Kennedy Behrman

Foundational Python for Data Science Pearson uCertify Course Access Code Card (eBook, PDF)

• Format: PDF

Produktbeschreibung

Learn all the foundational Python you'll need to solve real data science problems

Data science and machine learning--two of the world's hottest fields--are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This guide is specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning.

Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving.

• Master Google colab notebook Data Science programming
• Manipulate data with popular Python libraries such as: pandas and numpy
• Apply Python Data Science recipes to real world projects
• Learn functional programming essentials unique to Data Science
• Access case studies, chapter exercises, learning assessments, comprehensive Jupyter based Notebooks, and a complete final project

Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more--all created with colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.

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Produktdetails

• Verlag: Pearson ITP
• Seitenzahl: 256
• Altersempfehlung: ab 18 Jahre
• Erscheinungstermin: 12. Oktober 2021
• Englisch
• ISBN-13: 9780136624363
• Artikelnr.: 66021819

Autorenporträt

Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes.

Inhaltsangabe

Preface xiii
I:  Learning Python in a Notebook Environment 1
1  Introduction to Notebooks 3
2  Fundamentals of Python 13
3  Sequences 25
4  Other Data Structures 37
5  Execution Control 55
6  Functions 67
II: Data Science Libraries 83
7  NumPy 85
8  SciPy 103
9  Pandas 113
10  Visualization Libraries 135
11  Machine Learning Libraries 153
12  Natural Language Toolkit 159
III: Intermediate Python 171
13  Functional Programming 173
14  Object-Oriented Programming 187
15  Other Topics 201
A  Answers to End-of-Chapter Questions 215
Index 221