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Today is the era of data explosion. Even if you are not a programmer or analyst, you will inevitably deal with data at work. For example, product pricing, personalized recommendations, advertising, product design, retention improvement, etc., all need to extract effective information from massive data to assist decision-making.
Therefore, the ability to process and analyze data has become a very valuable skill and can greatly improve work efficiency.
data analysis tool
In the field of data analysis, Excel occupies an important position. That's because Excel is so ubiquitous that almost everyone uses it and it's easy to collaborate with others. Excel itself provides many calculation, analysis and chart display functions, which can quickly complete some simple data processing and analysis tasks.
However, as the amount of data increases and the data structure becomes more and more complex, sometimes using Excel alone to process data is less efficient or cannot fully meet the functional requirements of analysts. As an easy-to-use and feature-rich programming language, Python can be well combined with Excel to give full play to their respective advantages. It can not only use Python's powerful data analysis functions, but also use Excel's ease of use and data sharing. characteristics, so as to better complete the data analysis task.
In addition, Python can also handle many tasks such as data collection, file organization, and automated testing, so if you have data analysis and office automation needs, it is strongly recommended to learn directly from Python to achieve your goals faster.
Python common modules
Python has a rich tool library, among which Numpy, Pandas, Matplotlib, Scikit-Learn, etc. are commonly used in the field of data analysis.
Numpy - Provides array functionality, and functions for fast manipulation of data.
Pandas - The most powerful data analysis library in Python. Provides the functions of adding, deleting, checking, and modifying tabular data, and has rich data processing functions, and also supports data analysis functions.
Matplotlib - one of the most commonly used data visualization libraries in Python, including many functions for making charts.
Scikit-Learn - A machine learning library that provides a complete machine learning toolbox, including data preprocessing, classification, regression, clustering, prediction, and model analysis.
AI blessing
It is convenient to use Python for data analysis, but you still have to remember a lot of modules and functions, and you can only master it after a certain amount of continuous practice. This has also become the threshold for many people to analyze data with Python.
This situation has suddenly changed subversively this year, and that is the birth of ChatGPT. Some of the large AI models represented by it can already be helpful assistants when you learn programming and develop code. Therefore, you only need to understand the basic data analysis process and Python syntax, and you can write complex data analysis codes in a very short time.
If Excel provides wheels for data analysis, then Python adds engines to the wheels, and today's AI is directly equipped with rocket injectors. With the combination of these three, data collation, analysis, and visualization are no longer a cumbersome task. Master them and you can get home from get off work earlier!
Beginner's guide
Someone wants to ask: I don't even understand Python yet. If I want to do data analysis, where should I start?
Then you are asking the right person. Crossin's new book "Operation on Code: Using Python and ChatGPT to Efficiently Get Excel Data Analysis" is just right for you.
This book explains the ideas, methods and practical applications of processing and analyzing data from the perspective of the combined use of Python and Excel. Whether you are a learner who wants to engage in data analysis or an office worker in other occupations, you can master the skills of Python to analyze data through the study of this book.
The book consists of 12 chapters, covering data acquisition, data cleaning, data processing, data statistics, data visualization, etc. It is not only suitable for beginners to quickly grasp the basics, but also suitable for readers with certain experience to study in depth. The book innovatively introduces ChatGPT into teaching, uses ChatGPT to answer questions and provides practical training codes, and introduces some practical skills of using ChatGPT to assist learning, bringing a new way of learning to learners.
Features of this book:
Simple and easy to understand: It is explained through simple language and easy-to-understand cases, and it can be learned without relevant background knowledge.
Comprehensive system: The content is divided into three parts: entry, advanced, and actual combat, covering each process of data processing and analysis, and gradually deepening.
Rich cases: equipped with a large number of cases and data files, allowing readers to master skills through practical operations.
Expanded combination: combine data analysis with machine learning, so that readers can understand the application of data analysis in new technology fields.
AI-assisted: Apply ChatGPT, an AI cutting-edge product, to the learning process, and demonstrate how to use ChatGPT to assist learning and improve the efficiency of data analysis.
Readers and friends of the official account can contact me in the background after purchase and join the reader exchange group. Crossin will open the accompanying reading mode for you and answer all your questions when reading this book.
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