We specialize in hard to find items
All Categories
Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data
Share Tweet
*Price and Stocks may change without prior notice
*Packaging of actual item may differ from photo shown
- Electrical items MAY be 110 volts.
- 7 Day Return Policy
- All products are genuine and original
- Cash On Delivery/Cash Upon Pickup Available
About Practical Data Science With Python: Learn Tools And
Product Description Learn to effectively manage data and execute data science projects from start to finish using PythonKey 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 DescriptionPractical 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 forThe 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.Table of ContentsIntroduction to Data ScienceGetting Started with PythonSQL and Built-in File Handling Modules in PythonLoading and Wrangling Data with Pandas and NumPyExploratory Data Analysis and VisualizationData Wrangling Documents and SpreadsheetsWeb ScrapingProbability, Distributions, and SamplingStatistical Testing for Data SciencePreparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality ReductionMachine Learning for ClassificationEvaluating Machine Learning Classification Models and Sampling for ClassificationMachine Learning with Regression(N.B. Please use the Look Inside option to see further chapters) Review "In Practical Data Science with Python, Nate George sets himself the ambitious goal of making the elusive phrase "Data Science" into a practical reality for a very broad audience, requiring very little from the readers in terms of existing knowledge. He extensively covers the basics and practical applications, a lot about ML, followed by a little about NLP. I believe a bright, motivated reader working throughout the book and applying themselve
