Advanced data analysis
1. Lean data analysis
This book shows how to test your ideas, find real customers, create products that can make money, and improve corporate visibility. Through the analysis of more than 30 cases, this paper shows how to apply six typical business methods to various thinking methods of planning lean entrepreneurship, data analysis and data-driven, and find the first key policy that enterprises need to supplement.
2. The beauty of mathematics
This book makes the profound mathematical principles more popular and understandable, and makes non-professional readers appreciate the charm of mathematics. What readers learn through concrete examples is the way of thinking-
How to simplify the complex, how to deal with engineering problems with mathematics, how to jump out of the inherent thinking and constantly consider innovation.
data mining
1. Introduction to data mining (non-uniform version)
This book comprehensively introduces data mining, including five topics: data, classification, correlation analysis, clustering and anomaly detection. Except for anomaly detection, each topic has two chapters. The former chapter includes the concept of root, representative algorithm and comment technology, and the latter chapter talks about high-end concepts and algorithms. In this way, readers can understand more important high-end topics while thoroughly understanding the roots of data mining.
2. The concept and technology of data mining
This book comprehensively describes the concept, method, technology and latest research of data mining. This book has completely revised the first two editions, strengthened and organized the technical content of the whole book from the beginning, talked about data preprocessing, repeated mining, classification and clustering, comprehensively described OLAP and outlier detection, and discussed mining networks, messy data types and important application fields.
3. Data Mining and Digital Operation: Ideas, Methods, Skills and Applications
At present, the works on data mining in data operation practice are comprehensive and systematic, and it is also one of the few works in most data mining books that span multiple practical application cases and scenarios. It is also a work that creatively introduces a set of corresponding analysis ideas and corresponding analysis skills integration for different types of analysis and mining topics in data operation, providing readers with "menu-style" practical tips.
As a data analyst, if you are only content with the status quo and don't pay attention to self-improvement, then in the near future, you may become the company's "human flesh" data retrieval machine, which will affect your future work and life.
The above are the recommended reading books on improving data analysis ability compiled and shared by Bian Xiao today, hoping to help you. Bian Xiao believes that in order to make achievements in the big data industry, it is necessary to obtain some data analyst certificates with high gold content and keep learning, so as to have more core competitiveness and competitive capital.