Python Programming For Data Scientists

Download Python Programming For Data Scientists full books in PDF, epub, and Kindle. Read online Python Programming For Data Scientists ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Python Packages

Author : Tomas Beuzen
Publisher : CRC Press
ISBN 13 : 1000555062
Total Pages : 243 pages
Book Rating : 4.0/5 (5 download)

DOWNLOAD NOW!

Book Synopsis Python Packages by : Tomas Beuzen

Download or read book Python Packages written by Tomas Beuzen and published by CRC Press. This book was released on 2022-04-20 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python Packages introduces Python packaging at an introductory and practical level that’s suitable for those with no previous packaging experience. Despite this, the text builds up to advanced topics such as automated testing, creating documentation, versioning and updating a package, and implementing continuous integration and deployment. Covering the entire Python packaging life cycle, this essential guide takes readers from package creation all the way to effective maintenance and updating. Python Packages focuses on the use of current and best-practice packaging tools and services like poetry, cookiecutter, pytest, sphinx, GitHub, and GitHub Actions. Features: The book’s source code is available online as a GitHub repository where it is collaborated on, automatically tested, and built in real time as changes are made; demonstrating the use of good reproducible and clear project workflows. Covers not just the process of creating a package, but also how to document it, test it, publish it to the Python Package Index (PyPI), and how to properly version and update it. All concepts in the book are demonstrated using examples. Readers can follow along, creating their own Python packages using the reproducible code provided in the text. Focuses on a modern approach to Python packaging with emphasis on automating and streamlining the packaging process using new and emerging tools such as poetry and GitHub Actions.

Python for Data Science

Author : Erick Thompson
Publisher :
ISBN 13 : 9781801547994
Total Pages : 266 pages
Book Rating : 4.5/5 (479 download)

DOWNLOAD NOW!

Book Synopsis Python for Data Science by : Erick Thompson

Download or read book Python for Data Science written by Erick Thompson and published by . This book was released on 2020-10-30 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Python Data Science Handbook

Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491912138
Total Pages : 743 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Python Data Science Handbook by : Jake VanderPlas

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Python for Data Science For Dummies

Author : John Paul Mueller
Publisher : John Wiley & Sons
ISBN 13 : 1118843983
Total Pages : 432 pages
Book Rating : 4.1/5 (188 download)

DOWNLOAD NOW!

Book Synopsis Python for Data Science For Dummies by : John Paul Mueller

Download or read book Python for Data Science For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2015-06-23 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

Python Programming for Data Analysis

Author : José Unpingco
Publisher : Springer Nature
ISBN 13 : 3030689522
Total Pages : 263 pages
Book Rating : 4.0/5 (36 download)

DOWNLOAD NOW!

Book Synopsis Python Programming for Data Analysis by : José Unpingco

Download or read book Python Programming for Data Analysis written by José Unpingco and published by Springer Nature. This book was released on 2021-05-04 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns. After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly. The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.

Data Science from Scratch

Author : Joel Grus
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491904402
Total Pages : 330 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Data Science from Scratch by : Joel Grus

Download or read book Data Science from Scratch written by Joel Grus and published by "O'Reilly Media, Inc.". This book was released on 2015-04-14 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Practical Data Science with Python

Author : Nathan George
Publisher : Packt Publishing Ltd
ISBN 13 : 1801076650
Total Pages : 621 pages
Book Rating : 4.8/5 (1 download)

DOWNLOAD NOW!

Book Synopsis Practical Data Science with Python by : Nathan George

Download or read book Practical Data Science with Python written by Nathan George and published by Packt Publishing Ltd. This book was released on 2021-09-30 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to effectively manage data and execute data science projects from start to finish using Python 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.

Python for Data Science

Author : Yuli Vasiliev
Publisher : No Starch Press
ISBN 13 : 1718502214
Total Pages : 271 pages
Book Rating : 4.7/5 (185 download)

DOWNLOAD NOW!

Book Synopsis Python for Data Science by : Yuli Vasiliev

Download or read book Python for Data Science written by Yuli Vasiliev and published by No Starch Press. This book was released on 2022-08-02 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples. Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You’ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Python’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.

Python for Data Analysis

Author : Wes McKinney
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491957611
Total Pages : 676 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Python for Data Analysis by : Wes McKinney

Download or read book Python for Data Analysis written by Wes McKinney and published by "O'Reilly Media, Inc.". This book was released on 2017-09-25 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Python and R for the Modern Data Scientist

Author : Rick J. Scavetta
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492093378
Total Pages : 199 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Python and R for the Modern Data Scientist by : Rick J. Scavetta

Download or read book Python and R for the Modern Data Scientist written by Rick J. Scavetta and published by "O'Reilly Media, Inc.". This book was released on 2021-06-22 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together

Learning Scientific Programming with Python

Author : Christian Hill
Publisher : Cambridge University Press
ISBN 13 : 1108787460
Total Pages : 572 pages
Book Rating : 4.1/5 (87 download)

DOWNLOAD NOW!

Book Synopsis Learning Scientific Programming with Python by : Christian Hill

Download or read book Learning Scientific Programming with Python written by Christian Hill and published by Cambridge University Press. This book was released on 2020-11-12 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming.

Data Science

Author : Tiffany Timbers
Publisher : CRC Press
ISBN 13 : 1000579646
Total Pages : 466 pages
Book Rating : 4.0/5 (5 download)

DOWNLOAD NOW!

Book Synopsis Data Science by : Tiffany Timbers

Download or read book Data Science written by Tiffany Timbers and published by CRC Press. This book was released on 2022-07-15 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.

Introduction to Data Science

Author : Laura Igual
Publisher : Springer
ISBN 13 : 3319500171
Total Pages : 218 pages
Book Rating : 4.3/5 (195 download)

DOWNLOAD NOW!

Book Synopsis Introduction to Data Science by : Laura Igual

Download or read book Introduction to Data Science written by Laura Igual and published by Springer. This book was released on 2017-02-22 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Pandas for Everyone

Author : Daniel Y. Chen
Publisher : Addison-Wesley Professional
ISBN 13 : 0134547055
Total Pages : 1093 pages
Book Rating : 4.1/5 (345 download)

DOWNLOAD NOW!

Book Synopsis Pandas for Everyone by : Daniel Y. Chen

Download or read book Pandas for Everyone written by Daniel Y. Chen and published by Addison-Wesley Professional. This book was released on 2017-12-15 with total page 1093 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning

A Tour of Data Science

Author : Nailong Zhang
Publisher : CRC Press
ISBN 13 : 1000215199
Total Pages : 217 pages
Book Rating : 4.0/5 (2 download)

DOWNLOAD NOW!

Book Synopsis A Tour of Data Science by : Nailong Zhang

Download or read book A Tour of Data Science written by Nailong Zhang and published by CRC Press. This book was released on 2020-11-11 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

Learn Python by Building Data Science Applications

Author : Philipp Kats
Publisher : Packt Publishing Ltd
ISBN 13 : 1789533066
Total Pages : 464 pages
Book Rating : 4.7/5 (895 download)

DOWNLOAD NOW!

Book Synopsis Learn Python by Building Data Science Applications by : Philipp Kats

Download or read book Learn Python by Building Data Science Applications written by Philipp Kats and published by Packt Publishing Ltd. This book was released on 2019-08-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the constructs of the Python programming language and use them to build data science projects Key FeaturesLearn the basics of developing applications with Python and deploy your first data applicationTake your first steps in Python programming by understanding and using data structures, variables, and loopsDelve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in PythonBook Description Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards. What you will learnCode in Python using Jupyter and VS CodeExplore the basics of coding – loops, variables, functions, and classesDeploy continuous integration with Git, Bash, and DVCGet to grips with Pandas, NumPy, and scikit-learnPerform data visualization with Matplotlib, Altair, and DatashaderCreate a package out of your code using poetry and test it with PyTestMake your machine learning model accessible to anyone with the web APIWho this book is for If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.

Foundational Python for Data Science

Author : Kennedy Behrman
Publisher : Pearson
ISBN 13 : 0136624316
Total Pages : 816 pages
Book Rating : 4.1/5 (366 download)

DOWNLOAD NOW!

Book Synopsis Foundational Python for Data Science by : Kennedy Behrman

Download or read book Foundational Python for Data Science written by Kennedy Behrman and published by Pearson. This book was released on 2021-10-12 with total page 816 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 is the first guide 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.