This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms

Do you want to learn what data science is all about? Familiar with Python and wonder how to use it to its fullest extent? Then this guide is for you.Learn how to set up your data science toolbox containing all the important tools you will need to complete a data science project. Python is the number one programming language for data science and this book will walk you through a project, allowing you to get familiar with the tools so you can move on to your own projects.Earn how to preprocess data, load it, transform it and fix it for the purpose of analysis. Learn how to explore data and how to process it before looking at the fundamental machine learning algorithms required for data science, at graph analysis and visualization tools. You will learn: -How to use Python to set up your data science toolbox-How to prepare data-How to solve data science problems-How to explore and manipulate data-All about the data science pipeline and how to set one up-How to choose the right algorithm for the right task-The visualization tools you need to present your resultsData science is here and it's here to stay so start your data science journey right now by clicking that Buy Now button.

Get into the world of data science with "Data Science Toolbox for Beginners" your comprehensive resource for becoming proficient with the foundational tools and techniques of data science. Whether you're a novice stepping into this fascinating field or a practitioner seeking to brush up on your skills, this book is designed to equip you with the knowledge and hands-on experience you need to excel. What You'll Discover: Chapter 1: Basic Python for Data Analysis: Learn the basic concepts of function, enough to get started with data analysis and data science. Chapter 2: NumPy Mastery: Learn the ins and outs of NumPy, from basic array creation and manipulation to advanced statistical methods and linear algebra functions. Chapter 3: Pandas for Data Manipulation and Analysis: Unlock the power of Pandas for efficient data handling, including data structures, importing/exporting data, cleaning, transformation, and advanced data operations. Chapter 4: Scaling with Dask: Explore how Dask complements Pandas by enabling scalable data analysis, offering insights into its core components, arrays, machine learning capabilities, and distributed computing. Chapter 5: Data Visualization with Matplotlib: Master the art of data visualization using Matplotlib. Learn to create a variety of plots, customize aesthetics, and effectively present your data. Chapter 6: Seaborn for Statistical Data Visualization: Delve into Seaborn for sophisticated statistical data visualization, including distribution visualizations, categorical data plots, and styling. Chapter 7: Interactive Visualizations with Plotly: Elevate your data presentations with interactive Plotly visualizations, ranging from simple line plots to complex 3D plots, interactive maps, and financial charts. Chapter 8: Machine Learning with Scikit-Learn: Get hands-on with Scikit-Learn for machine learning, covering everything from data preprocessing and model selection to supervised and unsupervised learning. Chapter 9: Deep Learning with TensorFlow and Keras: Step into the world of deep learning. Create, compile, and train models with TensorFlow and Keras, and explore different model-building techniques. Chapter 10: Statistical Analysis Fundamentals: Understand the core concepts of statistical analysis, including descriptive statistics, probability distributions, regression analysis, and more. Chapter 11: Data Science Project Lifecycle: Navigate through the data science project lifecycle, from understanding project scope to data collection, cleaning, exploratory data analysis, model development, evaluation, deployment, and maintenance. Why This Book? Hands-on Learning: Each chapter provides practical examples to apply your learning. Comprehensive Coverage: The book covers a wide range of tools and techniques, making it a one-stop guide for beginners. Up-to-Date and Relevant: Stay abreast with the latest trends and best practices in the fast-evolving field of data science. Embark on your data science journey with confidence and skill. "The Essential Data Science Toolbox: A Beginner's Guide" is your key to unlocking the potential of data science and its array of tools. Grab your copy today and start transforming data into actionable insights!

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.

This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, engineers, system administrators, and researchers. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTML, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create your own tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, regression, and classification algorithms Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark

Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

Do you wonder what the fascination is around data these days? How do we obtain insights from this data? Do you know what a data scientist does? What is artificial intelligence and machine learning? Are these the same as data science? What does it take to become a data scientist? If you have ever wondered about these questions, you have come to the right place!There are many resources and courses online that you can use to learn more about data science, but with so much information available, it can become overwhelming. One of the best ways to learn about data science is to understand different machine learning concepts, statistics, and artificial intelligence to help you design models to perform an analysis.This book has all the information you need to learn what data science is, and what the prerequisites are to become a data scientist. If you're a beginner or if you already have experience in data science, this book will have something for you.In this book, you will: Learn what data science is about.Discover the difference between data science and business intelligence.Explore the tools required for data science.Find out the technical and non-technical skills every data scientist must have.Figure out how to create a visualization of the data set with clear and easy examples.Get advice on developing a Predictive Model Using R.Uncover detailed applications of data science.And much more!The book has been structured with easy-to-understand sections to help you learn everything you need to know about data science. In this book you will learn about the prerequisites of data science and the skills you need to become a data scientist. So, what are you waiting for? Grab your copy of this comprehensive guide now