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作者计算机毕业设计小途个人简介曾长期从事计算机专业培训教学本人也热爱上课教学语言擅长Java、微信小程序、Python、Golang、安卓Android等开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法也喜欢交流技术大家有技术代码这一块的问题可以问我想说的话感谢大家的关注与支持网站实战项目安卓/小程序实战项目大数据实战项目深度学习实战项目目录培训机构信息分析与可视化系统介绍培训机构信息分析与可视化系统演示视频培训机构信息分析与可视化系统演示图片培训机构信息分析与可视化系统代码展示培训机构信息分析与可视化系统文档展示培训机构信息分析与可视化系统介绍本系统名为《基于大数据的培训机构信息分析与可视化》主要围绕培训机构信息做存储、统计、分析和可视化展示。后端可选 PythonDjango 或 JavaSpring Boot 两套实现大数据部分使用 Hadoop 和 Spark借助 HDFS 存放原始与中间数据利用 Spark SQL 完成清洗、聚合、关联和指标计算Pandas、NumPy 用来做辅助统计与数值处理MySQL 负责保存用户、机构基础信息和分析结果。系统功能包括系统首页、大屏可视化、用户、培训机构数据、区域布局分析、许可时效分析、评估等级分析、工种结构分析、联系渠道分析、办学规模分析、机构画像分析、个人信息和修改密码。用户登录后可以查看培训机构总量、区域分布、许可到期情况、评估等级占比、工种结构、联系渠道、办学规模等图表也能进入机构画像查看单个机构的多维特征。前端用 Vue、ElementUI、Echarts、HTML、CSS、JavaScript、jQuery 做页面和图表展示整体重点放在大数据分析流程和可视化结果上让原本分散的培训机构信息能通过 Spark 计算后更直观地呈现出来。培训机构信息分析与可视化系统演示视频项目演示视频培训机构信息分析与可视化系统演示图片培训机构信息分析与可视化系统代码展示frompyspark.sqlimportSparkSession sparkSparkSession.builder.appName(TrainingOrgBigDataAnalysis).master(local[*]).config(spark.sql.shuffle.partitions,4).getOrCreate()defbuild_dashboard_data():org_dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/training_org).option(dbtable,training_org).option(user,root).option(password,123456).load()org_df.createOrReplaceTempView(training_org)org_df.cache()spark.sql(analyze table training_org compute statistics).collect()total_countspark.sql(select count(*) as total from training_org).collect()[0][total]region_countspark.sql(select region, count(*) as num from training_org group by region order by num desc).collect()grade_countspark.sql(select eval_grade, count(*) as num from training_org group by eval_grade order by num desc).collect()scale_countspark.sql(select scale_level, count(*) as num from training_org group by scale_level order by num desc).collect()work_type_countspark.sql(select work_type, count(*) as num from training_org group by work_type order by num desc).collect()contact_countspark.sql(select contact_channel, count(*) as num from training_org group by contact_channel order by num desc).collect()license_warnspark.sql(select case when datediff(license_end, current_date()) 90 then 即将到期 else 正常 end as status, count(*) as num from training_org group by status).collect()city_topspark.sql(select city, count(*) as num from training_org group by city order by num desc limit 10).collect()result{total:total_count,region:region_count,grade:grade_count,scale:scale_count,workType:work_type_count,contact:contact_count,licenseWarn:license_warn,cityTop:city_top}returnresultdefanalyze_region_layout():org_dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/training_org).option(dbtable,training_org).option(user,root).option(password,123456).load()org_df.createOrReplaceTempView(training_org)org_df.cache()region_dfspark.sql(select region, city, count(*) as org_num, sum(student_num) as student_total, avg(teacher_num) as teacher_avg from training_org group by region, city)region_df.createOrReplaceTempView(region_layout)region_totalspark.sql(select region, sum(org_num) as org_total, sum(student_total) as student_total from region_layout group by region order by org_total desc).collect()city_rankspark.sql(select city, org_num, student_total from region_layout order by org_num desc limit 20).collect()densityspark.sql(select region, sum(org_num) / (count(distinct city) 1) as avg_city_org from region_layout group by region).collect()scale_distspark.sql(select region, scale_level, count(*) as num from training_org group by region, scale_level).collect()license_regionspark.sql(select region, case when datediff(license_end, current_date()) 90 then 即将到期 else 正常 end as status, count(*) as num from training_org group by region, status).collect()work_regionspark.sql(select region, work_type, count(*) as num from training_org group by region, work_type).collect()contact_regionspark.sql(select region, contact_channel, count(*) as num from training_org group by region, contact_channel).collect()result{regionTotal:region_total,cityRank:city_rank,density:density,scaleDist:scale_dist,licenseRegion:license_region,workRegion:work_region,contactRegion:contact_region}returnresultdefanalyze_org_profile():org_dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/training_org).option(dbtable,training_org).option(user,root).option(password,123456).load()org_df.createOrReplaceTempView(training_org)org_df.cache()profile_dfspark.sql(select org_id, org_name, region, city, scale_level, eval_grade, work_type, contact_channel, student_num, teacher_num, license_end from training_org)profile_df.createOrReplaceTempView(org_profile)grade_scalespark.sql(select scale_level, eval_grade, count(*) as num from org_profile group by scale_level, eval_grade).collect()work_contactspark.sql(select work_type, contact_channel, count(*) as num from org_profile group by work_type, contact_channel).collect()high_qualityspark.sql(select org_id, org_name, eval_grade, scale_level from org_profile where eval_grade in (A, 优秀) and scale_level in (大型, 中型) order by student_num desc limit 20).collect()license_profilespark.sql(select org_id, org_name, license_end, case when datediff(license_end, current_date()) 90 then 即将到期 else 正常 end as status from org_profile).collect()size_profilespark.sql(select org_id, org_name, student_num, teacher_num, student_num / (teacher_num 1) as student_teacher_ratio from org_profile order by student_teacher_ratio desc limit 20).collect()region_profilespark.sql(select region, count(*) as org_num, avg(student_num) as avg_student, avg(teacher_num) as avg_teacher from org_profile group by region).collect()work_profilespark.sql(select work_type, count(*) as org_num, avg(student_num) as avg_student from org_profile group by work_type).collect()result{gradeScale:grade_scale,workContact:work_contact,highQuality:high_quality,licenseProfile:license_profile,sizeProfile:size_profile,regionProfile:region_profile,workProfile:work_profile}returnresult培训机构信息分析与可视化系统文档展示作者计算机毕业设计小途个人简介曾长期从事计算机专业培训教学本人也热爱上课教学语言擅长Java、微信小程序、Python、Golang、安卓Android等开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法也喜欢交流技术大家有技术代码这一块的问题可以问我想说的话感谢大家的关注与支持网站实战项目安卓/小程序实战项目大数据实战项目深度学习实战项目 计算机毕业设计选题推荐:基于大数据的培训机构信息分析与可视化、毕业设计选题、选题推荐、高质量项目、毕设指导、项目定制、源码、讲解文档
作者计算机毕业设计小途个人简介曾长期从事计算机专业培训教学本人也热爱上课教学语言擅长Java、微信小程序、Python、Golang、安卓Android等开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法也喜欢交流技术大家有技术代码这一块的问题可以问我想说的话感谢大家的关注与支持网站实战项目安卓/小程序实战项目大数据实战项目深度学习实战项目目录培训机构信息分析与可视化系统介绍培训机构信息分析与可视化系统演示视频培训机构信息分析与可视化系统演示图片培训机构信息分析与可视化系统代码展示培训机构信息分析与可视化系统文档展示培训机构信息分析与可视化系统介绍本系统名为《基于大数据的培训机构信息分析与可视化》主要围绕培训机构信息做存储、统计、分析和可视化展示。后端可选 PythonDjango 或 JavaSpring Boot 两套实现大数据部分使用 Hadoop 和 Spark借助 HDFS 存放原始与中间数据利用 Spark SQL 完成清洗、聚合、关联和指标计算Pandas、NumPy 用来做辅助统计与数值处理MySQL 负责保存用户、机构基础信息和分析结果。系统功能包括系统首页、大屏可视化、用户、培训机构数据、区域布局分析、许可时效分析、评估等级分析、工种结构分析、联系渠道分析、办学规模分析、机构画像分析、个人信息和修改密码。用户登录后可以查看培训机构总量、区域分布、许可到期情况、评估等级占比、工种结构、联系渠道、办学规模等图表也能进入机构画像查看单个机构的多维特征。前端用 Vue、ElementUI、Echarts、HTML、CSS、JavaScript、jQuery 做页面和图表展示整体重点放在大数据分析流程和可视化结果上让原本分散的培训机构信息能通过 Spark 计算后更直观地呈现出来。培训机构信息分析与可视化系统演示视频项目演示视频培训机构信息分析与可视化系统演示图片培训机构信息分析与可视化系统代码展示frompyspark.sqlimportSparkSession sparkSparkSession.builder.appName(TrainingOrgBigDataAnalysis).master(local[*]).config(spark.sql.shuffle.partitions,4).getOrCreate()defbuild_dashboard_data():org_dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/training_org).option(dbtable,training_org).option(user,root).option(password,123456).load()org_df.createOrReplaceTempView(training_org)org_df.cache()spark.sql(analyze table training_org compute statistics).collect()total_countspark.sql(select count(*) as total from training_org).collect()[0][total]region_countspark.sql(select region, count(*) as num from training_org group by region order by num desc).collect()grade_countspark.sql(select eval_grade, count(*) as num from training_org group by eval_grade order by num desc).collect()scale_countspark.sql(select scale_level, count(*) as num from training_org group by scale_level order by num desc).collect()work_type_countspark.sql(select work_type, count(*) as num from training_org group by work_type order by num desc).collect()contact_countspark.sql(select contact_channel, count(*) as num from training_org group by contact_channel order by num desc).collect()license_warnspark.sql(select case when datediff(license_end, current_date()) 90 then 即将到期 else 正常 end as status, count(*) as num from training_org group by status).collect()city_topspark.sql(select city, count(*) as num from training_org group by city order by num desc limit 10).collect()result{total:total_count,region:region_count,grade:grade_count,scale:scale_count,workType:work_type_count,contact:contact_count,licenseWarn:license_warn,cityTop:city_top}returnresultdefanalyze_region_layout():org_dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/training_org).option(dbtable,training_org).option(user,root).option(password,123456).load()org_df.createOrReplaceTempView(training_org)org_df.cache()region_dfspark.sql(select region, city, count(*) as org_num, sum(student_num) as student_total, avg(teacher_num) as teacher_avg from training_org group by region, city)region_df.createOrReplaceTempView(region_layout)region_totalspark.sql(select region, sum(org_num) as org_total, sum(student_total) as student_total from region_layout group by region order by org_total desc).collect()city_rankspark.sql(select city, org_num, student_total from region_layout order by org_num desc limit 20).collect()densityspark.sql(select region, sum(org_num) / (count(distinct city) 1) as avg_city_org from region_layout group by region).collect()scale_distspark.sql(select region, scale_level, count(*) as num from training_org group by region, scale_level).collect()license_regionspark.sql(select region, case when datediff(license_end, current_date()) 90 then 即将到期 else 正常 end as status, count(*) as num from training_org group by region, status).collect()work_regionspark.sql(select region, work_type, count(*) as num from training_org group by region, work_type).collect()contact_regionspark.sql(select region, contact_channel, count(*) as num from training_org group by region, contact_channel).collect()result{regionTotal:region_total,cityRank:city_rank,density:density,scaleDist:scale_dist,licenseRegion:license_region,workRegion:work_region,contactRegion:contact_region}returnresultdefanalyze_org_profile():org_dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/training_org).option(dbtable,training_org).option(user,root).option(password,123456).load()org_df.createOrReplaceTempView(training_org)org_df.cache()profile_dfspark.sql(select org_id, org_name, region, city, scale_level, eval_grade, work_type, contact_channel, student_num, teacher_num, license_end from training_org)profile_df.createOrReplaceTempView(org_profile)grade_scalespark.sql(select scale_level, eval_grade, count(*) as num from org_profile group by scale_level, eval_grade).collect()work_contactspark.sql(select work_type, contact_channel, count(*) as num from org_profile group by work_type, contact_channel).collect()high_qualityspark.sql(select org_id, org_name, eval_grade, scale_level from org_profile where eval_grade in (A, 优秀) and scale_level in (大型, 中型) order by student_num desc limit 20).collect()license_profilespark.sql(select org_id, org_name, license_end, case when datediff(license_end, current_date()) 90 then 即将到期 else 正常 end as status from org_profile).collect()size_profilespark.sql(select org_id, org_name, student_num, teacher_num, student_num / (teacher_num 1) as student_teacher_ratio from org_profile order by student_teacher_ratio desc limit 20).collect()region_profilespark.sql(select region, count(*) as org_num, avg(student_num) as avg_student, avg(teacher_num) as avg_teacher from org_profile group by region).collect()work_profilespark.sql(select work_type, count(*) as org_num, avg(student_num) as avg_student from org_profile group by work_type).collect()result{gradeScale:grade_scale,workContact:work_contact,highQuality:high_quality,licenseProfile:license_profile,sizeProfile:size_profile,regionProfile:region_profile,workProfile:work_profile}returnresult培训机构信息分析与可视化系统文档展示作者计算机毕业设计小途个人简介曾长期从事计算机专业培训教学本人也热爱上课教学语言擅长Java、微信小程序、Python、Golang、安卓Android等开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法也喜欢交流技术大家有技术代码这一块的问题可以问我想说的话感谢大家的关注与支持网站实战项目安卓/小程序实战项目大数据实战项目深度学习实战项目