R & RStudio
R is a free programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. RStudio is an Integrated Development Environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.
Python & Anaconda
Python is a free and open source, interpreted, general purpose programming language. With an approachable syntax, Python is an easy language to learn for both beginners as well as experienced programmers. The language is also popular within the scientific community, with libraries written specifically to assist in the task of data analysis of large and complex scientific datasets. Anaconda is a distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. The distribution includes data-science packages suitable for Windows, Linux, and macOS.
OpenRefine
OpenRefine is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data. OpenRefine always keeps your data private on your own computer until you want to share or collaborate.
Excel
Excel works great for simple data processing and statistical analysis. However, it's not very suitable for large scale data manipulation, advanced statistical analysis or large amounts of data.
|
Name |
Purpose |
|
Text analysis |
|
|
ArcGIS (various products available) |
Geospatial data (GIS) |
|
Qualitative analysis |
|
|
XML processing |
|
|
Geospatial data (GIS) |
|
|
Network analysis |
|
|
Text analysis |
|
|
Network analysis |
|
|
Geospatial data (GIS) |
|
|
Geospatial data (GIS) |
|
|
Quantitative analysis |
|
|
Text analysis |
|
|
Text analysis |
|
|
Quantitative analysis |
|
|
Qualitative analysis |
|
|
Editor |
|
|
Qualitative analysis |
|
|
Data cleanup |
|
|
XML processing |
|
|
Network analysis |
|
|
Data visualization |
|
|
Programming and scripting |
|
|
Geospatial data (GIS) |
|
|
Programming and scripting |
|
|
Quantitative analysis |
|
|
Network analysis |
|
|
Quantitative analysis |
|
|
Quantitative analysis |
|
|
Data visualization |
|
|
Qualitative analysis |
|
|
Text analysis |
Practical Python Data Visualization by
Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book's programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations. You'll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python's popular data visualization charting library. You'll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You'll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets. The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. With Practical Python Data Visualization you'll master the core concepts of data visualization with Pandas and the Jupyter notebook interface. What You'll Learn Review practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with Pandas Who This Book Is For Data Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.
Python Data Analytics: Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language by
Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Author Fabio Nelli expertly shows the strength of the Python programming language when applied to processing, managing and retrieving information. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts, reporting systems, and data export. This book examines how to go about obtaining, processing, storing, managing and analyzing data using the Python programming language. You will use Python and other open source tools to wrangle data and tease out interesting and important trends in that data that will allow you to predict future patterns. Whether you are dealing with sales data, investment data (stocks, bonds, etc.), medical data, web page usage, or any other type of data set, Python can be used to interpret, analyze, and glean information from a pile of numbers and statistics. This book is an invaluable reference with its examples of storing and accessing data in a database; it walks you through the process of report generation; it provides three real world case studies or examples that you can take with you for your everyday analysis needs.
A Python Data Analyst's Toolkit by
Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts - programming with Python, data analysis and visualization, and statistics. You'll start with an introduction to Python - the syntax, functions, conditional statements, data types, and different types of containers. You'll then review more advanced concepts like regular expressions, handling of files, and solving mathematical problems with Python. The second part of the book, will cover Python libraries used for data analysis. There will be an introductory chapter covering basic concepts and terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like Matplotlib and Seaborn. Case studies will be included as examples to help readers understand some real-world applications of data analysis. The final chapters of book focus on statistics, elucidating important principles in statistics that are relevant to data science. These topics include probability, Bayes theorem, permutations and combinations, and hypothesis testing (ANOVA, Chi-squared test, z-test, and t-test), and how the Scipy library enables simplification of tedious calculations involved in statistics. What You'll Learn Further your programming and analytical skills with Python Solve mathematical problems in calculus, and set theory and algebra with Python Work with various libraries in Python to structure, analyze, and visualize data Tackle real-life case studies using Python Review essential statistical concepts and use the Scipy library to solve problems in statistics Who This Book Is For Professionals working in the field of data science interested in enhancing skills in Python, data analysis and statistics.
R Programming Fundamentals by
Study data analysis and visualization to successfully analyze data with R Key Features Get to grips with data cleaning methods Explore statistical concepts and programming in R, including best practices Build a data science project with real-world examples Book Description R Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. To start with, you'll understand you how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops. Once you have grasped the basics, you'll move on to studying data visualization and graphics. You'll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you'll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values. By the end of this book, you'll have completed an entire data science project of your own for your portfolio or blog. What you will learn Use basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control Import data to R from various formats such as CSV, Excel, and SQL Clean data by handling missing values and standardizing fields Perform univariate and bivariate analysis using ggplot2 Create statistical summary and advanced plots such as histograms, scatter plots, box plots, and interaction plots Apply data management techniques, such as factoring, pivoting, aggregating, merging, and dealing with missing values, on the example datasets Who this book is for R Programming Fundamentals is for you if you are an analyst who wants to grow in the field of data science and explore the latest tools.
R Programming by Example by
This step-by-step guide demonstrates how to build simple-to-advanced applications through examples in R using modern tools. Key Features Get a firm hold on the fundamentals of R through practical hands-on examples Get started with good R programming fundamentals for data science Exploit the different libraries of R to build interesting applications in R Book Description R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable. What you will learn Discover techniques to leverage R's features, and work with packages Perform a descriptive analysis and work with statistical models using R Work efficiently with objects without using loops Create diverse visualizations to gain better understanding of the data Understand ways to produce good visualizations and create reports for the results Read and write data from relational databases and REST APIs, both packaged and unpackaged Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel Who this book is for This books is for aspiring data science professionals or statisticians who would like to learn about the R programming language in a practical manner. Basic programming knowledge is assumed.
Introduction to Data Science by
"The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book"--
Using OpenRefine by
The book is styled on a Cookbook, containing recipes - combined with free datasets - which will turn readers into proficient OpenRefine users in the fastest possible way.This book is targeted at anyone who works on or handles a large amount of data. No prior knowledge of OpenRefine is required, as we start from the very beginning and gradually reveal more advanced features. You don't even need your own dataset, as we provide example data to try out the book's recipes.
Beginning Data Science in R by
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.
Hands-On Data Science with R by
A hands-on guide for professionals to perform various data science tasks in R Key Features Explore the popular R packages for data science Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on examples and use-cases in R Book Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learn Understand the R programming language and its ecosystem of packages for data science Obtain and clean your data before processing Master essential exploratory techniques for summarizing data Examine various machine learning prediction, models Explore the H2O analytics platform in R for deep learning Apply data mining techniques to available datasets Work with interactive visualization packages in R Integrate R with Spark and Hadoop for large-scale data analytics Who this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course