George Practical Data Science with Python

Learn tools and techniques from hands-on examples to extract insights from data
1. Auflage 2021
ISBN: 978-1-80107-665-4
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 620 Seiten

No detailed description available for "Practical Data Science with Python".

Autoren/Hrsg.

George, Nathan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

Table of Contents - Introduction to Data Science
- Getting Started with Python
- SQL and Built-in File Handling Modules in Python
- Loading and Wrangling Data with Pandas and NumPy
- Exploratory Data Analysis and Visualization
- Data Wrangling Documents and Spreadsheets
- Web Scraping
- Probability, Distributions, and Sampling
- Statistical Testing for Data Science
- Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction
- Machine Learning for Classification
- Evaluating Machine Learning Classification Models and Sampling for Classification
- Machine Learning with Regression
- Optimizing Models and Using AutoML
- Tree-Based Machine Learning Models
- Support Vector Machine (SVM) Machine Learning Models
- Clustering with Machine Learning
- Working with Text
- Data Storytelling and Automated Reporting/ Dashboarding
- Ethics and Privacy
- Staying Up to Date and the Future of Data Science

Über Autor(innen)

George Nathan:

Nathan George is a data scientist at Tink in Stockholm, Sweden, and taught data science as a professor at Regis University in Denver, CO for over 4 years. Nathan has created online courses on Pythonic data science and uses Python data science tools for electroencephalography (EEG) research with the OpenBCI headset and public EEG data. His education includes the Galvanize data science immersive, a PhD from UCSB in Chemical Engineering, and a BS in Chemical Engineering from the Colorado School of Mines.