ch
Feedback
Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

前往频道在 Telegram

Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data

显示更多

📈 Telegram 频道 Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources 的分析概览

频道 Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 39 698 名订阅者,在 教育 类别中位列第 4 591,并在 印度 地区排名第 9 708

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 39 698 名订阅者。

根据 29 八月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 36,过去 24 小时变化为 17,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 1.87%。内容发布后 24 小时内通常能获得 0.73% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 744 次浏览,首日通常累积 290 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 2
  • 主题关注点: 内容集中在 analytic, dataset, visualization, sql, learning 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data

凭借高频更新(最新数据采集于 30 八月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。

Buy Ad
39 698
订阅者
+1724 小时
-137
+3630
帖子存档
+6
Practical Guide to Scikit-Learn for Data Science.pdf9.22 KB

+1
Fake news Detection Machine Learning Project with 92%Accuracy it contain compressed file in which "jupyter notebook file and dataset"✅

Cracking the SQL Interview: Your Key to Database Success 📊💼

ADVANCED SQL - Unlock Expert Level 💥Level up your SQL game with this Advanced SQL guide. 📊 💥Cover SQL in just 10 days with structured content & practice questions.

Hi All, Here is a Python Notebook for last minute revision of Exploratory data analysis (EDA) in Python. The notebook consists of all basic function and operations used in Exploratory data analysis such as : 1- Deleting rows and columns from a dataframe 2- Dealing with duplicate values 3- Removing outliers from dataframe 4- Outliers detection using IQR (Inter Quartile Range) 5- Outliers detection using Z score 6- Value Counts 7- Univariate Analysis 8- Subplots, Countplots, Scatterplots, Lineplots, Boxplots, Joint Distribution, Barplots, Pairplots, Parallel co-ordinates, HeatMaps 9- “How to choose the right chart"

Python for Data Analysts - Quick Summary (1).pdf0.64 KB

+1
Machine Learning with LightGBM and Python (2023).pdf3.87 MB