Twitter 数据分析

•

0 likes•1,322 views

this is my twitter Data Analysis

我的twitter统计数据分析

关于Tweet的统计

到目前为止，平均
每天发布8.8条tweets。在4
月19号发布第一条tweet，5
月份共计115条，6月份198
条，7月份363条。

关于Tweet的统计

时段统计
高峰时段：
8～9AM
8～10PM

最近30天的每日tweets统计

Interface used

twitter
Hellotxt
TwitZap
Bit.ly

Followers

增长趋势

AND MORE

Recommended

Data Mining with Splunk

Data Mining with Splunk

Data Mining with SplunkDavid Carasso

Apache Metron: Community Driven Cyber Security

Apache Metron: Community Driven Cyber Security

Apache Metron: Community Driven Cyber Security

DataWorks Summit/Hadoop Summit

Big Data Application Architectures - Fraud Detection

Big Data Application Architectures - Fraud Detection

Big Data Application Architectures - Fraud Detection

DataWorks Summit/Hadoop Summit

Splunk Webinar: Full-Stack End-to-End SAP-Monitoring mit Splunk

Splunk Webinar: Full-Stack End-to-End SAP-Monitoring mit Splunk

Splunk Webinar: Full-Stack End-to-End SAP-Monitoring mit Splunk

별헤는 사람들 2024년 3월호 전명원 교수 자료 - 우주의 스케일과 급팽창

별헤는 사람들 2024년 3월호 전명원 교수 자료 - 우주의 스케일과 급팽창

별헤는 사람들 2024년 3월호 전명원 교수 자료 - 우주의 스케일과 급팽창

Sql常见面试题yiditushe

Some experiences and lessons learnt from hunting a job

Some experiences and lessons learnt from hunting a job

Some experiences and lessons learnt from hunting a job

Sql培训 (1)

Recommended

Data Mining with Splunk

Data Mining with Splunk

Data Mining with SplunkDavid Carasso

Apache Metron: Community Driven Cyber Security

Apache Metron: Community Driven Cyber Security

Apache Metron: Community Driven Cyber Security

DataWorks Summit/Hadoop Summit

Big Data Application Architectures - Fraud Detection

Big Data Application Architectures - Fraud Detection

Big Data Application Architectures - Fraud Detection

DataWorks Summit/Hadoop Summit

Splunk Webinar: Full-Stack End-to-End SAP-Monitoring mit Splunk

Splunk Webinar: Full-Stack End-to-End SAP-Monitoring mit Splunk

Splunk Webinar: Full-Stack End-to-End SAP-Monitoring mit Splunk

별헤는 사람들 2024년 3월호 전명원 교수 자료 - 우주의 스케일과 급팽창

별헤는 사람들 2024년 3월호 전명원 교수 자료 - 우주의 스케일과 급팽창

별헤는 사람들 2024년 3월호 전명원 교수 자료 - 우주의 스케일과 급팽창

Sql常见面试题yiditushe

Some experiences and lessons learnt from hunting a job

Some experiences and lessons learnt from hunting a job

Some experiences and lessons learnt from hunting a job

Sql培训 (1)

用户研究中的数据分析

可视化与可视分析从数据拥有者到数据用户的桥梁

可视化与可视分析从数据拥有者到数据用户的桥梁

可视化与可视分析从数据拥有者到数据用户的桥梁gettyying

Oracle面市笔记yiditushe

在线卡片分类测试和数据分析工具分享

在线卡片分类测试和数据分析工具分享

在线卡片分类测试和数据分析工具分享

下一代推荐引擎的关键技术及应用案例

下一代推荐引擎的关键技术及应用案例

下一代推荐引擎的关键技术及应用案例gettyying

不生病的理由jiongjiong007

MySQL_EXPLAIN_liling

MySQL_EXPLAIN_liling

MySQL_EXPLAIN_lilingTracyLLing

Banking Big Data Analytics

Banking Big Data Analytics

Banking Big Data Analytics

Bria Strategy Group

Oracle数据库分析函数详解

Oracle数据库分析函数详解

Oracle数据库分析函数详解

The Joys of Clean Data with Matt Dowle

The Joys of Clean Data with Matt Dowle

The Joys of Clean Data with Matt Dowle

The simplethebeautiful

The simplethebeautiful

The simplethebeautiful

面试常见问题

数据挖掘竞赛经验分享严强Felicia Wenyi Fei

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Return predictability has been a controversial topic in finance for a long time. We show there is substantial predictive power in combining forecasting variables. We apply correlation screening to combine twenty variables that have been proposed in the return predictability literature, and demonstrate forecasting power at a six-month horizon. We illustrate the economic significance of return predictability through a simulation which takes positions in SPY proportional to the model forecast. The simulated strategy yields annual returns more than twice that of the buy-and-hold strategy, with a Sharpe ratio four times as large. This application of big data ideas to return predictability serves to shift the sentiment associated with market timing.

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

This document is a point of view on how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back. The PoV explores these challenges and suggests actions for banks in order to scale-up to the next level of customer data analytics.

Working With Big Data

Working With Big Data

Working With Big Data

Visual Design with Data

Visual Design with Data

Visual Design with Data

An immersive workshop at General Assembly, SF. I typically teach this workshop at General Assembly, San Francisco. To see a list of my upcoming classes, visit https://generalassemb.ly/instructors/seth-familian/4813 I also teach this workshop as a private lunch-and-learn or half-day immersive session for corporate clients. To learn more about pricing and availability, please contact me at http://familian1.com

More Related Content

Viewers also liked

用户研究中的数据分析

可视化与可视分析从数据拥有者到数据用户的桥梁

可视化与可视分析从数据拥有者到数据用户的桥梁

可视化与可视分析从数据拥有者到数据用户的桥梁gettyying

Oracle面市笔记yiditushe

在线卡片分类测试和数据分析工具分享

在线卡片分类测试和数据分析工具分享

在线卡片分类测试和数据分析工具分享

下一代推荐引擎的关键技术及应用案例

下一代推荐引擎的关键技术及应用案例

下一代推荐引擎的关键技术及应用案例gettyying

不生病的理由jiongjiong007

MySQL_EXPLAIN_liling

MySQL_EXPLAIN_liling

MySQL_EXPLAIN_lilingTracyLLing

Banking Big Data Analytics

Banking Big Data Analytics

Banking Big Data Analytics

Bria Strategy Group

Oracle数据库分析函数详解

Oracle数据库分析函数详解

Oracle数据库分析函数详解

The Joys of Clean Data with Matt Dowle

The Joys of Clean Data with Matt Dowle

The Joys of Clean Data with Matt Dowle

The simplethebeautiful

The simplethebeautiful

The simplethebeautiful

面试常见问题

数据挖掘竞赛经验分享严强Felicia Wenyi Fei

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Return predictability has been a controversial topic in finance for a long time. We show there is substantial predictive power in combining forecasting variables. We apply correlation screening to combine twenty variables that have been proposed in the return predictability literature, and demonstrate forecasting power at a six-month horizon. We illustrate the economic significance of return predictability through a simulation which takes positions in SPY proportional to the model forecast. The simulated strategy yields annual returns more than twice that of the buy-and-hold strategy, with a Sharpe ratio four times as large. This application of big data ideas to return predictability serves to shift the sentiment associated with market timing.

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

This document is a point of view on how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back. The PoV explores these challenges and suggests actions for banks in order to scale-up to the next level of customer data analytics.

Working With Big Data

Working With Big Data

Working With Big Data

Visual Design with Data

Visual Design with Data

Visual Design with Data

An immersive workshop at General Assembly, SF. I typically teach this workshop at General Assembly, San Francisco. To see a list of my upcoming classes, visit https://generalassemb.ly/instructors/seth-familian/4813 I also teach this workshop as a private lunch-and-learn or half-day immersive session for corporate clients. To learn more about pricing and availability, please contact me at http://familian1.com

Viewers also liked (18)

用户研究中的数据分析

可视化与可视分析从数据拥有者到数据用户的桥梁

可视化与可视分析从数据拥有者到数据用户的桥梁

可视化与可视分析从数据拥有者到数据用户的桥梁

Oracle面市笔记

在线卡片分类测试和数据分析工具分享

在线卡片分类测试和数据分析工具分享

在线卡片分类测试和数据分析工具分享

下一代推荐引擎的关键技术及应用案例

下一代推荐引擎的关键技术及应用案例

下一代推荐引擎的关键技术及应用案例

不生病的理由

MySQL_EXPLAIN_liling

MySQL_EXPLAIN_liling

MySQL_EXPLAIN_liling

Banking Big Data Analytics

Banking Big Data Analytics

Banking Big Data Analytics

Oracle数据库分析函数详解

Oracle数据库分析函数详解

Oracle数据库分析函数详解

The Joys of Clean Data with Matt Dowle

The Joys of Clean Data with Matt Dowle

The Joys of Clean Data with Matt Dowle

The simplethebeautiful

The simplethebeautiful

The simplethebeautiful

面试常见问题

数据挖掘竞赛经验分享严强

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

网站分析及数据推动设计决策分享- Smartdesign Shanghai Taylor

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Market Timing, Big Data, and Machine Learning by Xiao Qiao at QuantCon 2016

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?

Working With Big Data

Working With Big Data

Working With Big Data

Visual Design with Data

Visual Design with Data

Visual Design with Data

Twitter 数据分析

1. 我的twitter统计数据分析

2. 关于Tweet的统计到目前为止，平均每天发布8.8条tweets。在4 月19号发布第一条tweet，5 月份共计115条，6月份198 条，7月份363条。

3. 关于Tweet的统计时段统计高峰时段： 8～9AM 8～10PM

4. 最近30天的每日tweets统计

5. Interface used twitter Hellotxt TwitZap Bit.ly

7. 增长趋势

8.