03758nam a22005295i 4500 979-8-8688-0935-4 DE-He213 20251030110817.0 cr nn 008mamaa 241115s2024 xxu| s |||| 0|eng d 9798868809354 979-8-8688-0935-4 10.1007/979-8-8688-0935-4 doi Q336 UN bicssc COM021000 bisacsh UN thema Fouda, Engy. author. aut http://id.loc.gov/vocabulary/relators/aut 66209 Learn Data Science Using Python [electronic resource] : A Quick-Start Guide / by Engy Fouda. 1st ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XIV, 180 p. 94 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 1: Data Science in Action -- Chapter 2: Getting Started -- Chapter 3: Data Visualization -- Chapter 4: Statistical Analysis and Linear Models -- Chapter 5: Advanced Data Pre-processing and Feature Engineering -- Chapter 6: Preparing Data for Analysis -- Chapter 7: Regression. Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization. You’ll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You’ll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding. Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you’ll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression. You will: Understand installation procedures and valuable insights into Python, data types, typecasting Examine the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction. Artificial intelligence Data processing. 66210 Artificial intelligence. 58441 Machine learning. 55059 Python (Computer program language). 64608 Data Science. 66211 Artificial Intelligence. 58441 Machine Learning. 55059 Python. 47553 SpringerLink (Online service) Springer Nature eBook Printed edition: 9798868809347 Printed edition: 9798868809361 https://doi.org/10.1007/979-8-8688-0935-4 ZDB-2-CWD ZDB-2-SXPC 56320 56320