mastering data analysis

The classic introductory books on data analysis were recommended as follows: " Python Data Analysis Basics ": This book is a classic in the field of data analysis in China. It mainly introduced the basic knowledge and common tools of Python data analysis, including data cleaning, data visualization, machine learning, etc. " Principles of statistics ": This book is a classic textbook in the field of statistics. It provides a comprehensive introduction to the basic concepts, principles, and methods of statistics, including probability theory, hypothesis testing, regress analysis, and analysis of variation. 3 " Data structure and algorithm analysis ": This book is a classic in the field of data structure and algorithm analysis. It mainly introduced the basic concepts of data structure, the design and analysis of algorithms, sorting algorithms, search algorithms, etc. 4 " R Language Practicals ": This book is an introductory textbook for the R language. It mainly introduced the basic concepts, grammar, and commonly used tools of the R language, including data visualization, statistical analysis, machine learning, and other aspects. The four books above were classic textbooks in the field of data analysis. They were of high reference value for beginners. However, it was important to note that data analysis was a broad field. The specific knowledge and skills needed to be learned still needed to be determined according to one's actual needs and interests.

Text data analysis refers to the extraction of useful information and patterns through processing and analyzing text data to provide support for decision-making. The following are some commonly used text data analysis methods and their characteristics: 1. Word frequency statistics: By calculating the number of times each word appears in the text, you can understand the vocabulary and keywords of the text. 2. Thematic modeling: By analyzing the structure and content of the text, we can understand the theme, emotion and other information of the text. 3. Sentiment analysis: By analyzing the emotional tendency of the text, we can understand the reader or author's emotional attitude towards the text. 4. Relationship extraction: By analyzing the relationship between texts, you can understand the relationship between texts, topics, and other information. 5. Entity recognition: By analyzing the entities in the text, such as names of people, places, and organizations, you can understand the entity information of people, places, organizations, and so on. 6. Text classification: Through feature extraction and model training, the text can be divided into different categories such as novels, news, essays, etc. 7. Text Cluster: By measuring the similarity of the text, the text can be divided into different clusters such as science fiction, horror, fantasy, etc. These are the commonly used text data analysis methods. Different data analysis tasks require different methods and tools. At the same time, text data analysis needs to be combined with specific application scenarios to adopt flexible methods and technologies.

To let the data tell the story, we have to be objective. We can start by looking at the data from different perspectives. For example, we can break it down by different categories such as age groups or geographical regions. When we present the data, we should use simple and clear language. Don't overcomplicate things with too much jargon. Let the patterns and trends in the data emerge naturally. We can also compare the data with historical data or industry benchmarks to give it more context. This way, the data can effectively tell its own story without being distorted by our biases.

Data analysis is a broad field that involves many different topics and skills. For beginners, the following are some recommended data analysis books: Python Data Science handbook: This book provides a detailed introduction to the Python programming language for data analysis. It included the basics of Python programming, the use of data processing and visualization tools, and the basics of machine learning algorithms. 2.<< Mathematical Principles and Practice >>: This is a classic statistics textbook suitable for readers with a foundation in statistics. It covered the basic concepts of statistics, hypothesis testing, regressions, and analysis of variation. 3 The R Programming Language: This book introduced the basics of R programming, the use of data processing and visualization tools, and the basics of machine learning algorithms. R was a widely used programming language in the field of statistics and data visualization. Data Analysis Basics: This book covers the basics of data analysis, data cleaning, data visualization, and statistics. It was suitable for beginners to help them get started in the field of data analysis. 5 Machine Learning in Action: This book introduced the basics of machine learning algorithms, supervised learning, unsupervised learning, and deep learning. It is suitable for beginners to understand the basic concepts and algorithms of machine learning. These are some beginner data analysis books that provide useful basic knowledge and practical tools to help beginners get started in the field of data analysis.

The characters included the male lead, Xia Qian, and the female lead, Mystery Q. "Elf: This guy's data analysis is ridiculous." Author: Zofee did not bring an umbrella tonight. It was a light novel/derivative Doujinshi novel with pets and elven elements. [User recommendation: The protagonist is a relatively eccentric elf versus an analyst. The background is a mix of various worldviews and reality. There's nothing too toxic about it.] It was just that the plot was sometimes a little deliberate. For example, he didn't choose the first prize but the second prize instead of selling the first prize to buy the flying feather ball for the second prize. The first elf insisted on having the Flying Element, which was a little extravagant. Moreover, the Normal Element and the Spirit Element could not fight each other, so it was not a reason to not want the Spirit Element. The plot in this aspect was not handled well. I hope you will like this book.

The analysis concept of big data mainly includes the following aspects: Data cleaning: Data cleaning is a very important step in the process of big data processing. It involves the guarantee of data quality and the improvement of data accuracy. The purpose of data cleaning was to remove errors, missing values, and outlier values in the data to make the data more stable and reliable. Data modeling: Data modeling refers to transforming actual data into a visual data model to better understand the relationships and trends between data. The purpose of data modeling was to predict future trends and results by establishing mathematical models. 3. Data analysis: Data analysis refers to the discovery of patterns, trends, and patterns in the data by collecting, sorting, processing, and analyzing the data. The methods of data analysis included statistical inference, machine learning, data mining, and so on. 4. Data visualization: Data visualization refers to transforming data into a form that is easy to understand and compare through charts and graphs. The purpose of data visualization was to help people better understand the data and make smarter decisions. Data integration: Data integration refers to the integration of multiple data sources into a single data set for better analysis and application. The purpose of data integration was to make the data more complete and unified so as to improve the efficiency of analysis and application. 6. Data exploration: Data exploration refers to the discovery of abnormal values, special values, and patterns in the data through data analysis. The purpose of data exploration was to provide the basis and clues for subsequent data analysis. 7. Data governance: Data governance refers to the process of processing and managing big data. The purpose of data governance is to ensure the integrity, reliability, security, and usefulness of data to improve the efficiency of big data processing and management.

Surprise is a key element. For example, when the data shows something completely unexpected like the ice - cream sales during full moons. Another is the human element. The actions or behaviors of people that lead to the strange data patterns, like the night - shift workers and their cat pictures.

Start by understanding the data thoroughly. Identify key patterns and trends. Then, find a compelling way to present them as a narrative.

If you like the male protagonist's ability to analyze data and reason, I highly recommend the following two novels: 1. "Heavenly Arithmetic Machine": The male protagonist of this novel often makes decisions through calculation and reasoning. For example, he can infer the winner and loser at the first moment he makes a move. In addition, this novel is also a novel about a different continent. If you are interested in this genre, you can also read it. 2. "The Psychologist": The heroine of this novel is good at detective reasoning and can also use psychological and sociological knowledge to make inferences. If you like mystery detective novels, this one is not bad either. I hope you like this fairy's recommendation. Muah ~😗

There were many books on e-commerce supply chain and data analysis that could be used as reference: 1 E-commerce supply chain management ( ·Bice and William Tait) 2 Introduction to Data Analysis (by Tapscott and Aaron Cooley) 3 E-Commerce Data Analysis (Hopkins and Emily Kelly) 4 Proficient in E-Commerce Data Analysis (Bezos and Larry Page) 5 Introduction to Data Mining (Pavel and Steve Parkes) 6 "The Real Combat of Data Analysis on Taobao's Lynx"( ) 7."The Real Combat of Data Analysis in Jingdong"(Ning) 8 Logistics and supply chain management: case analysis and research methods ( ) 9 "Big Data Era: E-commerce Operation and Data Analysis"(Jie) 10 E-Commerce Operation ( ) 11 E-commerce Operation and supply chain management ( ) 12 E-commerce Data Analysis and Mining ( ) 13 Taobao Operation Manual ( ) 14 "Jingdong Operation Manual"( ) 15.<<Lynx Operation Manual>>( ) The above are just some examples. The specific book selection still needs to be determined according to the needs and actual situation.