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Practical Data Science with Python
Practical Data Science with Python

Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data

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Profile Icon Nathan George
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€31.49 €34.99
eBook Sep 2021 620 pages 1st Edition
eBook
€31.49 €34.99
Paperback
€43.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Nathan George
Arrow right icon
€31.49 €34.99
eBook Sep 2021 620 pages 1st Edition
eBook
€31.49 €34.99
Paperback
€43.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€31.49 €34.99
Paperback
€43.99
Subscription
Free Trial
Renews at €18.99p/m

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Practical Data Science with Python

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Key benefits

  • Understand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modeling
  • Build a strong data science foundation with the best data science tools available in Python
  • Add value to yourself, your organization, and society by extracting actionable insights from raw data

Description

Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.

Who is this book for?

The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.

What you will learn

  • Use Python data science packages effectively
  • Clean and prepare data for data science work, including feature engineering and feature selection
  • Data modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted models
  • Evaluate model performance
  • Compare and understand different machine learning methods
  • Interact with Excel spreadsheets through Python
  • Create automated data science reports through Python
  • Get to grips with text analytics techniques

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 30, 2021
Length: 620 pages
Edition : 1st
Language : English
ISBN-13 : 9781801076654
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What do you get with eBook?

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Product Details

Publication date : Sep 30, 2021
Length: 620 pages
Edition : 1st
Language : English
ISBN-13 : 9781801076654
Category :
Concepts :

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Table of Contents

29 Chapters
Part I - An Introduction and the Basics Chevron down icon Chevron up icon
Introduction to Data Science Chevron down icon Chevron up icon
Getting Started with Python Chevron down icon Chevron up icon
Part II - Dealing with Data Chevron down icon Chevron up icon
SQL and Built-in File Handling Modules in Python Chevron down icon Chevron up icon
Loading and Wrangling Data with Pandas and NumPy Chevron down icon Chevron up icon
Exploratory Data Analysis and Visualization Chevron down icon Chevron up icon
Data Wrangling Documents and Spreadsheets Chevron down icon Chevron up icon
Web Scraping Chevron down icon Chevron up icon
Part III - Statistics for Data Science Chevron down icon Chevron up icon
Probability, Distributions, and Sampling Chevron down icon Chevron up icon
Statistical Testing for Data Science Chevron down icon Chevron up icon
Part IV - Machine Learning Chevron down icon Chevron up icon
Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction Chevron down icon Chevron up icon
Machine Learning for Classification Chevron down icon Chevron up icon
Evaluating Machine Learning Classification Models and Sampling for Classification Chevron down icon Chevron up icon
Machine Learning with Regression Chevron down icon Chevron up icon
Optimizing Models and Using AutoML Chevron down icon Chevron up icon
Tree-Based Machine Learning Models Chevron down icon Chevron up icon
Support Vector Machine (SVM) Machine Learning Models Chevron down icon Chevron up icon
Part V - Text Analysis and Reporting Chevron down icon Chevron up icon
Clustering with Machine Learning Chevron down icon Chevron up icon
Working with Text Chevron down icon Chevron up icon
Part VI - Wrapping Up Chevron down icon Chevron up icon
Data Storytelling and Automated Reporting/Dashboarding Chevron down icon Chevron up icon
Ethics and Privacy Chevron down icon Chevron up icon
Staying Up to Date and the Future of Data Science Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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