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Data Science and Engineering - A learning path Exploratory Data Analysis, Metrics, Models with applications in Python Kindle Edition
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises.
It covers Exploratory Data Analysis and Modeling activities.
Since this text uses Python for application aspects, its installation and use are briefly described. In any case, this text should not be considered a Python manual. If by chance you made the wrong choice because you expected something different, you are free to return it but perhaps it is not the case to give a negative rating.
The text first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. Then it considers the concept of model, its life cycle and the relationship with measures and metrics. The measures of localization, dispersion, asymmetry, correlation, similarity, distance are then described. After, the test and score metrics used in machine learning, those relating to texts and documents, the association metrics between items in a shopping cart, the metrics related to relationship between objects, similarity between sets and between graphs, similarity between time series are considered.
As a preliminary activity to the modeling phase, the Exploration Data Analysis is deepened in terms of questions, process, techniques and types of problems. For each type of problem, the recommended plots, the methods of interpreting the results and their implementation in Python are considered.
The data modeling phase is considered from the point of view of machine learning by deepening the types of machine learning, the types of models, the types of problems and the types of algorithms.
After considering the ideal characteristics of models and algorithms, a vocabulary of the types of models and algorithms is compiled and their use in Python is considered through supervised and unsupervised learning projects.
The text is accompanied by supporting material and you can download the exercises and test data.
- LanguageEnglish
- Publication dateJanuary 2, 2025
- File size55.8 MB
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Product details
- ASIN : B0DS56RCRX
- Accessibility : Learn more
- Publication date : January 2, 2025
- Language : English
- File size : 55.8 MB
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Print length : 543 pages
- Page Flip : Enabled
- Part of series : Data Science and Engineering - A learning path
- Best Sellers Rank: #4,157,101 in Kindle Store (See Top 100 in Kindle Store)
- #1,121 in Data Mining (Kindle Store)
- #1,272 in Data Modeling & Design (Kindle Store)
- #2,181 in Data Modeling & Design (Books)
About the author

Graduated in Computer Science, he taught Formal Languages and Compilers at the University of Bari at the Faculty of Computer Science and Fundamentals of Computer Science II at the Polytechnic of Bari in the Degree course in Electronic Engineering
He has also worked for over twenty years in various companies also in the field of Data Science
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