There is a process of learning any skill from shallow to deep, and data analytics is no exception. A complete data analysis knowledge system is similar to the pyramid structure: the top layer is the perception of the value of data, business understanding, the middle is the data analysis methodology, and the bottom layer is the solution or specific operation method of data analysis. I have divided the recommended books for data analysis into three segments to facilitate progressive learning.
Introduction to Data Analytics
The introductory edition is suitable for those who are new to data analytics, those who don't have a holistic concept of data analytics, and is commonly used by fresh graduates, and those who are switching to a new profession with little experience.
Recommended Books for Introductory Edition
Deep and Shallow Data Analytics: one of the HeadFirst (Deep and Shallow) series of books published by O'Relly, the book has a large number of pictures and an interesting combination of cases. The book's easy-to-understand images and vivid images will enable the beginner to have a comprehensive knowledge of the concepts of analytics.
Who Says Rookies Can't Analyze Data: This book describes in detail the basic methodology and process of data analysis, and illustrates case studies using Excel tables as examples. At the same time, the book also introduces the significance of data analysis in the workplace, which can help the workplace whites quickly get started.
"Naked Statistics": the author was a pursuit of learning when he was young, and then he himself discovered a lot from statistics can be applied to life. This is the main theme of the book, combined with life to explain statistical knowledge, lively and interesting. It can avoid the statistics come up on the big talk of Bayesian probability and stochastic analysis of the boring.
Also similar books are "Statistics Lie", which is a little more well-known, through the exposure of "false numerical information" to help people understand the underlying statistical principles.
Advanced Data Analytics
The advanced version is industry-specific, requiring analysts to have a certain level of general knowledge and business understanding of data analytics; it's suitable for web analysts, business analysts, and data product managers, among others.
Recommended Books for Advanced Edition
Mastering web analytics 2.0: Analytics combines clickstream web analytics tools with qualitative data, tests and experiments, and competitive intelligence tools to derive detailed web strategies and operational-level scenarios. This book is old, but many of the ideas and traffic analysis cases are still very useful, and now you can only buy used copies of old books in China.
Similar to this kind of "Web Analytics in Action", is a domestic book on Web analytics, not the above classic, but wins in the new out, many cases and ideas have timely updates.
"Statistics in depth": with the above "data analysis in depth" belongs to the Headfirst series of books, the use of full of interactive real-world plot, to help readers quickly understand the theoretical knowledge of statistics.
"Data-based Management": Huang Chengming, explaining the application of data in the enterprise examples, read the benefit of a lot of examples are very grounded. Although it is biased toward retail management, but the road to one, can be applied to many industries, at that time based on the concepts inside the planning of the BD-oriented Meituan takeaway data products.
MySQL Must Know: This is also my introduction to SQL, a thin booklet that looks very fast, SQL is a very cost-effective skill, simple but powerful. Any product/operations/analysts who want to further improve their data analytics skills are advised to light up this skill point.
The First Data Analytics Handbook for Internet Growth: Our company's data analytics handbook is all about growth. This manual introduces the growth methodology of Internet startups, common methods of Internet data analysis (trend, conversion, retention, real-time, subgroups, granular checks, heatmaps), and applications in niche industries (such as SaaS, Internet finance, e-commerce, etc.).
Data Analytics Advanced Edition
Higher-order data analytics is relatively more professional, such as involving internal data governance, data combined with business analysis, data visualization and so on. Of course, there are data mining algorithms and other more in-depth things, this piece of research is not blindly recommended.
Recommended books for advanced edition
"Battle for Big Data": Alibaba's former vice president of data, Mr. Che Pinjue, explains Alibaba's insights in the process of internal corporate governance data, the said "store - through - with" data management three axes and "from data to operation The three-pronged approach to data management and the move from data-based operations to data-based operations can be a valuable reference.
Lean Data Analytics: The advantage of this book is that it divides enterprises into several large industry categories, and explains the characteristics of each industry's business model and analytical skills, which requires high analytical ability of the user, and the user must have the corresponding business knowledge.
"The Wall Street Journal Guide to Information Graphics", the Wall Street Journal is responsible for business analysis of people do visualization guide, the essence and practical, I have previously written a book note "The Wall Street Journal is to do data visualization", can be referred to.
The data warehouse classic tutorial: someone online to organize the information, the advantage is simple and clear, unlike the normal data warehouse textbook thick book.
Of course, data analytics is a very deep study, and I have only glimpsed the tip of the iceberg. To do a good job of data analysis must have a variety of skills: the need to see the value of data and understand the business, the need to familiarize themselves with the methodology of data analysis, but also to master the operation of data analysis software. While studying the above recommended books on data analytics, you can continue to deepen your understanding in practice and use data to drive business and customer growth.