Innovation and entrepreneurship education service system based on data analysis

Abstract

The invention relates to the technical field of education, and discloses an innovation and entrepreneurship education service system based on data analysis, and the system comprises a data collection module which is used for collecting various data related to innovation and entrepreneurship education, including but not limited to student information, course data, project cases and market trends; the data cleaning and preprocessing module is used for cleaning, denoising and formatting the collected data so as to ensure the data quality; the data analysis module is used for carrying out statistical analysis, machine learning and pattern recognition on the preprocessed data to extract valuable information and trend, and the result display module is used for displaying an analysis result to a user in the forms of a chart, a report and an instrument board. Through the personalized recommendation module, customized education content and resources can be provided based on historical behaviors and preferences of the user. According to the characteristic, accurate course recommendation, learning resources and the like can be provided according to learning requirements and interests of different users, so that the learning effect and satisfaction of the users are improved.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to an innovative and entrepreneurial education service system for data analysis. Background Art

[0002] At present, with the rapid development of information technology and big data, the field of education is gradually innovating and optimizing with the help of advanced technical means. Especially in innovation and entrepreneurship education, how to provide efficient and accurate teaching services has become an urgent problem to be solved. Most of the existing innovation and entrepreneurship education systems rely on traditional course arrangements and teaching models, which cannot fully take into account the differences in individual students' interests, learning progress and needs, resulting in insufficient personalization of educational content recommendations and difficulty in maximizing learning effects and practical abilities. In addition, the educational data analysis functions in most existing systems are relatively basic, and usually can only simply summarize and count students' basic information. There is a lack of in-depth data mining and intelligent prediction, which makes it difficult to provide comprehensive support and guidance for education decision makers, especially in terms of curriculum design, learning resource allocation, and student ability assessment.

[0003] Therefore, a solution is proposed. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an innovation and entrepreneurship education service system for data analysis, which solves the problems raised by the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data analysis innovation and entrepreneurship education service system, comprising:

[0006] Data collection module, used to collect various data related to innovation and entrepreneurship education, including but not limited to student information, course data, project cases and market trends;

[0007] Data cleaning and preprocessing module, used to clean, denoise and format the collected data to ensure data quality;

[0008] Data analysis module, which is used to perform statistical analysis, machine learning and pattern recognition on pre-processed data to extract valuable information and trends;

[0009] The result display module is used to display the analysis results to users in the form of charts, reports and dashboards;

[0010] Prediction model building module, used to build a prediction model based on historical data to predict the future trend of innovation and entrepreneurship education;

[0011] Personalized recommendation module, used to provide personalized educational content and resource recommendations based on the user's historical behavior and preferences;

[0012] Interactive learning modules are used to provide online interactive learning tools and resources to enhance users’ learning experience;

[0013] Security and privacy protection module, used to ensure the security and privacy of user data and prevent data leakage and unauthorized access;

[0014] The feedback and evaluation module is used to collect user feedback, evaluate the effectiveness of educational services, and make continuous improvements based on feedback.

[0015] Preferably, the data acquisition module includes:

[0016] Automated data scraping units to scrape data from the internet, databases, and social media platforms;

[0017] Manual data entry unit, used to allow users to manually enter specific data, such as questionnaire results;

[0018] The data synchronization unit is used to ensure that the collected data is synchronized and consistent with the existing database.

[0019] Preferably, the data cleaning and preprocessing module includes:

[0020] An outlier detection unit, used to identify and process outliers in the data;

[0021] A data denoising unit, used to reduce data noise through filtering and smoothing techniques;

[0022] The data formatting unit is used to convert data into a unified format for easy analysis.

[0023] Preferably, the data analysis module includes:

[0024] Descriptive statistics analysis unit, used to calculate basic statistics of data, such as mean, median and standard deviation;

[0025] A predictive analytics unit for applying time series analysis and regression models to predict future trends;

[0026] Machine learning units for training classifiers and clustering algorithms to identify patterns and associations in data.

[0027] Preferably, the result display module includes:

[0028] A chart generation unit, used to generate visual charts of data, such as bar charts, line charts and pie charts;

[0029] A report generation unit for automatically generating detailed reports of analysis results;

[0030] The dashboard display unit is used to provide an interactive dashboard to display key indicators and trends in real time.

[0031] Preferably, the prediction model building module includes:

[0032] Time series prediction unit, used to build prediction models based on time series data;

[0033] A machine learning prediction unit, used to build a prediction model based on a machine learning algorithm;

[0034] Model validation unit, used to evaluate the accuracy of the prediction model through cross-validation and test sets.

[0035] Preferably, the personalized recommendation module includes:

[0036] User portrait building unit, used to build user portraits based on user behaviors and preferences;

[0037] Recommendation algorithm unit, used to apply collaborative filtering and content recommendation algorithms to provide personalized recommendations;

[0038] The recommendation result adjustment unit is used to adjust the parameters of the recommendation algorithm according to user feedback.

[0039] Preferably, the interactive learning module includes:

[0040] Online Courses Unit, which provides online courses and video tutorials;

[0041] An interactive discussion board unit to provide a platform for users to discuss and share learning experiences;

[0042] Real-time feedback unit, used to collect user feedback during the learning process to optimize the learning content.

[0043] Preferably, the security and privacy protection module includes:

[0044] A data encryption unit, used to encrypt sensitive data;

[0045] An access control unit, used to control user access rights to data;

[0046] Privacy protection unit, used to ensure the privacy of user data and prevent unauthorized data sharing.

[0047] Preferably, the feedback and evaluation module includes:

[0048] A feedback collection unit, used to collect user feedback on educational services;

[0049] The effectiveness evaluation unit is used to evaluate the effectiveness and impact of educational services;

[0050] Continuous improvement unit, used to continuously improve and optimize services based on evaluation results.

[0051] The present invention provides a data analysis innovation and entrepreneurship education service system, which has the following beneficial effects:

[0052] 1. The present invention can provide customized educational content and resources based on the user's historical behavior and preferences through a personalized recommendation module. This feature can provide accurate course recommendations, project cases, learning resources, etc. according to the learning needs and interests of different users, thereby improving the user's learning effect and satisfaction. Through the construction of user portraits and the continuous optimization of recommendation algorithms, the system can realize personalized push of educational content, ensuring that each user obtains the learning resources that best meet their needs, thereby improving the efficiency and practicality of learning, and helping students achieve greater success in innovation and entrepreneurship education.

[0053] 2. The present invention provides education decision makers with rich data support and visualized analysis results through data analysis modules, prediction model building modules and result display modules. Through in-depth analysis of student information, course data, project cases and market trends, the system can extract valuable information and identify potential trends, helping education managers make more scientific decisions.

[0054] 3. The present invention improves the user's learning experience and establishes a benign learning feedback mechanism through an interactive learning module and a feedback and evaluation module. Online courses, interactive discussion areas, and real-time feedback units provide users with a wealth of interactive learning tools to help them continuously adjust themselves and make progress in the learning process. At the same time, the feedback and evaluation module can collect user feedback in real time and conduct effect evaluation to ensure that educational services can be continuously optimized according to user needs and learning effects. Through this dynamic feedback and improvement mechanism, the system can continuously improve the quality of education, meet the needs of different users, and promote the virtuous cycle development of innovation and entrepreneurship education. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is the overall flow chart of the present invention;

[0056] Figure 2 It is a flow chart of the automatic data capture unit of the present invention;

[0057] Figure 3 is a flow chart of an abnormal value detection unit of the present invention;

[0058] Figure 4 The module flow chart is a result display of the present invention;

[0059] Figure 5 It is a flow chart of the descriptive statistical analysis unit of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Please refer to the attached Figure 1 - Attachment Figure 5 The embodiment of the present invention provides an innovation and entrepreneurship education service system for data analysis, including:

[0062] Data collection module, used to collect various data related to innovation and entrepreneurship education, including but not limited to student information, course data, project cases and market trends;

[0063] The data acquisition module includes:

[0064] Automated data scraping units to scrape data from the internet, databases, and social media platforms;

[0065] Specifically, the automated data capture unit uses data crawler technology and API interfaces to capture data from multiple sources (such as educational platforms, public databases, social media, etc.). The main functions include:

[0066] Internet data crawling: crawling the innovation and entrepreneurship education content publicly available on the Internet, including course information, expert lectures, industry trends, etc.

[0067] Database data capture: Obtain historical data, academic achievements, industry cases, etc. from existing education management systems or industry databases.

[0068] Social media crawling: Collect discussions, articles and trend analysis related to innovation and entrepreneurship education on social platforms (such as Weibo, WeChat, Linked In, etc.).

[0069] Through scheduled tasks and custom rules, the automated data capture unit ensures continuous updating of data and filters and organizes data according to set conditions (such as specific keywords or topics).

[0070] In order to ensure that the data collected from multiple channels is always consistent, the data synchronization unit is responsible for regularly synchronizing the newly collected data with the existing database. The main functions of this module include:

[0071] Data version control: Manage the collected data versions to ensure data updates and comparison with historical records.

[0072] Data consistency check: Perform consistency checks on data from different sources to automatically identify potential data conflicts or inconsistencies.

[0073] Data synchronization scheduling: Automatically synchronize newly collected data to the central database or distributed storage system according to the set synchronization time and conditions.

[0074] The unit can be configured to set synchronization frequency and update strategy to ensure that system data is always kept up to date and accurate.

[0075] The manual data input unit is used to allow the user to manually input specific data, such as questionnaire results; specifically, the manual data input unit

[0076] To make up for the shortcomings of automated capture, the manual data entry unit allows users to directly input specific data, especially those attribute data that cannot be obtained through automated technology. Users can enter:

[0077] Questionnaire results: such as students’ feedback on courses, market research reports, etc.

[0078] Mentor’s comments and project progress: for example, evaluation of entrepreneurial projects, research and development progress of innovative products, etc.

[0079] Custom data: Educators or administrators can manually enter information such as course content, academic achievements, etc.

[0080] The unit is designed to support multiple input formats, such as text boxes, drop-down menus, file uploads, etc., to ensure data integrity and accuracy.

[0081] The data synchronization unit is used to ensure that the collected data is synchronized and consistent with the existing database.

[0082] Data cleaning and preprocessing module, used to clean, denoise and format the collected data to ensure data quality;

[0083] The data cleaning and preprocessing modules include:

[0084] An outlier detection unit, used to identify and process outliers in the data;

[0085] A data denoising unit, used to reduce data noise through filtering and smoothing techniques;

[0086] The data formatting unit is used to convert data into a unified format for easy analysis.

[0087] Data analysis module, which is used to perform statistical analysis, machine learning and pattern recognition on pre-processed data to extract valuable information and trends;

[0088] Specifically, the data cleaning and preprocessing module is responsible for the preliminary processing of the collected raw data to remove noise data, fill missing values ​​and standardize the data format. This process is a key step to ensure data quality and provide a solid foundation for subsequent data analysis.

[0089] De-duplicate data: Identify and delete duplicate entries that appear during the collection process to avoid duplicate calculations or affect analysis results.

[0090] Outlier detection: Identify and process outliers (such as extreme data points) through algorithms to ensure the rationality of data.

[0091] Missing value processing: fill in the missing parts of the data (such as using the mean, interpolation or prediction model) or delete irrelevant data rows.

[0092] Noise identification and removal: Based on statistical and machine learning methods, identify noise in data to ensure data accuracy and validity.

[0093] Semantic analysis: For text data (such as questionnaires or social media content), natural language processing (NLP) is performed to remove irrelevant stop words, special symbols, or repeated information.

[0094] Standardization: standardize data formats such as dates, currencies, units of measurement, etc. to facilitate subsequent analysis and report generation.

[0095] Data conversion: Convert the formats of different data sources according to requirements to adapt them to the internal processing specifications of the system (such as converting text data into digital labels).

[0096] Feature engineering: Extract and generate features suitable for subsequent analysis, such as students’ comprehensive scores, the success probability of entrepreneurial projects, etc.

[0097] Data structuring: Convert unstructured data (such as text, images, etc.) into structured data for easy storage and analysis.

[0098] Descriptive statistics analysis unit, used to calculate basic statistics of data, such as mean, median and standard deviation;

[0099] A predictive analytics unit for applying time series analysis and regression models to predict future trends;

[0100] Machine learning units for training classifiers and clustering algorithms to identify patterns and associations in data.

[0101] The result display module is used to display the analysis results to users in the form of charts, reports and dashboards;

[0102] The result display module includes:

[0103] A chart generation unit, used to generate visual charts of data, such as bar charts, line charts and pie charts;

[0104] A report generation unit for automatically generating detailed reports of analysis results;

[0105] Specifically, the purpose of the report generation unit is to present the results of data analysis in the form of a structured and formatted text report. This unit can automatically generate a detailed analysis report, including data description, analysis process, key findings, conclusions and recommendations.

[0106] Report Contents:

[0107] Data overview: Provides basic information and background of the data, including the source of the data, time range, data volume, etc.

[0108] Analysis methods: Introduce the techniques or methods used in data analysis (such as regression analysis, cluster analysis, etc.).

[0109] Analysis results: Show the main conclusions drawn from the data, such as changes in a trend, the impact of certain factors, etc.

[0110] Conclusion and suggestions: Based on the analysis results, provide suggestions or action guidelines for future decisions, such as how to improve the curriculum content of entrepreneurship education, how to increase student participation, etc.

[0111] Charts and data: Equip the report with charts, tables and other supporting materials to help convey the analysis results of the data intuitively.

[0112] The dashboard display unit is used to provide an interactive dashboard to display key indicators and trends in real time.

[0113] Specifically, the dashboard display unit is an interactive interface for real-time display of key indicators, trends and analysis results of data. It integrates a variety of charts and data elements, allowing users to view a variety of information in one interface and quickly obtain the key data they need.

[0114] Key Features:

[0115] Real-time update: The content displayed on the dashboard should be able to automatically update in real time to reflect the latest data dynamics. For example, key indicators such as the number of participants, capital investment, and project progress in the field of entrepreneurship education can be displayed in real time.

[0116] Interactivity: Users can interact with the dashboard, such as filtering data for different time periods, selecting analysis results in different dimensions, and viewing detailed data.

[0117] Customizability: Users can choose the metrics and charts to display according to their needs, and can adjust the layout and content display of the dashboard.

[0118] Integration of multiple data sources: Dashboards do not only display a single data set, but integrate multiple dimensions from different data sources to provide a more comprehensive analytical view.

[0119] Prediction model building module, used to build a prediction model based on historical data to predict the future trend of innovation and entrepreneurship education;

[0120] The predictive model building blocks include:

[0121] Time series prediction unit, used to build prediction models based on time series data;

[0122] A machine learning prediction unit, used to build a prediction model based on a machine learning algorithm;

[0123] Model validation unit, used to evaluate the accuracy of the prediction model through cross-validation and test sets.

[0124] Personalized recommendation module, used to provide personalized educational content and resource recommendations based on the user's historical behavior and preferences;

[0125] The personalized recommendation module includes:

[0126] User portrait building unit, used to build user portraits based on user behaviors and preferences;

[0127] Specifically, the user portrait construction unit

[0128] Functional description: The user portrait construction unit is responsible for building a personalized user portrait by analyzing the user's behavior data, interest preferences, and learning progress. Specifically, this unit will collect the following types of information:

[0129] Learning behavior data: including courses browsed by users, resources read, discussions participated in, tasks completed, etc.

[0130] Interest preferences: Analyze users’ interest areas through their interaction records on the platform (such as likes, comments, favorites, etc.).

[0131] Personality characteristics: Build a basic profile for the user based on their registration information (such as professional background, learning goals, experience, etc.).

[0132] Recommendation algorithm unit, used to apply collaborative filtering and content recommendation algorithms to provide personalized recommendations;

[0133] Specifically, the recommendation algorithm unit is responsible for applying different recommendation technologies to provide personalized educational content based on the constructed user profile. Common recommendation algorithms include:

[0134] Collaborative filtering algorithm: recommends content that the target user is interested in by analyzing the similarity between other users and the target user's behavior (such as "similar user recommendation"). This algorithm is based on the behavioral similarity between users and is suitable for content recommendation for large-scale users.

[0135] Content recommendation algorithm: Recommends relevant content based on the characteristics of the content itself (such as course tags, subject keywords, etc.), which is suitable for the user's existing interests and needs. For example, if a user frequently studies courses related to "marketing", the system can recommend more related courses and articles.

[0136] The recommendation result adjustment unit is used to adjust the parameters of the recommendation algorithm according to user feedback.

[0137] Interactive learning modules are used to provide online interactive learning tools and resources to enhance users’ learning experience;

[0138] Interactive learning modules include:

[0139] Online Courses Unit, which provides online courses and video tutorials;

[0140] Specifically, the online course unit is the core component of the education service system, which aims to provide learners with a systematic knowledge system through rich course content and video tutorials. Through this unit, learners can choose courses of interest on demand. The course content can cover various data analysis techniques, tools, algorithms and other fields, such as basic statistics, machine learning, data visualization, etc. Video tutorials can be accompanied by pictures and interactive examples to help users understand complex concepts more deeply. In addition, online course units can be updated regularly to ensure that learners are always exposed to the latest industry trends and technological developments. This module can also make personalized recommendations based on the learner's progress to help them master knowledge more efficiently.

[0141] An interactive discussion board unit to provide a platform for users to discuss and share learning experiences;

[0142] Specifically, the interactive discussion area unit provides a social platform for learners to promote communication and cooperation among learners. Through this unit, users can post questions, share learning experiences, and even conduct group discussions or collaborative projects. In this area, users can not only get help from classmates, but also get professional guidance from lecturers or experts. The establishment of an interactive discussion area forms a learning community, enhances learning motivation, and strengthens practical experience and teamwork ability. In order to improve the quality of discussion, certain management mechanisms can be set up, such as guiding discussions, setting topic tags, and regularly holding discussion theme activities to ensure the efficiency and meaningfulness of the discussion content.

[0143] Real-time feedback unit, used to collect user feedback during the learning process to optimize the learning content.

[0144] Specifically, the real-time feedback unit can help the learning platform collect user feedback in a timely manner during the learning process, so as to continuously optimize the learning content and teaching methods. Through this unit, learners can submit feedback after each course or module. The content involved may include course difficulty, teaching quality, content clarity, and user learning experience. Based on these real-time feedback, the platform can adjust the course progress, improve teaching methods, and even add or delete certain modules to make the teaching content more in line with students' needs. In addition to course feedback, learners can also provide feedback on platform functions, interfaces, user experience, etc. to help the development team continuously iterate and optimize the service system.

[0145] Security and privacy protection module, used to ensure the security and privacy of user data and prevent data leakage and unauthorized access;

[0146] The security and privacy protection modules include:

[0147] A data encryption unit, used to encrypt sensitive data;

[0148] Specifically, the data encryption unit plays a vital role in ensuring the security of the system and the privacy of users. In order to protect learners' personal information and learning data, all sensitive data (such as usernames, passwords, grades, feedback content, etc.) will be encrypted. By adopting modern encryption technologies, such as AES (Advanced Encryption Standard) or RSA encryption algorithms, it can be ensured that even if the data is intercepted during transmission, it cannot be interpreted by unauthorized third parties. In addition, the encryption unit can also ensure that sensitive data cannot be used or abused even if the database is leaked through encrypted storage.

[0149] An access control unit, used to control user access rights to data;

[0150] Specifically, the access control unit limits user access to different data in the system by setting up a permission management mechanism. This unit can ensure that only legally authorized users can access certain specific sensitive information. For example, learners can only view their own learning progress and grades, while administrators and instructors can view the statistics of all learners. Permission settings can be refined to different levels of access, including viewing, editing and deletion permissions. In addition, the system should also support multi-factor authentication mechanisms (such as combining passwords and SMS verification codes) to further improve security and prevent unauthorized users from accessing the system through brute force cracking and other means.

[0151] Privacy protection unit, used to ensure the privacy of user data and prevent unauthorized data sharing.

[0152] Specifically, the privacy protection unit ensures that the user's data will not be obtained or abused by unauthorized third parties. During the data collection and use process, the system will strictly follow privacy protection regulations, such as GDPR (General Data Protection Regulation), and clearly inform users of the purpose, scope and use of data collection. The platform should process user data through anonymization or de-identification technology to minimize the risk of exposing user personal information. In addition, the privacy protection unit will conduct security audits regularly and promptly fix potential security vulnerabilities to ensure that the platform will not leak or abuse user data when storing and processing it.

[0153] The feedback and evaluation module is used to collect user feedback, evaluate the effectiveness of educational services, and make continuous improvements based on feedback.

[0154] The feedback and evaluation modules include:

[0155] A feedback collection unit, used to collect user feedback on educational services;

[0156] The effectiveness evaluation unit is used to evaluate the effectiveness and impact of educational services;

[0157] Continuous improvement unit, used to continuously improve and optimize services based on evaluation results.

[0158] Specifically, an important function of the feedback and evaluation module is to collect user feedback to help the education platform continuously improve during operation. Users can submit feedback in a variety of ways, such as filling out online questionnaires, participating in opinion collection activities on the platform, and evaluating course content. These feedbacks will help platform operators understand learners' needs, difficulties encountered, and evaluations of course content, and provide data support for the adjustment of teaching content and methods in the next step. The content of feedback collection can cover various aspects such as course difficulty, teaching interactivity, platform interface friendliness, and learning support.

[0159] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data analysis innovation and entrepreneurship education service system, characterized in that: include: Data collection module, used to collect various data related to innovation and entrepreneurship education, including but not limited to student information, course data, project cases and market trends; Data cleaning and preprocessing module, used to clean, denoise and format the collected data to ensure data quality; Data analysis module, which is used to perform statistical analysis, machine learning and pattern recognition on pre-processed data to extract valuable information and trends; The result display module is used to display the analysis results to users in the form of charts, reports and dashboards; Prediction model building module, used to build a prediction model based on historical data to predict the future trend of innovation and entrepreneurship education; Personalized recommendation module, used to provide personalized educational content and resource recommendations based on the user's historical behavior and preferences; Interactive learning modules are used to provide online interactive learning tools and resources to enhance users’ learning experience; Security and privacy protection module, used to ensure the security and privacy of user data and prevent data leakage and unauthorized access; The feedback and evaluation module is used to collect user feedback, evaluate the effectiveness of educational services, and make continuous improvements based on feedback.

2. According to the data analysis innovation and entrepreneurship education service system of claim 1, it is characterized in that: The data acquisition module comprises: Automated data scraping units to scrape data from the internet, databases, and social media platforms; Manual data entry unit, used to allow users to manually enter specific data, such as questionnaire results; The data synchronization unit is used to ensure that the collected data is synchronized and consistent with the existing database.

3. According to the data analysis innovation and entrepreneurship education service system of claim 1, it is characterized by: The data cleaning and preprocessing module includes: An outlier detection unit, used to identify and process outliers in the data; A data denoising unit, used to reduce data noise through filtering and smoothing techniques; The data formatting unit is used to convert data into a unified format for easy analysis.

4. According to the data analysis innovation and entrepreneurship education service system of claim 1, it is characterized in that: The data analysis module includes: Descriptive statistics analysis unit, used to calculate basic statistics of data, such as mean, median and standard deviation; A predictive analytics unit for applying time series analysis and regression models to predict future trends; Machine learning units for training classifiers and clustering algorithms to identify patterns and associations in data.

5. According to the data analysis innovation and entrepreneurship education service system of claim 1, it is characterized in that: The result display module includes: A chart generation unit, used to generate visual charts of data, such as bar charts, line charts and pie charts; A report generation unit for automatically generating detailed reports of analysis results; The dashboard display unit is used to provide an interactive dashboard to display key indicators and trends in real time.

6. The data analysis innovation and entrepreneurship education service system according to claim 1 is characterized in that: The prediction model building module includes: Time series prediction unit, used to build prediction models based on time series data; A machine learning prediction unit, used to build a prediction model based on a machine learning algorithm; Model validation unit, used to evaluate the accuracy of the prediction model through cross-validation and test sets.

7. The data analysis innovation and entrepreneurship education service system according to claim 1 is characterized in that: The personalized recommendation module includes: User portrait building unit, used to build user portraits based on user behaviors and preferences; Recommendation algorithm unit, used to apply collaborative filtering and content recommendation algorithms to provide personalized recommendations; The recommendation result adjustment unit is used to adjust the parameters of the recommendation algorithm according to user feedback.

8. According to the data analysis innovation and entrepreneurship education service system of claim 1, it is characterized by: The interactive learning module includes: Online Courses Unit, which provides online courses and video tutorials; An interactive discussion board unit to provide a platform for users to discuss and share learning experiences; Real-time feedback unit, used to collect user feedback during the learning process to optimize the learning content.

9. The data analysis innovation and entrepreneurship education service system according to claim 1 is characterized in that: The security and privacy protection module includes: A data encryption unit, used to encrypt sensitive data; An access control unit, used to control user access rights to data; Privacy protection unit, used to ensure the privacy of user data and prevent unauthorized data sharing.

10. The data analysis innovation and entrepreneurship education service system according to claim 1 is characterized in that: The feedback and evaluation module includes: A feedback collection unit, used to collect user feedback on educational services; The effectiveness evaluation unit is used to evaluate the effectiveness and impact of educational services; Continuous improvement unit, used to continuously improve and optimize services based on evaluation results.

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