×
Dodano do koszyka:
Pozycja znajduje się w koszyku, zwiększono ilość tej pozycji:
Zakupiłeś już tę pozycję:
Książkę możesz pobrać z biblioteki w panelu użytkownika
Pozycja znajduje się w koszyku
Przejdź do koszyka

Zawartość koszyka

ODBIERZ TWÓJ BONUS :: »

Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools

(ebook) (audiobook) (audiobook) Książka w języku 1
Autor:
David Mertz
Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz - okladka książki

Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz - okladka książki

Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz - audiobook MP3

Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz - audiobook CD

Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
498
Dostępne formaty:
     PDF
     ePub
     Mobi

Ebook (107,10 zł najniższa cena z 30 dni)

119,00 zł (-75%)
29,90 zł

Dodaj do koszyka lub Kup na prezent Kup 1-kliknięciem

(107,10 zł najniższa cena z 30 dni)

Przenieś na półkę

Do przechowalni

Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way.

In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with.

Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses.

Wybrane bestsellery

O autorze książki

David Mertz, Ph.D. is the founder of KDM Training, a partnership dedicated to educating developers and data scientists in machine learning and scientific computing. He created a data science training program for Anaconda Inc. and was a senior trainer for them. With the advent of deep neural networks, he has turned to training our robot overlords as well.



He previously worked for 8 years with D. E. Shaw Research and was also a Director of the Python Software Foundation for 6 years. David remains co-chair of its Trademarks Committee and Scientific Python Working Group. His columns, Charming Python and XML Matters, were once the most widely read articles in the Python world.

Packt Publishing - inne książki

Zamknij

Przenieś na półkę

Proszę czekać...
ajax-loader

Zamknij

Wybierz metodę płatności

Ebook
29,90 zł
Dodaj do koszyka
Zamknij Pobierz aplikację mobilną Ebookpoint