and transformation needs to be supported by digital talents. The twentieth report clearly puts forward to “promote the digitalization of education”, the cultivation of high-level digital talents has been urgent, it has become the core competitiveness of national innovation and development, enterprise upgrading and transformation [1-2]. Especially with the ability to innovate composite and application-oriented talent has become a key factor in the development of emerging digital intelligence industry [3]. In order to effectively cope with the talent shortage problem and the prominent structural employment contradiction faced by the development of digital economy, institutions of higher education, as the most important main position for cultivating digital talents, must make efforts to promote the integration of industry and education, continuously improve the ability of teachers to utilize digital technology, and strengthen the innovation and entrepreneurship of college students, so as to produce a strong support for the development of digital industry [4-8]. Digitalization has brought a once-in-a-lifetime opportunity for education development, which will promote the construction of the new business major talent training mode of industry-education integration and accelerate the implementation of the high-quality development of applied undergraduate majors and the cultivation of digital talents [9-11].

The new business discipline is to introduce new technology, new ideas and new methods into the discipline on the basis of the traditional business discipline in order to cope with the new challenges in society and economy [12-13]. At present, the cultivation of digital management talents for the economic and management majors in colleges and universities is still in the initial exploration stage, and the main cultivation mode still remains in the stage of the traditional business discipline cultivation mode, which is unable to adapt to the needs of the new era of the new business discipline digital talent cultivation [14-16]. Therefore, the cultivation of new business digital management talents in colleges and universities urgently needs transformation and innovation.

Literature [17] shows that the development of enterprises in the context of the digital economy requires students to have rich knowledge of economics and business administration, while mastering the practical operation and flexible communication skills, therefore, the development of the practical ability of new business talents is of great significance. Literature [18] constructed a model of “new economy” science practice teaching system based on modern information technology, which contains three dimensions: in-class professional cognitive practice, industry comprehensive practice, and extracurricular social practice and innovation, which can help colleges and universities to cultivate management talents with practical ability for social enterprises. Literature [19] discusses the key elements of the combination of information technology and economic management courses in theoretical teaching and practical teaching, aiming to improve the practical ability, application ability, autonomy and innovation ability of economic and management talents, and to promote the further development of business administration. Literature [20] points out that data is the most important factor of production at present, analyzes the transformation of business administration talent cultivation mode in the era of big data from the perspective of supply and demand, and studies the reform path of colleges and universities adapting to the development of the digital economy and society from the perspective of the integration of industry and education talent cultivation mode. Literature [21] carries out informatization teaching reform of accounting majors in colleges and universities from the aspects of curriculum system, cultivation objectives, faculty strength, access to resources and teaching content, which helps to cultivate digital and informatization transformation accounting professionals. Literature [22] puts forward the idea of distributable classroom teaching based on information technology, carries out the distributable practical teaching of financial management teaching, and effectively solves the problems such as technical standard misunderstanding, functional misalignment misunderstanding and subject alienation hazards that exist in the teaching of financial management courses. Literature [23] constructed the business administration professional practice teaching platform in the information age, which can keep up with the changes in the informationized social environment and enterprise talent demand, supplement new knowledge for the business administration profession in a timely manner, and improve the competitiveness of business administration professionals in their job search to a large extent.

This paper conducted a questionnaire survey on the current situation of digital talent cultivation of new business disciplines in a university, and summarized the problems and causes summarized in the survey results, and analyzed them in the light of the current problems of digital talent cultivation in colleges and universities. Based on this, using big data-driven correlation analysis technology, based on the enrollment of the school in previous years and student employment, the main analysis of the correlation index between social demand and digital talent cultivation. We get the conclusion that the current cultivation path blindly emphasizes theory and neglects practical ability, so this paper constructs a “dualistic” digital talent practical ability cultivation path. To achieve a more effective talent cultivation method.

2

Data-driven theorizing and new business contexts

Alana sees data-driven as being based on information technology, which in turn leads to continuous collection, continuous algorithmic evaluation, and potentially unlimited record retention of learning data. Based on the above understanding of data-driven, this paper argues that data-driven research is based on data, which is a way to conduct scientific research by collecting, categorizing, storing, computing, analyzing, and verifying data. Different from qualitative research, data-driven through a variety of data as a support to reflect the social phenomenon or project the results of the problem, due to the strict logic and reliability, the conclusion is often very accurate and reliable.

In the era of technological development and informationalization, the use of big data technology is the foundation and prerequisite for ensuring effective teaching and improving the quality of education. In the early stage of data-driven teaching activities, the following basic elements are an important guarantee of data-driven teaching.

First, the data composition is diversified. Multi-dimensional data sources such as students’ gender, grade, class, learning resources, changes in learning performance, learning timeliness, etc. will help to comprehensively analyze and grasp the learning situation of students, and provide a scientific basis for the subsequent adoption of targeted teaching.

Second, establish a multi-level database. The database is used to store memory and analyze and interpret various types of data. As the number of data collection is very large, the construction of a scientific and reasonable multi-level database will be conducive to the rapid extraction, analysis, use and update of data, which greatly improves the efficiency of data utilization.

Third, implantation of data analysis tools. Huge data is difficult to quickly collect and analyze artificially, so it is very convenient and reasonable to implant relevant data analysis tools in advance, and use the preset data analysis model or data export format to output the required data from the multi-level database with reasonable structure.

With the introduction of the concept of new business studies, market-oriented cross-cultivation of business talents focusing on interdisciplinarity and inter-disciplinarity has gradually been emphasized by all sectors of society. In this context, the training of new business talents should focus on information intelligence technology as a means to respond to the rapid change of the business model in the new era with the mode of “management + technology” integration, in order to cultivate composite talents who can apply big data technology to mine business value information in the context of the digital economy. According to the cultivation objectives of new business talents, enterprise big data analysis demand, the actual ability of talent cultivation, and big data technology algorithms and resources, it is necessary to give a multi-dimensional analysis of the various paths to improve the big data analysis ability of new business talents, in order to explore a practical cultivation path.

3

Research on the construction of a training system driven by big data

3.1

Gray correlation calculation steps

With the rapid development of big data and artificial intelligence technology, the role of data in educational decision-making has become increasingly prominent. According to the educational data, the correlation relationship between the talent education and training programs and the actual career needs is obtained, so as to construct the path of cultivating the practical ability of digital talents in the new business discipline.

Gray correlation analysis is a factor analysis method that seeks the correlation between things with the guidance of the systematic idea of overall correlation and the basic feature of multi-angle thinking, which can provide an important technical analysis means for the modeling of complex systems, and its basic principle is to distinguish the degree of correlation between multiple factors in the system by comparing the geometrical relationship of the statistical sequences, and the closer the geometrical shapes of the sequential curves, the greater is the correlation degree between them. The closer the geometric shapes of the series curves are, the greater the correlation between them.

X₀ = (x₀(1),x₀(2),⋯,x₀(n)) is the sequence of system characteristics, i.e., the reference sequence.

X_i = (x_i(1),x_i(2),⋯,x_i(n)), (i = 1,⋯ m,m < 2) are sequences of correlation factors, i.e., comparison sequences;

There are Eqs: (1) $r (x_{0} (k), x_{i} (k)) = \frac{\min_{i} \min_{k} | x_{0} (k) - x_{i} (k) | + ξ \cdot \max_{i} \max_{k} | x_{0} (k) - x_{i} (k) |}{| x_{0} (k) - x_{i} (k) | + ξ \cdot \max_{i} \max_{k} | x_{0} (k) - x_{i} (k) |}$

Mark r(x₀(k),x_i(k)) as (k : (2) $r (X_{0}, X_{i}) = \frac{1}{n} \sum_{k = 1}^{n} r (x_{0} (k), x_{i} (k)) = \frac{1}{n} \sum_{k = 1}^{n} r_{0 i} (k)$

$r (X_{0}, X_{i}) = \frac{1}{n} \sum_{k = 1}^{n} r (x_{0} (k), x_{i} (k))$ satisfies the gray correlation public update, where ξ ∈ (0,1), called the discrimination coefficient, ξ the smaller, the greater the difference between the correlation coefficients, the stronger the ability to distinguish. In this paper, ξ is taken as 0.5. r(X₀,X_i) is called the gray correlation degree of X_i to X₀ and is denoted as r_0i.

Geometrically, the gray correlation is the degree of geometric similarity between the sequence curves, the closer the geometric shape, the greater the correlation.

3.2

Some properties of gray correlation analysis

1)

The four axioms of gray correlation analysis

Let X be the set of gray correlation factors, x₀ ∈ X be the parent factor, i.e., the reference sequence. x_i ∈ X be the sub-factor, i.e., the comparison sequence, and x₀(k),x_i(k) be the number of x₀ and x_i at point k, respectively, if r(x₀(k),x_i(k)) is a real number, to obtain the mean value of r(x₀(k),x_i(k)) as: (3) $r (x_{0}, x_{i}) = \frac{1}{N} \sum_{k = 1}^{N} r (x_{0} (k), x_{i} (k))$

The four axioms of gray correlation analysis means that equation (3) satisfies the following four cases:

(1) Normality: (4) $If present 0 < r (x_{0}, x_{i}) \leq 1 \forall k$ (5) $Then there are r (x_{0}, x_{i}) = 1 \Leftrightarrow x_{0} = x_{i}$

r(x₀,x_i) = 0 ⇔ x₀, x_i ∈ Φ, Φ is the empty set.

Normality shows that no factor in the system can be strictly uncorrelated.

(2) Even symmetry

If there exists x₀,x_i ∈ X: (6) $r (x_{0}, x_{i}) = r (x_{i}, x_{0}) \Leftrightarrow X = {x_{σ} | σ = 0, i}$

This suggests that in the set of gray correlation factors, if there are only two factors, r(x₀,x_i) is a two-by-two comparison, and two-by-two comparisons are symmetric.

(3) Holistic: (7) $r (x_{j}, x_{i}) \underline{\underline{a l w a y s r}} (x_{i}, x_{j}), x_{j}, x_{i} \in X \Leftrightarrow X = {x_{σ} | σ = 0, 1, \dots, n}$

This property is due to different circumstances and different trade-offs in the reference coefficients, and the results of the comparison will not necessarily conform to the symmetry principle.

(4) Proximity

The smaller |x₀(k)–x_i(k)| is, the larger r(x₀(k),x_i(k)) will be.

r(x₀,x_i) is known as the correlation coefficient of x₁ to x₀, while x₁ to x₀ is the correlation coefficient of x₁ to x₀, denoted as ξ_0i(k).

2)

Properties of gray correlation

Gray correlation is a measure of the proximity of a discrete function, and gray correlation analysis is used to analyze the degree of correlation between the factors of the eigen-system. It is worth noting that the gray correlation mapping in the gray correlation space is not unique, and even the object of a specific correlation mapping in the gray correlation space can only be obtained uniquely after a determined pure quantization process with a determined discrimination coefficient ρ. Therefore, the degree of association between the factors is mainly described in terms of the order of magnitude of the gray correlation, rather than exclusively in terms of the magnitude of the gray correlation.

A metric space (R,ξ) is provided and M is a bounded set of R.

Eq: (8) $r_{M} (x, y) = \frac{ρ \sup_{z \in M \cup {y}} ξ (x, z)}{ξ (x, y) + ρ \sup_{z \in M \cup {y}} ξ (x, z)}$

0 < ρ ≤ 1 , where the constant maturity ρ is known as the discriminant coefficient, and (R,ξ,r_M) constitutes a gray correlation space. The gray correlations defined here are full of the four axioms of gray correlation analysis, and also contain the correlations of various types of continuous and discrete functions. The bounded set M is called the expanded factor set of gray correlation analysis, or expanded factor set for short. If the values of the discrimination factor ρ and the distance ξ are determined, the gray correlation r_M(x,y) is generally not related to the reference element x and the comparison element y, but to the bounded set M.

Gray correlations have the following properties regarding the expanded factor set for gray correlations:

1)

Monotonically increasing

r_M(x,y) is monotonically increasing with respect to the set of expanding factors M. It is possible to set M and N as the two sets of expanding factors of R. If M ⊂ N, then we have r_M(x,y)<r_N(x,y), ∀x,y ∈ R.

2)

Boundedness

If r_M(x,y) is bounded with respect to the set of expanded factors M, then there is: (9) $ρ / (1 + ρ) < r_{M} (x, y) \leq 1, \forall M \subset R, \forall x, y \in R$

3)

Order preserving

Assuming M and N are two expanded factor sets, if factor set X = {x₁,x₂,⋯,x_m} is also a subset of M and N, and 0<r_M(x₀,x₁)≤r_M(x₀,x₂)≤⋯≤r_M(x₀,x_m)≤1 is known, it is bound to be: (10) $0 < r_{N} (x_{0}, x_{1}) \leq r_{N} (x_{0}, x_{2}) \leq \dots \leq r_{N} (x_{0}, x_{m}) \leq 1$

The correlation r_M(x,y) defined here is order-preserving with respect to the expanded factor set M. In correlation analysis, the decisive role is often not the size of the correlation, but the ordering between the correlations. In practice, this order-preserving property of correlation degree can be utilized to expand the factor set appropriately while adjusting the resolution factor ρ, so that the gray correlation degree can have strong clarity.

4

Research on the construction of digital talent training paths

4.1

Survey of Problems in Digital Talent Development

4.1.1

Analysis of investigators

To ensure that the questionnaire content is of reference value, it mainly covers the new digital business talents of a university, including leaders, professionals, and junior talents. The questionnaire was distributed and recovered using the questionnaire tool in line, and finally 272 valid questionnaires were obtained. On the whole, the respondents of a university were able to give truthful feedback according to their own situation, and the overall situation of the questionnaire survey was relatively true, rational, and objective. According to the statistical results of the data in this study, as shown in Table 1, the main force of digital talents is dominated by men, occupying 58.09% of the total number, while women have a proportion of 41.91%. All the individuals included in the study have a bachelor's degree or a higher degree, so only this part of the population has been collected and organized data, the proportion of which is 36.76% and 63.24%, respectively. For the professional division of digital talents, 3.04% of them were considered leadership talents. Digital leaders, another 51.10% were professional technicians, known as digital professionals, and the remaining 46.32% were newcomers to the job market, i.e. digital junior talents. Analyzed with the help of the manpower system, according to the categories of business lines served by these personnel, retail and risk lines account for a relatively large proportion, 24.63% and 25.74% respectively.

Table 1.

The basic situation of the investigators

	Classification	Number	Percentage
Gender	Male	158	58.09%
Gender	Female	114	41.91%
Educational background	Undergraduate	100	36.76%
Educational background	Graduate student	172	63.24%
Hierarchy	Digital personnel	7	2.57%
	Digital professionals	139	51.10%
	Digital junior talent	126	46.32%
Business line	Management line	50	18.38%
	Company line	58	21.32%
	Retail line	67	24.63%
	Risk line	70	25.74%
	Accounting line	27	9.93%

4.1.2

Satisfaction with digital talent development

Satisfaction with digital talent cultivation is shown in Figure 1, with dimensions 1~7: overall satisfaction with cultivation work, cultivation is systematic, cultivation goal is clear program, design is scientific and reasonable, cultivation planning is executed in a standardized way, evaluation mechanism is scientific and effective, and curriculum and faculty resources are of high quality. Respondents are less satisfied with the current digital talent cultivation work in the bank, with only 13.42% of the respondents being satisfied. 89.2% of the surveyed personnel think that the current digital talent cultivation is not systematic, of which 78.5% think that they are generally or unsatisfied with the clear cultivation objectives, 79.1% think that they are generally or unsatisfied with the scientific reasonableness of the program development, 71.9% think that they are generally or unsatisfied with the standardization of the implementation of cultivation planning, and 53.3% of the surveyed personnel are generally or unsatisfied with the evaluation mechanism, and 70.52% of the surveyed personnel are generally or unsatisfied with the quality of the curriculum and faculty resources. Taken as a whole, the overall satisfaction of the surveyed personnel with the digital talent cultivation work of a university is low, which is mainly manifested in the lack of systematic cultivation work, unclear cultivation objectives, irregular program design and implementation process, and the quality of courses and faculty members need to be further improved.

The analysis of digital talent cultivation process is shown in Figure 2, dimensions 1~7 cultivation objectives to meet knowledge needs, cultivation objectives to meet the needs of work practice, the overall program content is perfect, the form is rich and varied, focus on practical operation, focus on theoretical learning, and the evaluation mechanism is real and objective. From the viewpoint of goal setting, in whether the digital talent cultivation goal can meet the knowledge needs, more than half of the people think that the digital talent cultivation can meet the basic theoretical knowledge needs in daily work. However, in the survey of digital talent training objectives to meet the practical needs of work, 8.23%, 24.56%, and 33.34% of the personnel believe that they can basically meet the practical needs. Although most of the digital talent training in a university is based on the theoretical knowledge needed for work and practical needs, its positioning of talent training objectives is not clear, which also reflects the lack of understanding of the digital talent training system in a university from another angle. More attention should be paid to course content and teacher quality in the allocation of resources for digital talent training.

4.2

Establishment of sound digital talent cultivation path construction in business studies

4.2.1

Design of talent training model based on big data analysis

The first step in designing a new education model for talent training needs to obtain indicators related to social demand and talent training. The current stage of education development is carried out with the total course of national education development, and at the same time, with the direction of the development of social structure, colleges and universities have carried out innovations and reforms of talent training mode. Specific information is shown in Table 2. Nowadays, in terms of the education model, the school has expanded by 45% on the basis of the original number of students enrolled in the school, which enhances the proportion of general undergraduate students and increases the number of talents employed. However, in terms of the number of employed people, the employment rate of science majors is about 1.5 times higher than that of business talents.

Table 2.

The basic information of students' employment in a certain year

Student information	Quotient class	Science
Enrollment quantity	2790648	3589356
Expected graduate	2756284	3583472
Real graduate	2786539	3574826
Employment student	1922873	3105138
Job alignment	766953	840286
Student population	1150920	1360887

According to the above results, it can be seen that the current increase in the number of students receiving higher education has led to an increase in the number of graduates employed and solved the current situation of the shortage of social demand for talents. Therefore, based on the indicators of big data analysis, the structure of students' professional courses is analyzed, and the results of the analysis are shown in Table 3. The proportion of practice in general education elective courses in the module is 0. It can be seen that the training and education courses have problems such as emphasizing theory but not practice, and the lack of characteristics in the development of institutions, resulting in the weak comprehensive quality of students, which makes it difficult for the teaching system to meet the current stage of the social production needs, thus making the students' employment unsuitable for the target, which leads to the problem of poor employment rate.

Table 3.

Education training course structure table

Classroom instruction	Total amount	Practice time	Practice accounts for the module (%)	The module accounts for the proportion of the course (%)
General education	492	140	28.56	20.35
General education elective course	54	2	0	2.02
Subject required courses	586	130	22.4	23.20
Subject course	100	28	16.8	3.65
Compulsory course	816	138	24.02	31.28
Elective course	462	110	21.56	19.5

Obtaining the indicators of students' learning commitment to reconstruct the teaching system based on the above data to obtain the relevant indicators, integrate the education and training programs with the actual occupation, construct a “dual” education mechanism, set up new teaching and training programs according to the actual occupational needs, and improve the integration between teaching and employment. At the same time, schools and enterprises need to strengthen communication and joint efforts to carry out new types of education and training programs, so that talent training and enterprise demand match. In addition, it is necessary to enhance the social competitiveness of both schools and enterprises, enterprises provide students with practical training bases to enhance the practical ability of students; the school is based on the results of practical training, the students' deficiencies in the second theoretical training, to realize the cooperation between the classroom and the practice of both sides. The manifestation of this mechanism is shown in Figure 3.

In accordance with the above adjusted structure to reformulate the talent training model, the results are shown in Figure 4, there are 8 aspects of teaching activities that need to be adjusted to ensure that there is a match between the expertise of the talents and the technology required by society, and to improve the employment rate of the students and realize the synchronous development of the society and the talents by means of the targeted talent training model.

4.2.2

Systematic curriculum

The stable development of society requires a large number of high-quality and high-quality talents, which includes not only professional and technical talents, but also high-quality and highly educated talents. What modern enterprises need is to master a certain degree of new business expertise, skilled in the use of advanced digital financial software professional skills. In response to the demand for students to improve their academic qualifications, the new business majors of higher education institutions are also constantly reforming and innovating, so secondary schools also need to follow the footsteps of the times and constantly innovate, only in this way can we improve the knowledge level of students and docking institutions of higher education. Based on the stakeholder theory, the training objectives of new business majors need to pay attention to the needs of society, enterprises, schools, students and other aspects, continue to improve the education and teaching system, improve the quality of teaching, strengthen the students' understanding of professional knowledge and skills, enhance the students' digital ability in accounting, ensure the all-round development of students, and further enhance the quality of students' own quality and competitiveness in society.

The systematic curriculum system, as shown in Table 4, is the original curriculum and teaching arrangement of the new business major, in which public foundation courses and professional skills courses are set up, but a systematic curriculum system is not formed. In order to further improve the quality of digital talent cultivation for new business majors, universities need to reform the curriculum system of new business majors with the orientation of enterprise job requirements in the digital era and the goal of enhancing students' digital competence and social competitiveness. Schools need to conduct in-depth research on the needs of enterprises, understand the development prospects of new business talents in the digital era, and ensure that the courses offered by the school are in line with the development of the digital era and the employment needs of enterprises.

Table 4.

The original course set and the teaching schedule

Course name	Total credit	Total time	Semester
Course name	Total credit	Total time	1	2	3	4	5	6
Chinese character	2	40	3
Mental health and career	3	40		3
Philosophy and life	2	40			3
Occupational ethics and rule of law	3	40				3
Sports and health	8	145	3	2	2	3
Computer foundation	9	112	7▲
Chinese	8	145		4		4▲
English	7	145	4▲	5▲
mathematics	2	145	5		5
Public art	2	40				4
History	3	40			3
Basic accounting	9	145	10▲
Corporate accounting practice	14	220		7▲	7▲
Accounting computerization	7	112		7▲
Accounting practice	7	112			5▲	3
Cost accounting practice	5	80			5▲
Tax accounting	5	80				5▲
Tax basic knowledge	2	40			3
Financial regulations	5	40		2
Commodity purchase and marketing accounting practice	5	80				5▲
Basic knowledge of economic law	5	80				5
The practice of financial clerk	3	40				3
Military training, admission education	1	32	✓
Moral education practice week	3	64	One week	One week
True account	2	83	One week			Two week
“Real account” training	5	108			Two week	Two week
Enterprise operating sand-plate mould	2	28			one week
Internship	29	612					✓
Overhead internship	30	612						✓
Class meeting	9	148	3	3	2	3

First of all, according to the quality requirements and literacy requirements of the society for talents, set up comprehensive basic courses to help students accumulate scientific and cultural foundation, improve humanistic comprehensive literacy, develop good learning habits, lay a good foundation for the society to cultivate high-quality talents with all-round development of morality, intelligence, physical fitness, aesthetics and labor, docking with the cultivation objectives of higher education institutions to meet the needs of secondary school students to receive higher education, and to meet the learning needs of students with different endowment and potential, and provide diversified growth and development opportunities. It also meets the learning needs of students with different endowments and potentials, and provides diversified space for them to grow and become successful.

Secondly, the professional skills courses (professional core courses, professional skills direction courses, professional practical training courses, off-campus practical training) are set up in combination with the employment specifications of the enterprise positions in the digital era, and the courses are improved in combination with the employment specifications of the enterprises to promote the integration of the professional courses with digitalization, to ensure that the professional construction can be adapted to the needs of the transformation of the enterprise’s finance department in the digital era, to help students improve their social competitiveness and further increase the employment rate.

Thirdly, in order to meet the talent needs of the transformation of the enterprise sector and avoid homogenization of training objectives, different elective courses are set up in the direction of financial data analysis, financial management, and resource integration, etc., so as to meet the employment needs of the enterprise positions in the digital era. Finally, make full use of various ways, such as skill competitions and school-enterprise cooperation, to further enhance the efficiency of internships and practical training, to help students consolidate the professional knowledge they have learned, to enhance the training of professional skills, and to improve students' professional ability.

5

Conclusion

This paper analyzes the problems of digital talent cultivation through the questionnaire, and then focuses on the correlation between the types of talents needed by the society and the mode of cultivating talents in schools through the gray correlation technology at this stage, and adjusts the path of digital talent cultivation in a timely manner, so as to realize the efficient development of the practical ability of digital talents in the new business discipline. The conclusion of the study shows that most of the interviewed personnel have low satisfaction with the current digital talent training, and the positioning of talent training goals is unclear. Based on the correlation analysis of college enrollment and social demand, we adjusted eight teaching activities and constructed a “dual” talent cultivation path by linking digital talent cultivation with actual occupational demand. Through this series of initiatives, a reliable cultivation path is gradually established, and a solid foundation is laid for the development of practical ability of digital talents.

Funding:

This research was supported by the Research Plan for Undergraduate Teaching Quality and Teaching Reform in Tianjin Regular Institution of Higher Education: “Exploration and Practice of Cultivating Digital - application Talents in New Business Disciplines from the Perspective of Industry - education Integration” (Project Number: A231006901).

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Research on the Construction of the Path of Cultivating Practical Ability of Digital Talents in New Business Disciplines Driven by Big Data

Zhuo Guo

Published Online: Mar 21, 2025

Received: Oct 18, 2024

Accepted: Feb 02, 2025

DOI: https://doi.org/10.2478/amns-2025-0657

KeywordsData-driven, Correlation analysis, “Dualization”, Cultivation pathway

© 2025 Zhuo Guo, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Data-driven, Correlation analysis, “Dualization”, Cultivation pathway