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授課地點及時間 |
上課地點:【上海】:同濟大學(滬西)/新城金郡商務樓(11號線白銀路站) 【深圳分部】:電影大廈(地鐵一號線大劇院站)/深圳大學成教院 【北京分部】:北京中山學院/福鑫大樓 【南京分部】:金港大廈(和燕路) 【武漢分部】:佳源大廈(高新二路) 【成都分部】:領館區1號(中和大道) 【廣州分部】:廣糧大廈 【西安分部】:協同大廈 【沈陽分部】:沈陽理工大學/六宅臻品 【鄭州分部】:鄭州大學/錦華大廈 【石家莊分部】:河北科技大學/瑞景大廈
開班時間(連續班/晚班/周末班):2020年3月16日 |
課時 |
◆資深工程師授課
☆注重質量
☆邊講邊練
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質量以及保障 |
☆
1、如有部分內容理解不透或消化不好,可免費在以后培訓班中重聽;
☆ 2、在課程結束之后,授課老師會留給學員手機和E-mail,免費提供半年的課程技術支持,以便保證培訓后的繼續消化;
☆3、合格的學員可享受免費推薦就業機會。
☆4、合格學員免費頒發相關工程師等資格證書,提升您的職業資質。 |
☆課程大綱☆ |
|
- Introduction
-
- 1. Why is People Analytics relevant today?
- a. Industry trends
- b. Where People Analytics are heading
- c. Key messages from People Analytics leaders
-
- 2. Introduction to the foundational pillars of People Analytics
- a. Workforce Planning Analytics Pillar
- b. Sourcing and Recruitment Analytics Pillar
- c. Acquisition/Hiring Analytics Pillar
- d. The other pillars and their relevance to Talent Analytics.
-
- 3. Key metrics
- a. Workforce planning – KBI whitebook review of large organization
- b. Talent Analytics - KBI whitebook review of large organization
- c. Sourcing analytics - KBI whitebook review of large organization
-
- 4. Workforce and Talent Acquisition Planning Analytics
- a. What Is Workforce Planning?
- b. Workforce Planning Analytics
- c. Why Should You Care About Workforce Planning Analytics?
- d. Key Components of Talent Acquisition Analytics
- e. Making an IMPACT with Workforce Planning Analytics
- f. Workforce Planning Analytics Best Practices: Dos and Don’ts
- g. Case studies review
-
- 5. Talent Sourcing Analytics
- a. The Business Case for Talent Sourcing Today
- b. Why You Need to Care about Your Talent Sourcing Today
- c. Talent Sourcing in the Era of Big Data and Advanced
- d. Technology
- e. The Mobile Impact on Talent Sourcing
- f. Putting the IMPACT Cycle into Action
- g. Case studies review
-
- 6. Talent Acquisition Analytics
- a. What Is Talent Acquisition Analytics?
- b. How Talent Acquisition Works
- c. Application Phase
- d. Preinterview Assessment Analytics
- e. Interviews: Separating the Wheat from the Chaff
- f. Putting It All Together: Predictive Analytics for Selection
- g. Case studies review
-
- 7. Summary
- a. Outlining next steps
- b. Commitment
- c. Support needed – where to find it--
-
- Summary and Conclusion
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