I believe that everyone living in modern society is not unfamiliar with the word "data", and it is too closely connected with our daily lives. The popularity of computers and the advent of the big data era have also made data play an increasingly important role in our lives, and "data analysts" have become a hot profession.
However, what exactly is data analysis? What do data analysts need to master? What is the growth path of data analysts? Have you learned data analysis tools and data visualization and mastered the legendary "dragon-slaying skills"?
With these questions, Brother Ka began his writing journey of "Data Analysis Brother Ka Ka's Ten Chapters: From Thinking to Practice to Promoting Operational Growth" .
▲ Use data to promote growth
This is a "scenario-based practical operation guide", and Brother Ka connected technology with reality during the writing process. Whether you are a beginner who just plans to learn about data analysis or a veteran who has advanced to a senior data analyst, you can find what you need in this book!
01
combined with business scenarios to carry out data analysis and learning
In the understanding of many people, learning data analysis is a purely knowledge-oriented. For example, you need to learn data analysis tools, such as the syntax of Python "Three Musketeers" (Pandash, NumPy and Matplotlib) (such as various algorithms for data mining and machine learning).
This is of course true, "If you want to do a good job, you must first sharpen your tools." But such learning has obvious disadvantages. For example, your learning process may be very boring and your learning effect will also be superficial. This is because the data itself is actually worthless, and 's value exists in the application scenarios of data . Only by starting from the business scenario and finding content that is guiding operations is the basic principle of data analysis.
With such a principle consensus, you will probably understand why Brother Ka insists that only "combined with business scenarios" can truly deeply explore and learn the essence and connotation of data . Only by fully practicing in the scenario can the value of the tool be maximized, the connotation of knowledge and data can we truly understand the content of the tool, and effectively apply the tool to the business scenarios, and finally learn to learn from one example and apply it to other aspects.
Many veterans of the "pure knowledge school" often rely too much on tools and knowledge, and are prone to encounter difficulties in their work, and they will also face insurmountable bottlenecks at a certain stage. The reason is mostly because they have not integrated the three parties of "tools, knowledge and application".
"If you don't accumulate small steps, you can't reach a thousand miles; if you don't accumulate small flows, you can't become a river and a sea." Data analysis is a long-term accumulation work and cannot be achieved overnight. Only by accumulating and learning in specific scenarios of business practice can you advance to a qualified data analyst.
02
"Hardcore and interesting", data analysis reference book
"
Every book should be interesting.
—Wang Xiaobo
—Wang Xiaobo
"
Especially for learning reference books, it should be written more interestingly. After all, the learning process of breaking through the comfort zone is accompanied by pain. Even Mr. Liang Shiqiu said, "It is normal for us to be ordinary people."
For this reason, Brother Ka changed the serious normal of "textbook style" and was entertaining. Through "Data Analysis Brother Ka Ka's Ten Chapters: Promoting Operational Growth from Thinking to Practice", he led everyone to "learn" data seriously and "play" data happily.
In this book, there are both hard-core knowledge points and vivid stories.
(1) Hard core knowledge points : The whole book revolves around "data analysis" and "Operational growth" has two key elements. While systematically introducing basic knowledge such as data analysis thinking, data analysis methods, data collection skills, and data cleaning skills, it is problem-oriented and interprets the key business content of operation and growth, and conducts data analysis practice in various core operation links such as customer acquisition, activation, retention, monetization, and self-propagation cycle...
(2) vivid story : Starting from user stories and specific problems, Ka Ge takes readers step by step to learn from the emergence of problems to theoretical analysis, to the introduction and use of tools, and to the solution of problems, he implements every knowledge point in a solid manner, and explains advanced and useful skills in simple language and examples...
Only learning programming languages and data analysis tools is inevitable. Only by combining business scenarios and user stories can readers learn happier and master them more efficiently!
03
Respect the data, and respect the truth behind the data
"
Everything that cannot be measured by indicators cannot be managed (If you can't measure it, you can't manage it).
— Peter Drucker
”
human subjective cognition will always deviate, but the data will not lie.
For companies at all stages of entrepreneurship, development and business, they should carefully analyze the status quo, correctly understand the actual situation of their own development, determine reasonable goals, not vainly pursue data, not cheat, not be eager for quick success or instant profit, and not pursue rapid growth on paper. Otherwise, no matter how good the company has data without foundation, it may be just a flash in the pan.
For individuals working in the field of data analysis, on the one hand, being able to see the "secrets" implicit in the data through data analysis, mining the "value" in the data, presenting this value with beautiful charts, and thus making suggestions, driving the company's business growth and bringing considerable increments and profits to the company, is the greatest ability that an excellent data analyst should have.
On the other hand, respects data, and also respects the truth behind data . This is the greatest courage a data analyst should have. If one day you find that the carefully prepared data analysis report does not always match the results you expect, please do not forcefully use data to explain the results, or deliberately ignore certain factors that should not be ignored. Instead, we should respect the facts from beginning to end, start from facts, find the root cause, and find the shortcomings.
You must have the courage to accept data analysis is not a "splenishment", and you must also dare to find out the truth hidden behind the data.
04
How to play with data from thinking to practice?
Write this, you feel that the data teaching method of "Brother Ka" is very different from the general learning process! ! ! Come and learn about this new book from Accenture senior data consultant Huang Jia, "Ten Chapters of Data Analysis, from Thinking to Practice to Promote Operational Growth". The way of writing this book
is refreshing, and its practicality makes data analysts amazed. Many data analysis experts commented after reading:
·When reading this book, there is a sense of relaxed and pleasant feeling of "copying homework", which is really comfortable to read.
·If I read Teacher Huang’s book early, it will reduce the pain of exploratory learning at the beginning.
This book has the following characteristics:
First of all, this book is based on the actual combat of data analysis projects.The content structure in the book is arranged from the overall perspective to the specific situation, ensuring that you know how to go every step. Basics come first, master the data analysis skills modules at the macro level; in-depth practice, and experience the entire business process from beginning to end.
The setting of this book is problem-oriented, and use cases to take you step by step to solve the actual business.
3. Cultivate data thinking
This book not only contains practical business cases, but also includes important methods of data analysis, logical thinking models, and AARRR framework. The whole book revolves around all practical links in the operational framework of AARRR.
4. The code is hard-core and lively
This book not only focuses on theory, thinking, practical processes and methods. The quality of the relevant supporting codes is also the warmest.
Teacher Huang Jia's previous book "Zero-Basic Learning Machine Learning" , also has the protagonist of Ka Ge. It has been widely loved by readers for more than a year since its publication. It has been reprinted 7 times. Douban score is as high as 9.1 points . As an introductory book, it is a masterpiece. The book "Ten Episodes of Data Analysis" follows the witty, humorous and relaxed style, and takes the writing style to a higher level, integrating data analysis technology into stories and practical practice, and combining the two is more clever.
▲ A good book for machine learning
See this, do you want to play with data with Brother Ka? Such a high-quality book for data analysis and operation is worth buying.
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