Описание: This book presents an introductory overview of spatial data analysis methods and geoinformation analysis techniques. Each chapter introduces the related theory, explains how to interpret metrics outputs, and provides worked examples using ArcGIS and GeoDa. This is a valuable resource for graduate students and researchers analyzing geospatial data.
Jump-start your career as a data scientist--learn to develop datasets for exploration, analysis, and machine learning
SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls.
You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data.
This guide for data scientists differs from other instructional guides on the subject. It doesn't cover SQL broadly. Instead, you'll learn the subset of SQL skills that data analysts and data scientists use frequently. You'll also gain practical advice and direction on "how to think about constructing your dataset."
Gain an understanding of relational database structure, query design, and SQL syntax
Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms
Review strategies and approaches so you can design analytical datasets
Practice your techniques with the provided database and SQL code
In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner's perspective, moving your data scientist career forward
Автор: Titscher S et al Название: Methods of Text and Discourse Analysis ISBN: 0761964835 ISBN-13(EAN): 9780761964834 Издательство: Sage Publications Рейтинг: Цена: 9346.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An overview of linguistic and sociological approaches to text and discourse analysis.
Описание: Discusses the issues that the agricultural industry currently faces and the technological opportunities that can be explored to help protect and predict crop growth and achieve more resilient agricultural processes. The book analyses the impact of agricultural pollution and food insecurity and proposes solutions to promote sustainability.
Название: Learn Data Analysis with Python ISBN: 1484234855 ISBN-13(EAN): 9781484234853 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Table of Contents 1. IntroductionHow to use this bookInstalling iPython NotebookWhat is iPython notebook?What is Anaconda?Getting StartedGetting the datasets for the workbook's exercises2. Getting Data into and out of PythonLoading Data from CSV FilesSaving Data to CSVLoading Data from Excel FilesSaving Data to Excel FilesCombining Data from Multiple Excel Files: Loading Data from SQLSaving Data to SQLRandom Numbers and Creating Random Data3. Preparing Data is Half the BattleCleaning DataCalculating and Removing OutliersMissing Data in Pandas DataframesFiltering Inappropriate ValuesFinding Duplicate RowsRemoving Punctuation from Column ContentsRemoving Whitespace from Column ContentsStandardizing DatesStandardizing Text like SSN's, Phone #'s and Zip CodesCreating New VariablesBinning DataApplying Function to Groups, Bins and ColumnsRanking Rows of DataCreate a Column Based on a ConditionalMaking New Columns Using FunctionsConverting String Categories to Numeric VariablesOrganizing the DataRemoving and Adding ColumnsSelecting ColumnsChange Column NameSetting Column Names to Lower CaseFinding Matching RowsFilter Rows Based on Conditions: Selecting Rows Based on ConditionsRandom Sampling Dataframe4. Finding the MeaningComputing aggregate statisticsComputing Aggregate Statistics on Matching RowsSorting DataCorrelationRegressionRegression without InterceptBasic Pivot TableRandom Sampling DataframeSelecting Pandas DataFrame Rows Based on ConditionsDistribution AnalysisCategorical Variable AnalysisTime Series Analysis5. Visualizing DataData Quality ReportGraph a Dataset - Line PlotGraph a Dataset - Bar PlotGraph a Dataset - Box PlotGraph a Dataset - HistogramGraph a Dataset - Pie ChartGraph a Dataset - Scatter PlotPlotting w/ ImagePlotting Data on a Map with BasemapPlotting a Gantt ChartSetting ticks, labels & gridsAdding legends & annotationsMoving Spines to the Center6. Practice ProblemsPivot Exercise 1Pivot Exercise 2Pivot Exercise 2Pivot Exercise 3LegendRegression Exercise 1Regression Exercise 2Regression Exercise 3Analysis ProjectNotes
Автор: Kumar Mukhiya Suresh, Ahmed Usman Название: Hands-On Exploratory Data Analysis with Python ISBN: 1789537258 ISBN-13(EAN): 9781789537253 Издательство: Неизвестно Рейтинг: Цена: 14712.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides practical knowledge about the main pillars of EDA including data cleaning, data preparation, data exploration, and data visualization. You can leverage the power of Python to understand, summarize and investigate your data in the best way possible. The book presents a unique approach to exploring hidden features in your data.
Автор: Karsdorp Folgert, Riddell Allen, Kestemont Mike Название: Humanities Data Analysis: Case Studies with Python ISBN: 0691172366 ISBN-13(EAN): 9780691172361 Издательство: Wiley Рейтинг: Цена: 7920.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A practical guide to data-intensive humanities research using the Python programming language
The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment.
The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter.
An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions.
Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python
Applicable to many humanities disciplines, including history, literature, and sociology
Offers real-world case studies using publicly available data sets
Provides exercises at the end of each chapter for students to test acquired skills
Emphasizes visual storytelling via data visualizations
Описание: Looking for methods that you can use to make yourself more competitive in your industry? Or are you worried about what your customers may think about your products or services?
The truth is...In the modern world, data has become fundamental, and companies are finding new ways to use the insights provided by data to improve their bottom line and customer experience. This is sometimes a challenge. There is so much data that figuring out what steps to take, and what is found in that data is not always as easy as we would like.
The good news is that working with data science can help you learn more about your customers and your industry, with the use of a simple coding language, and give you the insights and predictions that you need to see some great improvements with your business. And that's what you'll learn in Python for Data Science.
Python for Data Science is going to spend some time looking at all of the neat things that we can do with data science, so you can finally beat out the competition and increase your bottom line, all while helping out the customer.
You will learn:
Why Data Science is so Important in our Fast-Paced World of Today
5 Practical Applications of Data Science
Best Data Science Library that will help us to Get our Work Done with Python
A Simple Technique to Set Up our Virtual Environment
Proven Strategies to Get High-Quality Data
Tips and Tricks to Get your Data Organized
Data Analytics and Why It is so Important
What Machine Learning is all About and How it Fit into your Data Science Projects
Learning how data science works and how to complete your own data analysis in the process is going to be very important to the amount of success that you are going to see.
Even if you're completely new to data science or you've done some programming before and are looking to switch to an exciting new career track in Data Science, Python for Data Science will teach you all the practical techniques used by real-world data scientists and analysts to solve problems.
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Get this Book Now to Master Python for Data Science
Описание: Unlock the programming skills you need to prepare for a lucrative career in Data Science with this comprehensive introduction to Python programming for data analytics
Are you completely new to programming and want to learn how to code, but don't know where to begin? Are you looking to upgrade your data wrangling skills to future-proof your career and break into Data Science and Analytics?
If you answered yes to any of the questions above, then keep reading...
Data analysis has become a huge industry with tons of career potential and will remain relevant far into the foreseeable future.
With the exponential growth and explosion of new data and the focus on using data to improve customer experiences and carry out research, data analysts will be needed to process and make sense of large amounts of information, with Python being the language of choice because of its versatility.
In this guide, you're going to be shown everything you need to break into the world of Data Analysis with Python. Filled with tutorials for powerful libraries and practical, hands-on exercises, you're going to learn how to aggregate, munge, analyze and visualize data in Python.
Here's a sample of what you're going to discover in Python Data Analytics
Why Python is the perfect language to learn if you want to break into Big Data and data analytics
Core statistical models and computation methods you need to know about as a budding data analyst
How to master the CSV library for reading, writing and handling tabular data
Using the Xlrd library to extract data from Microsoft Excel files
How to convert text to speech using the powerful Win32.com library
How to use the NumPy library to carry out fundamental and basic scientific and technical computing
How to use the SciPy library to carry out advanced scientific and highly technical computing
Surefire ways to manipulate the easy-to-use data structures of the Pandas framework for high-performance data analysis
How to plot complex data, create figures and visualize data using the Python Matplotlib library
...and tons more
If you're completely new to programming and have never written a single line of code, but want to get started, this guide is perfect for as a crash guide to getting up to speed with programming in general.
Whether you're a programmer looking to switch into an exciting new field with lots of potential for the future, or a regular data analyst looking to acquire the skills needed to remain relevant in a fast-changing world, this guide will teach you how to master powerful libraries used in the real-world by experienced data scientists.
Автор: Mark Andrews Название: Doing Data Science in R: An Introduction for Social Scientists ISBN: 1526486768 ISBN-13(EAN): 9781526486769 Издательство: Sage Publications Рейтинг: Цена: 25186.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This approachable introduction to doing data science in R provides step-by-step advice on using data science tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually.
Автор: Mark Andrews Название: Doing Data Science in R: An Introduction for Social Scientists ISBN: 1526486776 ISBN-13(EAN): 9781526486776 Издательство: Sage Publications Рейтинг: Цена: 8237.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This approachable introduction to doing data science in R provides step-by-step advice on using data science tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually.
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