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Data Analytics & Visualization

Introduction to data analytics and visualization, software programs and the available consultation service from the Digital Lab at the Research Informatics and Publishing.

Data Analytics & Visualization Guide


  • What is Data Analytics? It is the science of examining raw data with the purpose of drawing conclusions about that information. - SearchDataManagement
  • What is Data Visualization? Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. - Wiki
  • What are the differences Data Visualization vs. Data Analytics? Data visualization represents data in a visual context by making explicit trends and patterns inherent in the data; Data analytics go a step deeper, identifying or discovering the trends and patterns inherent in the data with tools and algorithms. - Fingent
  • How are Data Analytics and Data Visualization integrated into one workflow?- Alam et al. 2017

The general workflow for data analytics and visualization is to gather data sources; consolidate data, preprocess data including filtering, aggregation, transformation and other data tasks; model data, including creating models, estimating and validating models; analyze results, including description, prediction, prescriptions and impact evaluation; visualize the result with user interaction.

  • What are the types of Data Analytics applications?
  1. Business Intelligence (BI) and reporting: it provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. – SearchDataManagement
  2. Advanced Data Analytics:it includes data mining, which involves sorting through large data sets to identify trends, patterns and relationships; predictive analytics, which seeks to predict customer behavior, equipment failures and other future events; and machine learning, an artificial intelligence technique that uses automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modeling. Big data analytics applies data mining, predictive analytics and machine learning tools to sets of big data that often contain unstructured and semi-structured data. Text mining provides a means of analyzing documents, emails and other text-based content.  – SearchDataManagement

Available recourses and consultation service

Please feel free to use the following links to download the software you need and study the documentation as a starting point for your projects. If you would like to discuss more about your research projects, the Digital Lab from the Research Informatics and Publishing provides consultation service to the research community by appointments and it offers accesses to computers with the following software programs.

Database & Business Intelligence (BI): MS SQL SERVER,MS POWER BITableau

Big Data Analytics & Machine Learning through Google Cloud Platform (GCP): Google Cloud Platform (GCP)TensorFlowGoogle Big QueryGoogle Big DataPython

Beside the documentation from the official websites, we recommend other great learning resources, such as ICS-ACI that offers workshops, software programs and High Performance Computing (HPC) platforms. Or join PSU Data Analytics degree programs. 

Upcoming workshops

Penn State University Libraries will offer data visualization sessions highlighting services, such as Data Analytics & Visualization, and Map and Geospatial of the Libraries’ Research Informatics and Publishing department. The sessions will take place in the Digital Lab at Pattee Library, with remote viewing available online via Zoom.


These sessions are aimed at introducing the resources and expertise available at the University Libraries in these areas and offering an opportunity for hands-on experience with data analysis and visualization using the available software tools at the university libraries.

These sessions are free and open to all Penn State students, staff and faculty.


Dates for Data Analytics & Visualization Spring 2020 sessions

Data Visualization with Tableau

Thursday, Jan. 30, 2020, 2:00-3:30p.m., W312A Pattee Library, and via Zoom


Introduction to Database - MS Access & Excel

Thursday, Feb. 27, 2020, 2:00-3:30p.m., W312A Pattee Library, and via Zoom


Business Analytics with MS SQL Server & Business Intelligence (BI)/SSIS, SSAS, SSRS

Thursday, Mar. 26, 2020, 2:00-3:30p.m., W312A Pattee Library, and via Zoom


Data Visualization with Power BI Desktop

Thursday, Apr. 23, 2020, 2:00-3:30p.m., W312A Pattee Library, and via Zoom


The general scope and purpose for these sessions are to overview the subject of data analytics, also to explore some commonly used software tools for demonstrating data analysis and visualization process based on some public sample datasets. Information about online resources, available university libraries’ resources (computers and software programs), and one-on-one consultation service from Research Data Services at Research Informatics and Publishing will be provided. Advanced Registration is recommended.

Contact: Xuying Xin, Data Analyst (Certified Tableau Desktop Specialist),


Dates for Maps and Geospatial Spring 2020 sessions

Getting to know Geospatial Data and Mapping

Wednesday, February 19, 3-4:30pm, W13 Pattee Library and via Zoom

Wednesday, February 26, 3-4:30pm, W13 Pattee Library and via Zoom


This session will introduce participants to geospatial data from US and international sources, along with information on geospatial software access at Penn State including ArcGIS Online, ArcGIS ArcMap, and ArcGIS Pro. This session will provide a brief introduction to geospatial data techniques and analysis. Additional resources will be provided to help participants get started using geospatial software in projects. Resources from these Maps and Geospatial guides will be highlighted: Global Partners: International Geospatial Data, ArcGIS Online, ArcGIS Pro, and Geographic Information Systems (GIS).

Contact: Tara Anthony, Geospatial Specialist,