Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!
Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.
You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.

Osoby które kupowały "Mastering Python Data Analysis. Become an expert at using Python for advanced statistical analysis of data using real-world examples", wybierały także:

• Biologika Sukcesji Pokoleniowej. Sezon 3. Konflikty na terytorium 127,27 zÅ‚, (14,00 zÅ‚ -89%)
• Windows Media Center. Domowe centrum rozrywki 66,67 zÅ‚, (8,00 zÅ‚ -88%)
• PodrÄ™cznik startupu. Budowa wielkiej firmy krok po kroku 93,33 zÅ‚, (14,00 zÅ‚ -85%)
• Ruby on Rails. Ćwiczenia 18,75 zÅ‚, (3,00 zÅ‚ -84%)
• Scrum. O zwinnym zarz 77,78 zÅ‚, (14,00 zÅ‚ -82%)

Spis treści

Mastering Python Data Analysis. Become an expert at using Python for advanced statistical analysis of data using real-world examples eBook -- spis treści

• 1. Tools of the Trade
• 2. Exploring data
• 3. Learning about Models
• 4. Regression
• 5. Clustering
• 6. Delving into Bayesian Methods
• 7. Using Python for Machine learning
• 8. Time Series Analysis
• 9. Appendix