"The crisp-dm figure is about building a model (ml approach) wiht evaluation options while building. The links are related to questions in a business goal within the deloyment stage. That is not covered in Crisp-dm.What is needed is: - An…"
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Adrian Walker commented on Mehmet Gökce's blog post An Overview of Business Problems and Data Science Solutions — Part 2"Data understanding and business understanding can be coupled robustly by writing data apps in Executable English.
Each app can then explain its results with a human readable audit trail showing its reasoning steps all the way to the data that is…"
Mehmet Gökce posted a blog postAn Overview of Business Problems and Data Science Solutions — Part 2
Data MiningThere is an important distinction related to data mining. First the difference between mining the data to find patterns and build models, and second using the results of data mining. Data Mining results inform the data mining process itself.The CRISP data mining processCross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model and breaks…See More
Mehmet Gökce's blog post was featuredAn Overview of Business Problems and Data Science Solutions — Part 2
Data MiningThere is an important distinction related to data mining. First the difference between mining the data to find patterns and build models, and second using the results of data mining. Data Mining results inform the data mining process itself.The CRISP data mining processCross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model and breaks…See More
Mehmet Gökce liked Vincent Granville's blog post Full Stack Data Scientist: The Elusive Unicorn and Data HackerMehmet Gökce liked Vincent Granville's blog post Full Stack Data Scientist: The Elusive Unicorn and Data HackerMehmet Gökce posted a blog postBusiness Problems and Data Science Solutions Part 1
An important principle of data science is that data mining is a process. It includes the application of information technology, such as the automated discovery and evaluation of patterns from data. It also includes an analyst’s creativity, business knowledge, and common sense. Understanding the whole process helps to structure data mining projects.Since the data mining process breaks up the overall task of finding patterns from data into a set of well-defined subtasks, it is also useful for…See More
Mehmet Gökce's blog post was featuredBusiness Problems and Data Science Solutions Part 1
An important principle of data science is that data mining is a process. It includes the application of information technology, such as the automated discovery and evaluation of patterns from data. It also includes an analyst’s creativity, business knowledge, and common sense. Understanding the whole process helps to structure data mining projects.Since the data mining process breaks up the overall task of finding patterns from data into a set of well-defined subtasks, it is also useful for…See More
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- 20 years of IT experience. Software Engineer, ECommerce, PIM, CRM, ETL, Webdevelopment, BI.
- My Web Site Or LinkedIn Profile
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Mehmet Gökce's Blog
An Overview of Business Problems and Data Science Solutions — Part 2
Posted on October 29, 2018 at 9:13am 1 Comment 0 Likes
Data Mining
There is an important distinction related to data mining. First the difference between mining the data to find patterns and build models, and second using the results of data mining. Data Mining results inform the data mining process itself.
The CRISP data mining process
Cross-industry standard process…
Continue Business Problems and Data Science Solutions Part 1
Posted on October 24, 2018 at 8:58am 0 Comments 0 Likes
An important principle of data science is that data mining is a process. It includes the application of information technology, such as the automated discovery and evaluation of patterns from data. It also includes an analyst’s creativity, business knowledge, and common sense. Understanding the whole process helps to structure data mining projects.
Since the data mining process breaks up the overall task…
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