What is Data Analytics and Visualization?

Data analytics is the method of examining data sets (structured or unstructured) in order to get useful insights to draw conclusions about the datasets. ​

Data visualization is the graphical representation of information and data designed to help people carry out tasks more effectively. ​

What are the types of Data Analytics and Visualization?

• 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

• 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, and 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

What is the general workflow for data analytics and visualization? 

It always starts with gathering data. Planning for data collection is very important because what data is collected and how it is organized determines how easily the data can be processed for analysis and visualization down the road. When data is not ready to be analyzed data preprocessing is needed, which includes data combination, data cleaning, data transformation, and data modeling. Once data is cleaned and modeled, it is ready for analysis and visualization. The last step of the workflow is to share the data visualization with colleagues either within or outside the organization. It can be done using data analytics tools with their online services.

Commonly used data analytics and visualization tools

Business Analytics

Download links (MS applications are free to PSU users as part of the Office 365 group license, login for free download)

• POWER BI
  • PC version: https://www.microsoft.com/en-us/download/details.aspx?id=58494
  • Online access: https://powerbi.microsoft.com/en-us/landing/signin/  
• Tableau
  • Tableau Public is free to anyone, but files can only be saved to a public server, it works on both PC and Mac: https://public.tableau.com/en-us/s/download;
  • Tableau offers a free one-year Tableau Desktop license for teachers and students:
    • Tableau for students: https://www.tableau.com/academic/students
    • Tableau for teaching: https://www.tableau.com/academic/teaching
• MS SQL SERVER Express (PC): https://www.microsoft.com/en-us/sql-server/sql-server-downloads
• MS Access (PC): https://www.microsoft.com/en-us/microsoft-365/buy/compare-all-microsoft-365-products?tab=1&rtc=1

Learning resources

• MS Power BI: https://docs.microsoft.com/en-us/power-bi/guided-learning/; https://catalog.libraries.psu.edu/catalog/29282447
• Tableau Public: https://public.tableau.com/en-us/s/resources; https://www.tableau.com/learn/training/20194; https://www.tableau.com/learn/training/elearning     
• MS Access: https://support.microsoft.com/en-us/office/access-video-training-a5ffb1ef-4cc4-4d79-a862-e2dda6ef38e6
• MS SQL Server: https://docs.microsoft.com/en-us/sql/sql-server/tutorials-for-sql-server-2016?view=sql-server-ver15
• MS Excel: https://support.microsoft.com/en-us/office/excel-for-windows-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb

Big Data Analytics & Machine Learning

Access to the Cloud Platforms with AI/Machine Learning tools for Nature Language Processing (NLP), Auto Machine Learning, Bot Services, etc. 

• MS Azure (Get started with 12 months of free services): https://azure.microsoft.com/en-us/free/
• Google Cloud Platform/GCP (Start running workloads on GCP for free): https://cloud.google.com/free/
• Amazon Web Services (AWS): https://aws.amazon.com/ 

Learning Resources

• Penn State Cloud Services: https://www.cloud.psu.edu/  
• MS Azure: https://docs.microsoft.com/en-us/learn/paths/azure-fundamentals/
• Google Cloud Platform (GCP): https://cloud.google.com/bigquery/docs/tutorials
• Amazon Web Services (AWS): https://aws.amazon.com/what-is-aws/?nc2=h_ql_le_int