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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

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Frequently bought together

£41.99

Machine Learning for Time-Series with Python

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£29.99

Total £ 113.97

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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.

Amazon Verified review

T. Zwingmann Oct 29, 2021

(Disclaimer: Ich habe vom Verlag ein Test-Exemplar zwecks Rezension erhalten, stehe aber ansonsten in keiner Verbindung mit dem Autor oder dem Verlag.)Das Buch hat sich die Aufgabe gestellt, alle Bereiche von Data Science abzudecken, angefangen bei der Geschichte um den Begriff bishin zu neuen Konzepten wie Auto-ML. Das geht an einigen Stellen verständlicherweise leider etwas zu Lasten der Tiefe und der Beispiele, was die im Vorwort genannten Einsteiger als Zielgruppe wohl manchmal überfordern dürfte. Für Leser mit ersten Berührungspunkten und/oder Erfahrungen im Bereich Data Science ist es hingegen ein tolles Nachschlage- bzw. Referenzwerk.

Francesco Jan 07, 2022

This book really stands out from the many other similar data science books. It serves as an introduction and practical guide to kickstart your career in data science.Many topics are covered although not extensively (i.e. mathematically), and the book is clearly organised in six main parts, each with its own sub-sections. The non-extensive explanations are by no means a downside (given the book's goal of being a practical guide, as pointed by the author), and the presentation of the material is strong enough that every reader is able to run the examples and experiments themselves. The book reads very well, personally making it one of the easiest books I have ever read. This is even for the seasoned developers and data scientists: you can find some hidden gems here and there.Overall, I'm very satisfied with the book and I would strongly recommend it. A must-have for everyone working or interested in data science - however, it is definitely not for someone that wants to know the math behind things of, for example, Machine Learning.

Brian Barnett Nov 07, 2021

First off, I found this to be an amazing book on data science. I’m relatively new to Python and data science so the topic can be somewhat intimidating. However, Nathan George’s style and approach is thorough, engaging, and encouraging.He starts with the basics and builds from there, with LOTs of practical, realistic scenarios and code samples. Having some experience in Python will be beneficial but not entirely necessary as Nathan does a great job explaining everything from the beginning, including installing Python.From there he dives into a thorough explanation of what data science is and demonstrates many of the great Python packages and tools available to aid you in your exploratory data analysis and visualization including sqlite3, sqlalchemy, pandas, numpy, scikit-learn, textract, matplotlib, sklearn, statsmodels, and more. It was great learning about the swifter package, which automatically parallelizes the use of pandas' apply function.An area I never considered he covers in detail is data wrangling Word docs, PDFs, and Excel spreadsheets. It was very enlightening and eye opening to discover how these types of files can be used in data science.And of course, you can’t cover data science without covering machine learning (ML). And he goes into depth (many chapters) explaining ML, covering supervised and unsupervised learning. And he stresses the importance of having clean data for ML to work with. He shows you various techniques to clean and prepare your data for ML.Throughout the book, Nathan encourages you to follow along, and points you to various code samples and files on a GitHub repo, and at the end of each chapter has a “Test Your Knowledge” section where he encourages you to put what you just learned into practice to help solidify your knowledge.I enjoyed his chapter on Ethics and Privacy. It’s great to learn about data science but it’s important to learn how to use it ethically, consider bias, and to consider people’s privacy when doing so. He walks through several examples of how to achieve the results we desire but to do so in an ethical way.I would highly recommend this book to anyone interested in learning data science or learning more in-depth data science techniques. You will find it all in this book, covered in detail and thoroughly explained in a step by step, encouraging approach.

Gabe Rigall Oct 21, 2021

BLUF: If you want to get into Data Science but are put off by the staggering amount of information out there, this book is for you.PROS:- Basic enough but not entry-level. This is definitely not a book for Python newbies. However, data science hopefuls will find everything they need to avoid floundering in the dark or chasing interesting-yet-fruitless bits of info down deep rabbit holes.- Just enough information on practical tools for data science. I'm still finding new functionality in pandas - even after two years of using it.- Provides a fair comparison of languages other than Python. If you're still figuring out which language you want to use as a primary, this book may help inform your choice.- Provides brief-yet-practical advice on data science career specializations.CONS:- A little dry at points. I appreciated the history of data science but to be quite honest, I skipped around a lot in this book. This of course is my subjective viewpoint and a minor gripe. This text has a lot to offer anyone looking into data science.

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

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