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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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Machine Learning for Time-Series with Python

$57.99

$57.99

$40.99

Total $ 156.97

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Sven Einsiedler Nov 30, 2021

Tl;dr: This book is a great choice for everyone with basic statistics & programming knowledge, who want to expand their Python skills and knowledge in the Data Science field. If you are looking for a book that covers a wide array of different Data Science topics & practical applications, this is the right book for you.What I liked most about "Practical Data Science with Python" is that it provided me with the perfect mixture of theory (basic statistical concepts, Machine learning models, etc.) and practical applications that went beyond just Python basics (Git, Web Scraping, handling SQL within Python, etc.). As a recent graduate now working for a tech company, I was able to refresh my knowledge from school, while at the same time picking up a new programming language. I think this book provides you with a really good tool kit if you are interested in working in tech or any data-driven company for that matter.The book is well-structured and easy to follow along. Each chapter starts with an introduction, outlining the topics that will be covered, and ends with a "Test your knowledge" section and summary. The questions are a fun way to keep track of your learnings and to double-check that you are indeed following along. The book further makes use of a lot of illustrative figures and code examples, which prevents unnecessarily long text parts that might be hard to follow.I would recommend this book for everyone with some initial programming knowledge (not necessarily from Python) looking for an "all rounder" Data Science introduction book.

Amazon Verified review

ScouserBass Dec 10, 2022

As a beginner with Python but a good knowledge of stats I’ve been working through the book methodically and applying its lessons to my own project and dataset. The inclusion of Jupyter Notebook resources for book has been very enlightening. My Python competence has come on leaps and bounds. Thoroughly recommended for budding Data Scientists.

Earle Feb 10, 2022

**Full disclosure, I was a student of Dr. George and purchased the book directly from Packt.**I highly recommend the book for newcomers to data science or those that have a general understanding of data science but want to solidify concepts and advance their knowledge. The book provides a nice balance of providing the reader with enough detail on each topic without overloading the reader using material that is too complex. Often books in the data science domain are too basic and do not cover more advanced subject matter or the author assumes prior knowledge leaving the reader feeling lost and confused. Dr. George navigates these challenges well and starts the reader out with an introduction on getting started with Python to eventually walking the reader through supervised learning techniques such as boosted trees and SVM, as well as unsupervised learning techniques such as K-means clustering. I found the sections on feature selection/engineering and optimizing models very comprehensive, including a wide range of techniques and options. A nice addition to the book was the inclusion of AutoML and PyCaret. That is an area that interests me, and I have yet to explore. I rarely write reviews on books, but due to the vast information nicely presented in an easy to understand and follow format, I felt compelled to endorse this book. I hope you enjoy the book as much as I do.

vishal kaushik Oct 21, 2021

Hello everyone,I am honored and glad at the same time that I got to read this book as I am data science enthusiast myself and beginning to set my foot in this domain.This book covers good length and breadth of the subject matter. It starts with very basic like how to install python and related packages and libraries along with version control using git( not all the books do that) .Then the books covers basics of data analysis using various libraries and tools and preparing data for machine learning models. Then the author dives into various machine learning algorithms with great and easy to understand examples.This book is definitely the best i have read in this subject domain and I highly recommend to everyone who is eager to jump into data science.It is definitely a great addition to my resource for learning data science.

Danial Jan 03, 2022

I have reviewed first 3 chapters and gave an overview to bunch other chapters. The initial setup of environments is explained in a very easy way. I am amazed by the way that lines of code are distinguishable from other text which helped me go through code when exploring for specific issues and fix them in a short time.Major thing that can be improved is the use of more visualizations in each chapter. Besides that I am very satisfied with the book.

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

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