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Hands-On Data Analysis with Pandas. Efficiently perform data collection, wrangling, analysis, and visualization using Python

(ebook) (audiobook) (audiobook) Książka w języku 1
Hands-On Data Analysis with Pandas. Efficiently perform data collection, wrangling, analysis, and visualization using Python Stefanie Molin - okladka książki

Hands-On Data Analysis with Pandas. Efficiently perform data collection, wrangling, analysis, and visualization using Python Stefanie Molin - okladka książki

Hands-On Data Analysis with Pandas. Efficiently perform data collection, wrangling, analysis, and visualization using Python Stefanie Molin - audiobook MP3

Hands-On Data Analysis with Pandas. Efficiently perform data collection, wrangling, analysis, and visualization using Python Stefanie Molin - audiobook CD

Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
740
Dostępne formaty:
     PDF
     ePub
     Mobi
Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value.

Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data.

By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

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O autorze książki

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Packt Publishing - inne książki

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