🍊作者：计算机毕设匠心工作室
🍊简介：毕业后就一直专业从事计算机软件程序开发，至今也有8年工作经验。擅长Java、Python、微信小程序、安卓、大数据、PHP、.NET|C#、Golang等。
擅长：按照需求定制化开发项目、 源码、对代码进行完整讲解、文档撰写、ppt制作。
🍊心愿：点赞 👍 收藏 ⭐评论 📝
👇🏻 精彩专栏推荐订阅 👇🏻 不然下次找不到哟~
🍅 ↓↓文末获取源码联系↓↓🍅
基于大数据的学生创业数据分析可视化系统-功能介绍
基于大数据的学生创业数据分析可视化系统是一个专门针对学生创业潜力挖掘和职业发展路径分析的综合性平台。该系统采用Hadoop分布式存储架构和Spark大数据处理引擎，能够高效处理海量学生数据，通过对学生的技术技能、管理能力、沟通水平等多维度指标进行深度分析，实现学生群体综合画像分析、创业潜力深度挖掘、不同职业发展路径特征对比以及关键影响因素关联性分析等核心功能。系统前端采用Vue框架结合ElementUI组件库构建用户界面，通过Echarts图表库实现数据的多样化可视化展示，包括雷达图、热力图、散点图等多种图表形式。后端基于Django框架开发，集成Spark SQL进行大数据查询处理，运用K-Means聚类算法对学生群体进行智能分类，并通过Pandas和NumPy进行数据预处理和统计分析。整个系统通过HDFS实现数据的分布式存储，确保数据处理的高效性和可靠性，为教育决策者和学生个人提供科学的数据支撑和可视化分析结果。
基于大数据的学生创业数据分析可视化系统-选题背景意义
选题背景 随着高等教育规模的不断扩大和创新创业教育理念的深入推广，越来越多的大学生开始关注自身的创业潜力和职业发展方向。当前高校普遍面临着如何科学评估学生创业能力、如何为不同特质的学生提供个性化职业指导等现实问题。传统的学生评价体系往往依赖主观判断和有限的定性分析，缺乏对学生多维度数据的深度挖掘和客观量化评估。与此同时，学生在学习过程中产生的大量行为数据、技能评估数据、项目参与数据等宝贵信息资源，由于缺乏有效的分析工具和处理手段，往往无法得到充分利用。这种信息孤岛现象不仅影响了教育资源的优化配置，也限制了学生个人发展的精准指导。在大数据技术日趋成熟的背景下，如何运用现代化的数据分析技术对学生创业数据进行深度挖掘和可视化呈现，已成为教育信息化发展的重要课题。 选题意义 本课题的研究具有较为明显的理论价值和实践意义。从理论层面来看，通过构建基于大数据技术的学生创业数据分析模型，能够为高等教育数据挖掘领域提供一定的方法参考，丰富教育数据分析的理论体系。该系统尝试将Hadoop和Spark等大数据处理技术引入教育数据分析场景，探索了大数据技术在教育领域的应用可能性。从实践角度而言，系统能够帮助高校教育管理者更好地了解学生群体特征，为制定针对性的教育策略和资源配置提供数据依据。学生个人也能够通过系统的分析结果，更清楚地认识自己的能力结构和发展潜力，从而做出更合适的学习规划和职业选择。对于创业教育的开展，系统提供的创业潜力分析和特征画像功能，可以帮助相关教师识别具有创业潜质的学生群体，开展更有针对性的创业指导。虽然作为一个毕业设计项目，系统的规模和功能相对有限，但其探索的技术路线和分析思路，对于推动教育数据分析的发展仍具有一定的参考价值。
基于大数据的学生创业数据分析可视化系统-技术选型
大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL
基于大数据的学生创业数据分析可视化系统-视频展示
基于大数据的学生创业数据分析可视化系统-图片展示
基于大数据的学生创业数据分析可视化系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, when, desc, asc
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
import pandas as pd
import numpy as np
from django.http import JsonResponse
from sklearn.cluster import KMeans as SKKMeans
def analyze_student_entrepreneurial_potential(request):
spark = SparkSession.builder.appName("StudentEntrepreneurialAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/student_data/entrepreneurial_data.csv")
high_potential_students = df.filter(col("entrepreneurial_aptitude") == "高")
medium_potential_students = df.filter(col("entrepreneurial_aptitude") == "中")
low_potential_students = df.filter(col("entrepreneurial_aptitude") == "低")
high_avg_technical = high_potential_students.select(avg(col("technical_skill_score")).alias("avg_technical")).collect()[0]["avg_technical"]
high_avg_managerial = high_potential_students.select(avg(col("managerial_skill_score")).alias("avg_managerial")).collect()[0]["avg_managerial"]
high_avg_communication = high_potential_students.select(avg(col("communication_skill_score")).alias("avg_communication")).collect()[0]["avg_communication"]
high_avg_study_time = high_potential_students.select(avg(col("avg_daily_study_time")).alias("avg_study_time")).collect()[0]["avg_study_time"]
high_avg_innovation = high_potential_students.select(avg(col("innovation_activity_count")).alias("avg_innovation")).collect()[0]["avg_innovation"]
medium_avg_technical = medium_potential_students.select(avg(col("technical_skill_score")).alias("avg_technical")).collect()[0]["avg_technical"]
medium_avg_managerial = medium_potential_students.select(avg(col("managerial_skill_score")).alias("avg_managerial")).collect()[0]["avg_managerial"]
medium_avg_communication = medium_potential_students.select(avg(col("communication_skill_score")).alias("avg_communication")).collect()[0]["avg_communication"]
medium_avg_study_time = medium_potential_students.select(avg(col("avg_daily_study_time")).alias("avg_study_time")).collect()[0]["avg_study_time"]
medium_avg_innovation = medium_potential_students.select(avg(col("innovation_activity_count")).alias("avg_innovation")).collect()[0]["avg_innovation"]
low_avg_technical = low_potential_students.select(avg(col("technical_skill_score")).alias("avg_technical")).collect()[0]["avg_technical"]
low_avg_managerial = low_potential_students.select(avg(col("managerial_skill_score")).alias("avg_managerial")).collect()[0]["avg_managerial"]
low_avg_communication = low_potential_students.select(avg(col("communication_skill_score")).alias("avg_communication")).collect()[0]["avg_communication"]
low_avg_study_time = low_potential_students.select(avg(col("avg_daily_study_time")).alias("avg_study_time")).collect()[0]["avg_study_time"]
low_avg_innovation = low_potential_students.select(avg(col("innovation_activity_count")).alias("avg_innovation")).collect()[0]["avg_innovation"]
potential_comparison = {
"high_potential": {
"technical_skill": round(high_avg_technical, 2),
"managerial_skill": round(high_avg_managerial, 2),
"communication_skill": round(high_avg_communication, 2),
"study_time": round(high_avg_study_time, 2),
"innovation_count": round(high_avg_innovation, 2)
},
"medium_potential": {
"technical_skill": round(medium_avg_technical, 2),
"managerial_skill": round(medium_avg_managerial, 2),
"communication_skill": round(medium_avg_communication, 2),
"study_time": round(medium_avg_study_time, 2),
"innovation_count": round(medium_avg_innovation, 2)
},
"low_potential": {
"technical_skill": round(low_avg_technical, 2),
"managerial_skill": round(low_avg_managerial, 2),
"communication_skill": round(low_avg_communication, 2),
"study_time": round(low_avg_study_time, 2),
"innovation_count": round(low_avg_innovation, 2)
}
}
spark.stop()
return JsonResponse({"success": True, "data": potential_comparison})
def analyze_career_path_characteristics(request):
spark = SparkSession.builder.appName("CareerPathAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/student_data/entrepreneurial_data.csv")
career_paths = df.select("career_path_recommendation").distinct().collect()
career_analysis_results = {}
for path_row in career_paths:
path = path_row["career_path_recommendation"]
path_students = df.filter(col("career_path_recommendation") == path)
path_count = path_students.count()
avg_technical = path_students.select(avg(col("technical_skill_score")).alias("avg_technical")).collect()[0]["avg_technical"]
avg_managerial = path_students.select(avg(col("managerial_skill_score")).alias("avg_managerial")).collect()[0]["avg_managerial"]
avg_communication = path_students.select(avg(col("communication_skill_score")).alias("avg_communication")).collect()[0]["avg_communication"]
avg_entrepreneurial_events = path_students.select(avg(col("entrepreneurial_event_hours")).alias("avg_events")).collect()[0]["avg_events"]
avg_innovation_activities = path_students.select(avg(col("innovation_activity_count")).alias("avg_innovation")).collect()[0]["avg_innovation"]
high_potential_count = path_students.filter(col("entrepreneurial_aptitude") == "高").count()
medium_potential_count = path_students.filter(col("entrepreneurial_aptitude") == "中").count()
low_potential_count = path_students.filter(col("entrepreneurial_aptitude") == "低").count()
high_potential_ratio = round((high_potential_count / path_count) * 100, 2) if path_count > 0 else 0
avg_career_alignment = path_students.select(avg(col("career_goal_alignment_score")).alias("avg_alignment")).collect()[0]["avg_alignment"]
avg_time_management = path_students.select(avg(col("time_management_score")).alias("avg_time_mgmt")).collect()[0]["avg_time_mgmt"]
career_analysis_results[path] = {
"student_count": path_count,
"avg_technical_skill": round(avg_technical, 2),
"avg_managerial_skill": round(avg_managerial, 2),
"avg_communication_skill": round(avg_communication, 2),
"avg_entrepreneurial_events": round(avg_entrepreneurial_events, 2),
"avg_innovation_activities": round(avg_innovation_activities, 2),
"high_potential_ratio": high_potential_ratio,
"avg_career_alignment": round(avg_career_alignment, 2),
"avg_time_management": round(avg_time_management, 2)
}
spark.stop()
return JsonResponse({"success": True, "data": career_analysis_results})
def perform_student_clustering_analysis(request):
spark = SparkSession.builder.appName("StudentClusteringAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/student_data/entrepreneurial_data.csv")
feature_columns = ["technical_skill_score", "managerial_skill_score", "communication_skill_score", "time_management_score", "innovation_activity_count", "avg_daily_study_time", "entrepreneurial_event_hours"]
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
assembled_df = assembler.transform(df)
kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(assembled_df)
clustered_df = model.transform(assembled_df)
cluster_centers = model.clusterCenters()
cluster_analysis = {}
for i in range(4):
cluster_data = clustered_df.filter(col("cluster") == i)
cluster_count = cluster_data.count()
avg_technical = cluster_data.select(avg(col("technical_skill_score")).alias("avg_tech")).collect()[0]["avg_tech"]
avg_managerial = cluster_data.select(avg(col("managerial_skill_score")).alias("avg_mgmt")).collect()[0]["avg_mgmt"]
avg_communication = cluster_data.select(avg(col("communication_skill_score")).alias("avg_comm")).collect()[0]["avg_comm"]
avg_time_mgmt = cluster_data.select(avg(col("time_management_score")).alias("avg_time")).collect()[0]["avg_time"]
avg_innovation = cluster_data.select(avg(col("innovation_activity_count")).alias("avg_innov")).collect()[0]["avg_innov"]
avg_study_time = cluster_data.select(avg(col("avg_daily_study_time")).alias("avg_study")).collect()[0]["avg_study"]
avg_entrepreneurial = cluster_data.select(avg(col("entrepreneurial_event_hours")).alias("avg_entre")).collect()[0]["avg_entre"]
high_potential_count = cluster_data.filter(col("entrepreneurial_aptitude") == "高").count()
high_potential_ratio = round((high_potential_count / cluster_count) * 100, 2) if cluster_count > 0 else 0
startup_founder_count = cluster_data.filter(col("career_path_recommendation") == "Startup Founder").count()
entrepreneur_residence_count = cluster_data.filter(col("career_path_recommendation") == "Entrepreneur-in-Residence").count()
entrepreneurial_career_ratio = round(((startup_founder_count + entrepreneur_residence_count) / cluster_count) * 100, 2) if cluster_count > 0 else 0
cluster_analysis[f"cluster_{i}"] = {
"student_count": cluster_count,
"avg_technical_skill": round(avg_technical, 2),
"avg_managerial_skill": round(avg_managerial, 2),
"avg_communication_skill": round(avg_communication, 2),
"avg_time_management": round(avg_time_mgmt, 2),
"avg_innovation_activity": round(avg_innovation, 2),
"avg_daily_study_time": round(avg_study_time, 2),
"avg_entrepreneurial_events": round(avg_entrepreneurial, 2),
"high_potential_ratio": high_potential_ratio,
"entrepreneurial_career_ratio": entrepreneurial_career_ratio
}
spark.stop()
return JsonResponse({"success": True, "data": cluster_analysis, "cluster_centers": [center.tolist() for center in cluster_centers]})
基于大数据的学生创业数据分析可视化系统-结语
👇🏻 精彩专栏推荐订阅 👇🏻 不然下次找不到哟~
🍅 主页获取源码联系🍅
