While the rating tells you how good a book is according to our two core criteria, it says nothing about its particular defining features. Therefore, we use a set of 20 qualities to characterize each book by its strengths:
Applicable – You’ll get advice that can be directly applied in the workplace or in everyday situations.
Analytical – You’ll understand the inner workings of the subject matter.
Background – You’ll get contextual knowledge as a frame for informed action or analysis.
Bold – You’ll find arguments that may break with predominant views.
Comprehensive – You’ll find every aspect of the subject matter covered.
Concrete Examples – You’ll get practical advice illustrated with examples of real-world applications or anecdotes.
Controversial – You’ll be confronted with strongly debated opinions.
Eloquent – You’ll enjoy a masterfully written or presented text.
Engaging – You’ll read or watch this all the way through the end.
Eye opening – You’ll be offered highly surprising insights.
For beginners – You’ll find this to be a good primer if you’re a learner with little or no prior experience/knowledge.
For experts – You’ll get the higher-level knowledge/instructions you need as an expert.
Hot Topic – You’ll find yourself in the middle of a highly debated issue.
Innovative – You can expect some truly fresh ideas and insights on brand-new products or trends.
Insider’s take – You’ll have the privilege of learning from someone who knows her or his topic inside-out.
Inspiring – You’ll want to put into practice what you’ve read immediately.
Overview – You’ll get a broad treatment of the subject matter, mentioning all its major aspects.
Scientific – You’ll get facts and figures grounded in scientific research.
Visionary – You’ll get a glimpse of the future and what it might mean for you.
Well structured – You’ll find this to be particularly well organized to support its reception or application.
恭喜你又学完了一篇干货！复述、评论及做笔记是对知识最大的致敬↓
大数据讲究什么，
全量分析而非增量分析，
高效分析而非精确分析，
相关分析而非因果分析
趋势也是建立在观察和分析基础上，也是对全量分析，而非仅仅对增量分析。因为全量分析，而非抽样分析，对结果的精确度要求就没有那么高， 仅仅分析是否存在相关关系，而非存在因果关系，最终目的是获取对用户行为意愿的分析。
趋势分析需要趋势假设，案例收集， 反例分析，趋势分析， 趋势定义， 趋势验证， 趋势使用。
趋势假设可以存在， 然后再去收集案例，这是自上而下的获取案例。
但是不假设分析，而仅仅从案例开始， 逐步的建立假设， 并逐步修正，自下而上可能是建立趋势假设的更好方法。
趋势分析和大数据最显著的区别在于对案例采集的范围和规模无穷无尽，需要广泛的好奇心和想象力，以及分析能力。