Research Guides: Finding and Evaluating Data: Data Analysis

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Data analysis

Center for Research Data & Digital Scholarship Established in 2017, the Center for Research Data and Digital Scholarship is a partnership between Research Computing and the University Libraries. We are a collaborative team of data and information professionals, scholars, and educators with expertise in data-intensive scholarship and teaching. Our goal is to serve as a interdisciplinary hub and resource on campus that empowers everyone as they navigate the Research Lifecycle, through workshops and seminars, certificate programs, and collaborative opportunities.
Center for Research Data & Digital Scholarship: Learning Materials Check out CRDDS-produced videos, tutorials, and other learning materials.
Research Computing at CU Our Mission: CU Boulder Research Computing (RC) supports fair and equitable access for a diverse set of stakeholders to innovative large-scale computing and data resources emphasizing open, stable, and secure access to data while maintaining required measures of compliance for our sponsors.

SAGE Research Methods Cases A collection of case studies about social research that are designed to help researchers understand abstract methodological concepts by introducing them to case studies that applied those methods in practice.
SAGE Research Methods Datasets A collection of case studies about social research that are designed to help researchers understand abstract methodological concepts by introducing them to case studies that applied those methods in practice.
SAGE Research Methods Online Information about writing a research question, conducting a literature review, choosing a research method, collecting data, and writing up the findings. Coverage spans the full range of research methods used in the social and behavioral sciences, plus a wide range of methods used commonly in sciences, health sciences, and humanities.

The Subtle Sources of Sampling Bias Hiding in Your Data by Ransbotham, Sam
Plummeting data acquisition costs have been a big part of the surge in business analytics. We have much richer samples of data to use for insight. But more data doesn't inherently remove sampling bias; in fact, it may make it worse.
An Introduction to Data Analysis by Tiffany Bergin Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques   - Data management - The big data phenomenon   - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.
An introduction to data analysis in R : hands-on coding, data mining, visualization and statistics from scratch by Alfonso Zamora Saiz, Carlos Quesada González, Lluís Hurtado Gil, Diego Mondéjar Ruiz. This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications.
Python for data analysis : data wrangling with Pandas, NumPy, and Jupyter by Wes McKinney Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third 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, and Jupyter in the process.
Intelligent Data Analysis by Deepak Gupta (Editor); Siddhartha Bhattacharyya (Editor); Ashish Khanna (Editor); Kalpna Sagar (Editor) This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools.

Coursera Data Analysis Courses

Yale's List of 10 Free Data Analytics Courses Data analytics is the science of taking raw data, cleaning it, and analyzing it to inform conclusions and support decision making. From business to health care to social media, data analytics is changing the way organizations operate.
Udemy's Free Data Analysis Courses and Tutorials
MITx MicroMasters Program
Harvardx Professional Certificate in Data Science You can take courses individually for free but have to pay to get the certificate.