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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

Introduction

  • 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 https://lynda.psu.edu/and ICS-ACI that offers workshops, software programs and High Performance Computing (HPC) platforms. Or join PSU Data Analytics degree programs. 

Upcoming workshops

Data Analytics & Visualization: Relational Database – MS Access

Date and time: TBD,  Late January, 2020

Location: Pattee W312A (Digital Lab) and via Zoom

 

This workshop is an introduction to relational database: a set of related tables from which data can be extracted in many different ways using the standard structural query language (SQL). This session focuses on MS Access, a basic database system with graphic user interface (GUI) and development tools. Participants will be given a dataset to work with to explore the tools available in Access for creating database, tables, forms, reports and queries. Information for additional resources and tools for data analytics and visualization are available in the Data Analytics & Visualization Guide.

 

Advanced registration is recommended. 

 

Presenter: Xuying Xin, Data Analyst (Research Informatics and Publishing), xzx1@psu.edu