Practical data science with python

Practical Data Science with Python

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.

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

Who this book is 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.

Источник

Saved searches

Use saved searches to filter your results more quickly

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

Practical Data Science with Python, published by Packt

License

PacktPublishing/Practical-Data-Science-with-Python

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Sign In Required

Please sign in to use Codespaces.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching Xcode

If nothing happens, download Xcode and try again.

Launching Visual Studio Code

Your codespace will open once ready.

There was a problem preparing your codespace, please try again.

Latest commit

Git stats

Files

Failed to load latest commit information.

README.md

This is the code repository for Practical Data Science with Python, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

This is intended to be a book for beginners to intermediate learners who want to become data scientists or learn some of the basics of data science and machine learning.

Instructions and Navigation

The code from this book and repository is intended for Python 3.9. See chapter 2 for instructions on getting setup with Python.

All of the code is organized into folders. Each folder contains the code and data for that chapter.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

About

Practical Data Science with Python, published by Packt

Источник

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

Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook 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.

What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is 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.

Источник

Practical Data Science with Python 3

This is a preview of subscription content, access via your institution.

Table of contents (12 chapters)

Front Matter

Introduction to Data Science

Data Engineering

Software Engineering

Documenting Your Work

Data Processing

Data Visualization

Machine Learning

Recommender Systems

Data Security

Graph Analysis

Complexity and Heuristics

Deep Learning

Back Matter

About this book

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.

As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You’ll see how to create maintainable software for data science and how to document data engineering practices.

This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You’ll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.

Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.

• Play the role of a data scientist when completing increasingly challenging exercises using Python 3
• Work work with proven data science techniques/technologies
• Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data
• Apply theory of probability, statistical inference, and algebra to understand the data science practices

Keywords

• Data Science
• Python 3
• Machine Learning
• Neural Networks
• OMG Essence
• Apache Spark
• TensorFlow
• Numpy
• Pandas
• Matpotlib
• IPython notebooks

Authors and Affiliations

Kikinda, Serbia

About the author

Ervin Varga is a Senior Member of IEEE and Professional Member of ACM. He is an IEEE Software Engineering Certified Instructor. Ervin is an owner of the software consulting company Expro I.T. Consulting, Serbia. He has an MSc in computer science, and a PhD in electrical engineering (his thesis was an application of software engineering and computer science in the domain of electrical power systems). Ervin is also a technical advisor of the open-source project Mainflux.

Bibliographic Information

• Book Title : Practical Data Science with Python 3
• Book Subtitle : Synthesizing Actionable Insights from Data
• Authors : Ervin Varga
• DOI : https://doi.org/10.1007/978-1-4842-4859-1
• Publisher : Apress Berkeley, CA
• eBook Packages : Professional and Applied Computing , Professional and Applied Computing (R0) , Apress Access Books
• Copyright Information : Ervin Varga 2019
• Softcover ISBN : 978-1-4842-4858-4 Published: 08 September 2019
• eBook ISBN : 978-1-4842-4859-1 Published: 07 September 2019
• Edition Number : 1
• Number of Pages : XVII, 462
• Number of Illustrations : 94 b/w illustrations
• Topics : Python , Big Data , Open Source

Источник