Learning
Analyzing Quantitative Data by ISBN: 0761967591Publication Date: 2003-03-06What basic knowledge and skills do novice researchers in social science require? How can students be helped to over-come `symbol phobia′ or `figure blindness′? This generous and constantly insightful book is designed for social researchers who need to know what procedures to use under what circumstances, in practical research projects. It accomplishes this without requiring an in-depth understanding of statistical theory, but also avoids both trivializing procedures or resorting to `cookbook′ techniques. Among the key features of the book are: - Accessibility - Organization of the wide, often bewildering array of methods of data analysis into a coherent and user-friendly scheme of classification: types of analysis and levels of measurement - Demystification - the first chapter unpacks commonly taken-for-granted concepts such as `analysis′, `data′ and `quantitative′ - Location of methods in real research problems The book is a triumphant introduction to the theory and practice of quantitative methods. It will quickly establish itself as essential reading for students doing social research throughout the social sciences. `With this book Norman Blaikie retains his reputation as the leading rapporteur and raconteur of social research methodology. With many other introductory texts, data analysis becomes just an exercise unto itself, and students (sometimes) learn to go through the motions without really knowing why. After working with Blaikie′s text, novice researchers will know why quantitative inquiry is important′ - Ray Pawson, University of Leeds
- Beginning data analysis with python and JupyterGetting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.
- Data analysis with PythonPython is a popular, easy to use, programming language, applied in a wide range of disciplines. Data Science is an interdisciplinary field that employs techniques to extract knowledge from data.
- Data analysis with R [electronic resource] : a comprehensive guide to manipulating, analyzing, and visualizing data in RLearn to load, wrangle, and analyze your data using R - the world's most powerful statistical programming language.
- Graphical data analysis with R"....the book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results...." Provided by the publisher
- Learn Data Analysis with Python Lessons in CodingGet started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it.
- Mastering Data Analysis with R"Gain sharp insights into your data and solve real-world data science problems with R from data munging to modeling and visualization. Handle your data with precision and care for optimal business intelligence. Restructure and transform your data to inform decision-making. Packed with practical advice and tips to help you get to grips with data mining. If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you...." Provided by the publisher.
- Qualitative data analysis from start to finish"This is the ideal book to get you up and running with the basics of qualitative data analysis. It breaks everything down into a series of simple steps and introduces the practical tools and techniques you need to turn your transcripts into meaningful research..." Provided by the Publisher
- Qualitative data analysis with NVivo"...It sets out: The power and flexibility of the NVivo software. How best to use NVivo at each stage in your research project. Examples from the authors′ own research and the sample data that accompanies the software, supplemented with vignettes drawn from across the social sciences...." Provided by the publisher
- The SAGE handbook of qualitative data analysisThis handbook is the first to provide a state-of-the art overview of the whole field of QDA; from general analytic strategies used in qualitative research, to approaches specific to particular types of qualitative data, including talk, text, sounds, images and virtual data.
- Advances in data analysis and classificationThe international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data.
Qualitative
- Qualitative Research JournalDevoted to the communication of the theory and practice of qualitative research in the human sciences.
- International journal of qualitative methodsThe International Journal of Qualitative Methods is the peer-reviewed interdisciplinary open access journal of the International Institute for Qualitative Methodology (IIQM) at the University of Alberta, Canada.
Quantitative
- Statistical Analysis and Data MiningStatistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact.
- Statistical Analysis & Data MiningAddresses the broad area of data analysis, including data mining algorithms, statistical approaches and practical applications.
- Computational statistics & data analysisComputational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis.
Humanities
- Journal of Cultural AnalyticsThe Journal of Cultural Analytics is an open-access journal dedicated to the computational study of culture. Its aim is to promote high quality scholarship that applies computational and quantitative methods to the study of cultural objects (sound, image, text), cultural processes (reading, listening, searching, sorting, hierarchizing) and cultural agents (artists, editors, producers, composers).
- Journal of Data Mining and Digital HumanitiesDigital Humanities is a convergence of humanities fields (linguistic, history, psychology…) using data archives, processing and interaction. Data mining is an interdisciplinary subfield of computer science, involving the methods at the intersection of artificial intelligence, machine learning and database systems.
- Journal of Open Humanities DataThe Journal of Open Humanities Data (JOHD) aims to be a key part of a thriving community of scholars sharing humanities data. The journal features peer reviewed publications describing humanities research objects or techniques with high potential for reuse.
- Research Data Journal for the Humanities and Social SciencesThe Research Data Journal for the Humanities and Social Sciences (RDJ) is a peer-reviewed journal, which is designed to comprehensively document and publish deposited datasets and to facilitate their online exploration.
- WashU Data Service Youtube ChannelRecordings of workshops and tutorials for data services.
The Data Services team offers workshops in the fall and spring semesters on important Data Analysis subjects. This list includes previous sessions that may be offered on a recurring basis. If you would like to see one of these sessions offered again, please contact Data Services.
R vs Python: Love them Both?
This hands-on workshop introduce common data analysis tasks in the R and Python programming languages and explore which language is better suited for a particular task. Basic code for data manipulation and visualization will be run using R Studio Cloud and the repl.it environment for Python. Basic proficiency in R and/or Python is recommended.
Exploring Your Data With OpenRefine (Fall 2021)
OpenRefine is a powerful open source tool to deal with messy data by running a small server on your computer and interacting with data in a web browser. The advantages of OpenRefine are that it can handle a large amount of data and that your data is kept private until you want to share it. This workshop gives general overview of OpenRefine along with focusing on useful tasks such as faceting, clustering, and using regular expressions to manipulate values. This workshop was taught by Dorris Scott and Mollie Webb from Data Services.
All WashU faculty, staff, and students have access to online training classes through LinkIn Learning (formerly lynda.com). See this site for access information. LinkedIn Learning offers several courses on Data Analysis including...
- General Data Analysis
- Python
- R
- Excel
The links below provide access to several common data analysis tools and their associated documentation or training resources:
- ArcGIS Spatial Analysis
- Training and Documentation
- ESRI Spatial Statistics Resources - videos, presentations, documentation
- R-ArcGIS Bridge - leverage R libraries with ArcGIS data visualization
- Training and Documentation
- Python Software Foundation - source for software, documentation, and training
- Training & Documentation
- Python Documentation
- Python Wiki
- Data Camp - online training courses; all first chapters are free
- Training & Documentation
- R & R-Studio
- R Project Organization - software environment for statistical computing and graphics
- R-Studio - open source data science software
- Training & Documentation
- Tableau - data analysis and visualization software
- Free one-year license for students or teachers
- Tableau Public - free, limited capability version
- Training & Documentation