{"product_id":"110267","title":"Think with data and lead with data in the era of AI upheaval. ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYEEOLlmVmOJMgXIFgCunM46zo4hZ.png?v=1765098294\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e In the age of disruptive AI, think with data and lead with data. \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/126167769\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd\u003e\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eIs your organization making good use of all that dusty data?\u003cbr\u003e You can't discuss technology and business in the AI ​​era without knowing data and statistics!\u003cbr\u003e In a world full of volatility, let's find the patterns behind it!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e It lifts the curtain behind data science and provides “the knowledge and know-how to think, speak, understand, and act critically about data.”\u003cbr\u003e From understanding the tendencies of organizational members to the mathematical principles behind algorithms, this book condenses everything about data and statistics used in practice. \u003cbr\u003eThis book will help you master the analytical tools, terminology, and mindset necessary to successfully navigate the data science business, while also providing a deeper understanding of challenging data-related problems.\u003cbr\u003e Through learning, you will be able to think critically about data and analytics, and express your opinions intelligently on all things data-related.\u003cbr\u003e\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e[Part 1] Your First Journey to Thinking and Leading with Data\u003cbr\u003e\u003cbr\u003e Chapter 1: What's the Problem?\u003c\/b\u003e\u003cbr\u003e _Questions Data Leads Must Ask\u003cbr\u003e ___Why is this issue important?\u003cbr\u003e ___Who does this problem affect?\u003cbr\u003e ___What to do if there is no appropriate data\u003cbr\u003e When will the ___ project end?\u003cbr\u003e ___What should I do if I am not satisfied with the results?\u003cbr\u003e Why did the data project fail?\u003cbr\u003e ___Customer Awareness\u003cbr\u003e ___Things to think about \u003cbr\u003e_Let's focus on what matters\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2: What is Data?\u003c\/b\u003e\u003cbr\u003e _Data vs. Information\u003cbr\u003e ___Example dataset\u003cbr\u003e _Data type\u003cbr\u003e How is data collected and formatted?\u003cbr\u003e Observational data vs. experimental data\u003cbr\u003e ___Structured data vs. unstructured data\u003cbr\u003e _Basic summary statistics\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3: Get Ready for Statistical Thinking\u003c\/b\u003e\u003cbr\u003e _Let's ask a question\u003cbr\u003e _Everything is subject to change\u003cbr\u003e ___Customer Awareness Scenario (Sequel)\u003cbr\u003e ___Case Study: Kidney Cancer Incidence\u003cbr\u003e _Probability and Statistics\u003cbr\u003e ___Probability vs. Intuition\u003cbr\u003e ___Discoveries using statistics\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 2] Attitudes toward data, knowledge of probability and statistics\u003cbr\u003e\u003cbr\u003e Chapter 4: Let's Argue with Data\u003c\/b\u003e\u003cbr\u003e _What would you do if you were me?\u003cbr\u003e ___The disaster caused by missing data\u003cbr\u003e _Let's check the source of the data\u003cbr\u003e ___Who collected the data\u003cbr\u003e ___How was the data collected?\u003cbr\u003e Is the data representative?\u003cbr\u003e Was there any bias in the sampling?\u003cbr\u003e How did you handle ___outliers? \u003cbr\u003e_What is unverified data?\u003cbr\u003e ___How did you handle missing values?\u003cbr\u003e ___Is the data capable of measuring the concept you are trying to measure?\u003cbr\u003e _Let's argue with all data, regardless of size\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5: Let's Explore the Data\u003c\/b\u003e\u003cbr\u003e Exploratory data analysis of data leads\u003cbr\u003e The need for exploratory thinking\u003cbr\u003e __What questions should I ask?\u003cbr\u003e ___Virtual Scenario\u003cbr\u003e _Can data answer your questions?\u003cbr\u003e ___Set expectations and think sensibly\u003cbr\u003e ___Is this a data value that can be intuitively understood?\u003cbr\u003e ___Manage outliers and missing values ​​well\u003cbr\u003e _What relationships do you see in the data?\u003cbr\u003e Let's understand the ___ correlation\u003cbr\u003e ___Be careful not to misunderstand the correlation.\u003cbr\u003e ___Correlation does not imply causation\u003cbr\u003e _Have you found new exploration opportunities in your data?\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6: What is Probability?\u003c\/b\u003e\u003cbr\u003e _Let's guess\u003cbr\u003e _The Rules of the Game\u003cbr\u003e ___Mathematical notation\u003cbr\u003e Conditional probability and independent events \u003cbr\u003e___Probability of occurrence of various events\u003cbr\u003e ___two events occurring simultaneously\u003cbr\u003e _Thought Experiments on Probability\u003cbr\u003e 3 Checkpoints on ___ Probability\u003cbr\u003e _Be careful when assuming that events are independent of each other.\u003cbr\u003e Don't fall into the gambler's fallacy\u003cbr\u003e _Remember that all probabilities are conditional probabilities.\u003cbr\u003e Let's not change the ___dependence relationship\u003cbr\u003e ___Bayes' theorem\u003cbr\u003e _Make sure that the probability is meaningful\u003cbr\u003e ___correction\u003cbr\u003e ___even if the possibility is slim, events happen\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7: Let's Challenge Statistics\u003c\/b\u003e\u003cbr\u003e _What is statistical inference?\u003cbr\u003e ___Leave room for error\u003cbr\u003e ___The more data there is, the more evidence there is.\u003cbr\u003e ___Let's question the current situation\u003cbr\u003e ___Is there any evidence to the contrary?\u003cbr\u003e ___Balance judgment errors\u003cbr\u003e _Statistical inference process\u003cbr\u003e _Questions needed to verify statistical analysis results\u003cbr\u003e ___In what context are the statistical analysis results derived?\u003cbr\u003e ___What is the sample size?\u003cbr\u003e ___What is being verified\u003cbr\u003e ___What is the null hypothesis?\u003cbr\u003e What is the level of ___ significance? \u003cbr\u003e___How many times have you verified it?\u003cbr\u003e Can you provide a confidence interval?\u003cbr\u003e ___Is this a practically meaningful result?\u003cbr\u003e Are you assuming a causal relationship?\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 3] Relearning Machine Learning, Deep Learning, and AI Knowledge through Various Case Studies\u003cbr\u003e\u003cbr\u003e Chapter 8: Machine Learning: Finding Hidden Patterns and Groups in Data\u003c\/b\u003e\u003cbr\u003e _What is unsupervised learning?\u003cbr\u003e _Dimension reduction\u003cbr\u003e ___Create a complex variable\u003cbr\u003e _Principal component analysis\u003cbr\u003e ___Principal components of exercise ability data\u003cbr\u003e ___Principal Component Analysis Summary\u003cbr\u003e ___Traps to watch out for\u003cbr\u003e _Cluster analysis\u003cbr\u003e _k-means cluster analysis\u003cbr\u003e ___Retail store cluster analysis\u003cbr\u003e ___Traps to watch out for\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9: Regression Models for Predicting the Future and Explaining Phenomena\u003c\/b\u003e\u003cbr\u003e _Supervised learning\u003cbr\u003e What does linear regression do?\u003cbr\u003e ___least squares regression (not just for its fancy name)\u003cbr\u003e _What we can learn from linear regression\u003cbr\u003e ___When more variables are input\u003cbr\u003e _The confusion caused by linear regression\u003cbr\u003e ___Missing variables\u003cbr\u003e ___Multicollinearity\u003cbr\u003e ___data leak\u003cbr\u003e ___Extrapolation error\u003cbr\u003e ___Most relationships are not linear \u003cbr\u003e___Explain or Predict\u003cbr\u003e Performance of the ___regression model\u003cbr\u003e _Other regression models\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10: A classification model that can identify the criteria for judgment\u003c\/b\u003e\u003cbr\u003e _What is a classification problem?\u003cbr\u003e ___Three methods of classification models\u003cbr\u003e ___Classification problem setting\u003cbr\u003e _Logistic regression\u003cbr\u003e ___Advantages of Logistic Regression\u003cbr\u003e _Decision tree\u003cbr\u003e _Ensemble model\u003cbr\u003e ___Random Forest\u003cbr\u003e ___Gradient Boosted Tree\u003cbr\u003e ___Explanatory power of ensemble models\u003cbr\u003e _Beware of common traps\u003cbr\u003e Applying a model that does not fit the ___ data type\u003cbr\u003e ___data leak\u003cbr\u003e ___Dataset partitioning for model building and testing\u003cbr\u003e ___Choosing appropriate thresholds for decision making\u003cbr\u003e _Misconceptions about accuracy\u003cbr\u003e ___Confusion matrix\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11: Text Analysis: Identifying themes and emotions contained in text\u003c\/b\u003e\u003cbr\u003e _Expectations for text analysis\u003cbr\u003e _How to convert text to numbers\u003cbr\u003e ___word bag\u003cbr\u003e ___N-gram\u003cbr\u003e ___word embeddings\u003cbr\u003e _Topic Modeling\u003cbr\u003e _Text classification\u003cbr\u003e ___Naive Bayes\u003cbr\u003e ___Sentiment Analysis \u003cbr\u003e_Practical issues to consider in text analysis\u003cbr\u003e ___Big Tech's technological edge\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 12: Deep Learning and AI: What Data Leaders Need to Know\u003c\/b\u003e\u003cbr\u003e _Neural network model\u003cbr\u003e ___In what ways are neural networks similar to the human brain?\u003cbr\u003e ___Simple neural network model\u003cbr\u003e ___How Neural Networks Learn\u003cbr\u003e ___A slightly more complex neural network\u003cbr\u003e _Deep Learning Application Cases\u003cbr\u003e ___Advantages of Deep Learning\u003cbr\u003e ___How computers 'see' images\u003cbr\u003e ___Convolutional Neural Network\u003cbr\u003e ___Deep learning for language processing and sequential data\u003cbr\u003e _Practical Applications of Deep Learning\u003cbr\u003e Is ___ data sufficient?\u003cbr\u003e ___Is the data structured?\u003cbr\u003e What does a ___ neural network look like?\u003cbr\u003e _Perspectives on AI\u003cbr\u003e ___Big Tech's Advantageous Position\u003cbr\u003e ___Ethical Issues of Deep Learning\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 4] What Data Leads Should Do for Project and Organizational Success\u003cbr\u003e\u003cbr\u003e Chapter 13: Failures and Traps Lurking Everywhere\u003c\/b\u003e\u003cbr\u003e _Data Bias and Strange Phenomena\u003cbr\u003e ___survivorship bias\u003cbr\u003e ___regression to the mean\u003cbr\u003e ___Simpson's Paradox\u003cbr\u003e ___Confirmation bias \u003cbr\u003e___Sunk cost fallacy\u003cbr\u003e ___algorithm bias\u003cbr\u003e ___Other biases\u003cbr\u003e _Typical pitfalls of data projects\u003cbr\u003e ___Statistics and Machine Learning Pitfalls\u003cbr\u003e ___Project Trap\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 14: Understanding the Diverse Tendencies of Organizational Members\u003c\/b\u003e\u003cbr\u003e 7 Situations When Communication Breaks Down\u003cbr\u003e ___Postmortem visit\u003cbr\u003e ___a presentation without substance\u003cbr\u003e ___Spread of inaccurate information\u003cbr\u003e ___into the swamp\u003cbr\u003e ___Reality Check\u003cbr\u003e ___seize power\u003cbr\u003e ___a braggart..\u003cbr\u003e _Three Attitudes People Have When It Comes to Data\u003cbr\u003e ___Data Blindman\u003cbr\u003e ___Data pessimist\u003cbr\u003e ___Data Lead\u003cbr\u003e _organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 15: Towards a Higher Place\u003c\/b\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4484\/MidCate001\/448300145.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eInto the book\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e It's an industry that exploits businesses' fears of missing out by promising certainty in an uncertain world.\u003cbr\u003e We call this the 'data science business'.\u003cbr\u003e\u003cbr\u003e --- p.38\u003cbr\u003e \u003cbr\u003eThe stock market fluctuates daily, political polls change weekly (and sometimes even depending on the polling agency), gasoline prices fluctuate, and even though your blood pressure may spike when you get it checked by a doctor, it's normal when you get it checked by a nurse.\u003cbr\u003e If you measure your commute in seconds, it will vary slightly from day to day depending on various factors such as traffic, weather, your child's school commute, and getting your morning coffee out.\u003cbr\u003e There is volatility in everything in the world.\u003cbr\u003e How comfortable are you with this phenomenon?\u003cbr\u003e --- p.91\u003cbr\u003e\u003cbr\u003e In casinos, the bags containing the marbles are carefully designed so that samples can be extracted continuously.\u003cbr\u003e But politicians never know what's in the bag until all the marbles (i.e. the voting results) are revealed on Election Day.\u003cbr\u003e\u003cbr\u003e --- p.99\u003cbr\u003e \u003cbr\u003eWhen checking a politician's approval rating, if a survey is conducted only targeting supporters of a specific party, the data collected in this way will be subject to sampling bias.\u003cbr\u003e Only well-designed experiments can reduce the risk of potential sampling bias.\u003cbr\u003e\u003cbr\u003e --- p.121\u003cbr\u003e\u003cbr\u003e Even a child knows that the odds of a coin flip are 50-50, but the 2016 presidential election proved difficult to predict, even with the entire polling industry analyzing terabytes of data.\u003cbr\u003e\u003cbr\u003e --- p.149\u003cbr\u003e\u003cbr\u003e A debtor's default is not independent of his neighbor's default, but for a long time Wall Street financiers overlooked this fact.\u003cbr\u003e Both events are fundamentally intertwined with the global economic situation.\u003cbr\u003e\u003cbr\u003e --- p.158\u003cbr\u003e\u003cbr\u003e The false assumption of independence underestimates the likelihood that all projects will fail in the following year, and consequently overestimates the likelihood that at least one project will succeed. \u003cbr\u003eAs the 2008 financial crisis and subsequent economic downturn demonstrated, we must not forget the importance of independent assumptions.\u003cbr\u003e\u003cbr\u003e --- p.159\u003cbr\u003e\u003cbr\u003e I hope you understand clearly that computers cannot understand language like humans, and that to a computer, language is just numbers.\u003cbr\u003e I think just knowing this fact has tremendous value.\u003cbr\u003e If you simply understand that the process of converting text to numbers removes some of the meaning humans assign to words and sentences, you'll never be fooled by marketing claims that AI can solve all your text-related business problems.\u003cbr\u003e\u003cbr\u003e --- p.284\u003cbr\u003e\u003cbr\u003e It's important to note that algorithmic bias, no matter how well-intentioned (or neutral), can and does occur everywhere.\u003cbr\u003e No model's predictions can tell us the ultimate truth.\u003cbr\u003e Because all results using models are a product of assumptions.\u003cbr\u003e\u003cbr\u003e --- p.321\u003cbr\u003e \u003cbr\u003eFor data pessimists, their personal experiences are more important than data science, statistics, or machine learning.\u003cbr\u003e So they are cynical about the role of data workers.\u003cbr\u003e They view data as a nuisance but an unavoidable element, preferring intuition.\u003cbr\u003e If they are not satisfied with the result, they are more likely to find flaws and overemphasize details rather than offer constructive criticism.\u003cbr\u003e Let's think about why they are so cynical.\u003cbr\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e --- p.338\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e| What this book covers |\u003c\/b\u003e\u003cbr\u003e - Attitude and skills toward data for statistical thinking\u003cbr\u003e - Variability that affects daily life and decision-making processes\u003cbr\u003e - Data literacy skills to provide appropriate opinions on statistics and analysis results in the field.\u003cbr\u003e - The fundamental principles and knowledge behind machine learning, text analysis, deep learning, and AI.\u003cbr\u003e - Traps that are easy to fall into when analyzing and interpreting data \u003cbr\u003e- What a data lead must do to ensure the success of projects and organizations.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e| Target audience for this book |\u003c\/b\u003e\u003cbr\u003e This book is fun to read and provides valuable insights for anyone, including novice data scientists, data analysts, business professionals, AI\/machine learning engineers, and corporate executives.\u003cbr\u003e This is especially true for marketing professionals who need to work with data analysts, developers, employees, or researchers who are not yet familiar with data, C-level executives who need deeper knowledge of data to introduce new AI technologies and make decisions, and managers who need to lead data teams or organizations.\u003cbr\u003e This is a must-read for anyone who wants to work in the data field or grow into a data leader.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003e| Structure of this book |\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 1: Your First Journey to Thinking and Leading with Data\u003c\/b\u003e\u003cbr\u003e Part 1 covers how to think from a data lead perspective. \u003cbr\u003eLearn how to critically review data projects your organization is undertaking and ask appropriate questions.\u003cbr\u003e We'll explore the definition of data, the correct use of terminology, and how to view the world from a statistical perspective.\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 2: Attitudes toward data, knowledge of probability and statistics\u003c\/b\u003e\u003cbr\u003e Data leads actively participate in important discussions about data.\u003cbr\u003e In Part 2, we'll explore how to argue with data and what questions you need to ask to understand the statistical concepts you encounter in your work.\u003cbr\u003e You will learn the basic statistical and probability concepts needed to understand or address data analysis results.\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 3: Relearning Machine Learning, Deep Learning, and AI Knowledge through Various Case Studies\u003c\/b\u003e\u003cbr\u003e Data leaders must understand the fundamental principles of how statistical and machine learning models work. \u003cbr\u003eYou will gain an intuitive understanding of unsupervised learning, regression, classification, text analysis, and deep learning.\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 4: What Data Leads Do to Ensure Project and Organizational Success\u003c\/b\u003e\u003cbr\u003e Data leads should be aware of common mistakes and pitfalls when working with data.\u003cbr\u003e We explore the technical pitfalls that lead organizations and projects to failure, and learn about the people involved in data projects and their tendencies.\u003cbr\u003e Finally, we will provide guidance on how to succeed as a data leader.\u003cbr\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e \u003cb\u003e[Author's Note]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e We believe many people want to learn about data properly, but don't know where to start.\u003cbr\u003e The numerous data science and statistics books already published cover a very diverse spectrum. \u003cbr\u003eOn one side of the spectrum, there are numerous non-technical books that extol the benefits and hopeful outlook of data utilization.\u003cbr\u003e There are some good books among them.\u003cbr\u003e However, no matter how good a book is, it is often a book that deals with business that is biased towards the present.\u003cbr\u003e Most of the books are written by journalists to highlight the dramatic aspects of the rise of data.\u003cbr\u003e\u003cbr\u003e These books explain how specific business problems are solved through the lens of data. Terms like AI and machine learning often appear.\u003cbr\u003e Here, I hope there is no misunderstanding.\u003cbr\u003e Books like this have helped raise people's awareness of how to use data.\u003cbr\u003e However, it does not delve deeply into the specific tasks to be performed, but simply focuses on the big picture problems and solutions.\u003cbr\u003e\u003cbr\u003e At the other end of the spectrum are high-level technical books. \u003cbr\u003eMost of them are heavy books with over 500 pages, so they are not only physically demanding but also mentally taxing.\u003cbr\u003e\u003cbr\u003e There are mountains of books at both ends of the spectrum.\u003cbr\u003e The communication gap is not easily narrowed because most people only read business or technical books.\u003cbr\u003e Fortunately, there are some excellent books between these two extremes.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e We'd like to add our book to this list.\u003cbr\u003e The book you are reading can be read by anyone without any burden, even without a computer or notepad nearby.\u003cbr\u003e If you enjoyed our book, I highly recommend reading at least one of the two books mentioned above to further solidify your understanding of data.\u003cbr\u003e You won't regret it.\u003cbr\u003e\u003cbr\u003e Our authors really like this book. \u003cbr\u003eIf this book motivates you to learn about data and data analysis and sparks a desire to learn more, then I consider that a success.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Translator's Note]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e I've read, studied, and sometimes translated many books on data, but this book is so great that I wish I had written it myself instead of translating it.\u003cbr\u003e When I first received the original book and skimmed through the chapter titles, I thought, \"Isn't this content too easy?\" However, from the moment I started reading it in earnest, savoring each sentence in preparation for the translation, until the very end of the last chapter, I couldn't help but be impressed by the authors' efforts to write in line with the book's planning intentions, and their deep expertise in data analysis and statistics.\u003cbr\u003e \u003cbr\u003eIt is often said that “the easiest thing to write is the hardest.”\u003cbr\u003e I've always rationally agreed with this statement, but I've rarely experienced a concrete example. After reading this book, I felt like I'd finally encountered a true example of what it means.\u003cbr\u003e 'Easy to write' means that the writer has a perfect grasp of the core and logic of the content, which allows for writing that is easy, clear, and logical.\u003cbr\u003e\u003cbr\u003e This book covers just the right depth and breadth of data analysis and statistics, which can be challenging.\u003cbr\u003e It's a great introductory book for those considering a career in this field, but it's also incredibly helpful for anyone who doesn't need to delve too deeply into the technical details but wants to build up their knowledge to a level where they can communicate with data analysts. \u003cbr\u003eIt's a truly remarkable book that delicately walks the line between general education and a full-fledged technical book.\u003cbr\u003e\u003cbr\u003e Especially in an era like today, when AI is rapidly becoming popular, it is necessary to focus on the 'essence' of data, the raw material that powers AI.\u003cbr\u003e While countless books and articles about AI abound today, the most accurate way to understand AI is to trace the evolution of \"data-driven statistical thinking\" into AI.\u003cbr\u003e In that sense, I hope this book can serve as a first textbook for the general public living in the AI ​​era.\u003cbr\u003e\u003cbr\u003e The technical aspects of the book are something I'm already familiar with, and since they don't go into too much detail, I was able to easily understand the authors' arguments and messages in the original book. However, the problem was the process of translating it into Korean. \u003cbr\u003eThe content covered in each sentence and paragraph is dense and the meaning is compressed, so the sentence itself is easy, but it took a lot of thought and time to translate the exact meaning and subtle nuances of the original text into Korean sentences.\u003cbr\u003e Although it is an old saying, I had no choice but to translate it 'one by one', putting in a lot of time and effort.\u003cbr\u003e\u003cbr\u003e I must confess that I have always had a strong desire to write a good book that strikes the perfect balance between educational and technical.\u003cbr\u003e However, while translating this book, I felt a sense of disappointment that a similar book had already been published, but at the same time, I felt a sense of joy at having discovered such a great book and being given the opportunity to translate it.\u003cbr\u003e It is such an excellent book, and I can confidently recommend it to many people.\u003cbr\u003e \u003cb\u003e- Choi Jae-won\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eHaving lived as a materials engineer for several decades, I have been busy researching and analyzing various materials engineering phenomena, even during my degree course.\u003cbr\u003e After receiving my degree, I started working in the semiconductor industry. In addition to the materials engineering perspective I had previously covered, I was exposed to statistical concepts such as various quality control techniques and model interpretation for reliability analysis.\u003cbr\u003e It was essential for companies to improve the quality and lifespan of their products to make a profit.\u003cbr\u003e\u003cbr\u003e But there was still a thought lingering in my head.\u003cbr\u003e If we fully understand the phenomena dealt with in materials engineering, it seemed that such statistical approaches could be minimized and perhaps even eliminated.\u003cbr\u003e Looking back now, I think perhaps it was more that I wanted to completely ignore statistical approaches and applications. \u003cbr\u003eMost of the science and engineering I've been focusing on, including materials engineering, has been about exploring causal relationships to clearly identify cause and effect.\u003cbr\u003e Then, the AI ​​era arrived and began to be applied to all fields, including semiconductors.\u003cbr\u003e This made me wonder what the difference was between the classical data concepts in statistics and the data handled by AI.\u003cbr\u003e Out of this vague curiosity, I searched through countless papers and books, and even wandered the ocean of the Internet.\u003cbr\u003e\u003cbr\u003e For ordinary researchers like me, isn't there a book like \"a pearl in the mud\" that doesn't just talk about rosy futures, without coding or complex statistical formulas, but instead gets straight to the point? Are there authors who write for people with similar curiosity? Indeed, this connection did exist! It was the original book, \"Becoming a Data Head.\" \u003cbr\u003eThis pearl, which I found with great difficulty, was an English book, but it was so enjoyable to read that I still vividly remember the feeling.\u003cbr\u003e The authors of this book were storytellers who, as if they were discussing everything I had ever wondered about in a chatroom, were able to smoothly unravel it. As I read the book, I felt as if decades of fundamental questions about statistics and data had been answered in one fell swoop.\u003cbr\u003e\u003cbr\u003e How many things in the universe, including technical challenges, can we truly understand causal relationships? That's why it was necessary to start with statistics and understand the world of data opened up by deep learning and AI.\u003cbr\u003e Another striking aspect of this book is that it offers a variety of metaphors that illustrate how not only ordinary engineers and researchers, but also business executives and managers should view and utilize data for the success of their companies.\u003cbr\u003e \u003cbr\u003eI hope that readers from various fields will enjoy the joy of data enlightenment that this book brings, and I conclude this article with that hope.\u003cbr\u003e \u003cb\u003e- Jang Jin-wook\u003c\/b\u003e \u003cb\u003e[Author's Note]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e We believe many people want to learn about data properly, but don't know where to start.\u003cbr\u003e The numerous data science and statistics books already published cover a very diverse spectrum.\u003cbr\u003e On one side of the spectrum, there are numerous non-technical books that extol the benefits and hopeful outlook of data utilization.\u003cbr\u003e There are some good books among them.\u003cbr\u003e However, no matter how good a book is, it is often a book that deals with business that is biased towards the present.\u003cbr\u003e Most of the books are written by journalists to highlight the dramatic aspects of the rise of data.\u003cbr\u003e \u003cbr\u003eThese books explain how specific business problems are solved through the lens of data. Terms like AI and machine learning often appear.\u003cbr\u003e Here, I hope there is no misunderstanding.\u003cbr\u003e Books like this have helped raise people's awareness of how to use data.\u003cbr\u003e However, it does not delve deeply into the specific tasks to be performed, but simply focuses on the big picture problems and solutions.\u003cbr\u003e\u003cbr\u003e At the other end of the spectrum are high-level technical books.\u003cbr\u003e Most of them are heavy books with over 500 pages, so they are not only physically demanding but also mentally taxing.\u003cbr\u003e\u003cbr\u003e There are mountains of books at both ends of the spectrum.\u003cbr\u003e The communication gap is not easily narrowed because most people only read business or technical books.\u003cbr\u003e Fortunately, there are some excellent books between these two extremes.\u003cbr\u003e\u003cbr\u003e \u003cbr\u003eWe'd like to add our book to this list.\u003cbr\u003e The book you are reading can be read by anyone without any burden, even without a computer or notepad nearby.\u003cbr\u003e If you enjoyed our book, I highly recommend reading at least one of the two books mentioned above to further solidify your understanding of data.\u003cbr\u003e You won't regret it.\u003cbr\u003e\u003cbr\u003e Our authors really like this book.\u003cbr\u003e If this book motivates you to learn about data and data analysis and sparks a desire to learn more, then I consider that a success.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Translator's Note]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e I've read, studied, and sometimes translated many books on data, but this book is so great that I wish I had written it myself instead of translating it. \u003cbr\u003eWhen I first received the original book and skimmed through the chapter titles, I thought, \"Isn't this content too easy?\" However, from the moment I started reading it in earnest, savoring each sentence in preparation for the translation, until the very end of the last chapter, I couldn't help but be impressed by the authors' efforts to write in line with the book's planning intentions, and their deep expertise in data analysis and statistics.\u003cbr\u003e\u003cbr\u003e It is often said that “the easiest thing to write is the hardest.”\u003cbr\u003e I've always rationally agreed with this statement, but I've rarely experienced a concrete example. After reading this book, I felt like I'd finally encountered a true example of what it means.\u003cbr\u003e 'Easy to write' means that the writer has a perfect grasp of the core and logic of the content, which allows for writing that is easy, clear, and logical.\u003cbr\u003e \u003cbr\u003eThis book covers just the right depth and breadth of data analysis and statistics, which can be challenging.\u003cbr\u003e It's a great introductory book for those considering a career in this field, but it's also incredibly helpful for anyone who doesn't need to delve too deeply into the technical details but wants to build up their knowledge to a level where they can communicate with data analysts.\u003cbr\u003e It's a truly remarkable book that delicately walks the line between general education and a full-fledged technical book.\u003cbr\u003e\u003cbr\u003e Especially in an era like today, when AI is rapidly becoming popular, it is necessary to focus on the 'essence' of data, the raw material that powers AI.\u003cbr\u003e While countless books and articles about AI abound today, the most accurate way to understand AI is to trace the evolution of \"data-driven statistical thinking\" into AI. \u003cbr\u003eIn that sense, I hope this book can serve as a first textbook for the general public living in the AI ​​era.\u003cbr\u003e\u003cbr\u003e The technical aspects of the book are something I'm already familiar with, and since they don't go into too much detail, I was able to easily understand the authors' arguments and messages in the original book. However, the problem was the process of translating it into Korean.\u003cbr\u003e The content covered in each sentence and paragraph is dense and the meaning is compressed, so the sentence itself is easy, but it took a lot of thought and time to translate the exact meaning and subtle nuances of the original text into Korean sentences.\u003cbr\u003e Although it is an old saying, I had no choice but to translate it 'one by one', putting in a lot of time and effort.\u003cbr\u003e\u003cbr\u003e I must confess that I have always had a strong desire to write a good book that strikes the perfect balance between educational and technical. \u003cbr\u003eHowever, while translating this book, I felt a sense of disappointment that a similar book had already been published, but at the same time, I felt a sense of joy at having discovered such a great book and being given the opportunity to translate it.\u003cbr\u003e It is such an excellent book, and I can confidently recommend it to many people.\u003cbr\u003e \u003cb\u003e- Choi Jae-won\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Having lived as a materials engineer for several decades, I have been busy researching and analyzing various materials engineering phenomena, even during my degree course.\u003cbr\u003e After receiving my degree, I started working in the semiconductor industry. In addition to the materials engineering perspective I had previously covered, I was exposed to statistical concepts such as various quality control techniques and model interpretation for reliability analysis.\u003cbr\u003e It was essential for companies to improve the quality and lifespan of their products to make a profit.\u003cbr\u003e\u003cbr\u003e But there was still a thought lingering in my head. \u003cbr\u003eIf we fully understand the phenomena dealt with in materials engineering, it seemed that such statistical approaches could be minimized and perhaps even eliminated.\u003cbr\u003e Looking back now, I think perhaps it was more that I wanted to completely ignore statistical approaches and applications.\u003cbr\u003e Most of the science and engineering I've been focusing on, including materials engineering, has been about exploring causal relationships to clearly identify cause and effect.\u003cbr\u003e Then, the AI ​​era arrived and began to be applied to all fields, including semiconductors.\u003cbr\u003e This made me wonder what the difference was between the classical data concepts in statistics and the data handled by AI.\u003cbr\u003e Out of this vague curiosity, I searched through countless papers and books, and even wandered the ocean of the Internet.\u003cbr\u003e \u003cbr\u003eFor ordinary researchers like me, isn't there a book like \"a pearl in the mud\" that doesn't just talk about rosy futures, without coding or complex statistical formulas, but instead gets straight to the point? Are there authors who write for people with similar curiosity? Indeed, this connection did exist! It was the original book, \"Becoming a Data Head.\"\u003cbr\u003e This pearl, which I found with great difficulty, was an English book, but it was so enjoyable to read that I still vividly remember the feeling.\u003cbr\u003e The authors of this book were storytellers who, as if they were discussing everything I had ever wondered about in a chatroom, were able to smoothly unravel it. As I read the book, I felt as if decades of fundamental questions about statistics and data had been answered in one fell swoop.\u003cbr\u003e \u003cbr\u003eHow many things in the universe, including technical challenges, can we truly understand causal relationships? That's why it was necessary to start with statistics and understand the world of data opened up by deep learning and AI.\u003cbr\u003e Another striking aspect of this book is that it offers a variety of metaphors that illustrate how not only ordinary engineers and researchers, but also business executives and managers should view and utilize data for the success of their companies.\u003cbr\u003e\u003cbr\u003e I hope that readers from various fields will enjoy the joy of data enlightenment that this book brings, and I conclude this article with that hope.\u003cbr\u003e \u003cb\u003e- Jang Jin-wook\u003c\/b\u003e \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e May 3, 2024\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 368 pages | 544g | 152*224*19mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791189909628\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1189909626 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893711044650,"sku":"110267","price":29.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/980591c5411ec277e52fe08bf74fddab.jpg?v=1765413176","url":"https:\/\/librairie.coreenne.fr\/en\/products\/110267","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}