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

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(x0,xi)=1N∑k=1Nr(x0(k),xi(k))

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

(1) Normality: (4) If present 0<r(x0,xi)≤1∀k (5) Then there are r(x0,xi)=1⇔x0=xi

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.

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) rM(x,y)=ρsupz∈M∪{y}ξ(x,z)ξ(x,y)+ρsupz∈M∪{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:

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.

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.

Figure 1.

Analysis of the cultivation satisfaction of digital personnel

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.

Figure 2.

Digital talent training process 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.

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.

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.

Figure 3.

The mechanism of dimetagenesis

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.

Figure 4.

The talent training mode adjustment results

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.

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.