COMM 205 Notes - Class 6,8 - Tableau PDF

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2021/22 COMM 205 Class 6,8 notes on Tableau...

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Class 6 - Tableau Part 1

Chapter 4 - Data and Databases Data, Information, and Knowledge

Data: raw facts; lacks content & intent; can be qualitative (descriptive) or quantitative (numbers) Information: processed data that has context, relevance, and purpose; involves manipulation of raw data to obtain an indication of magnitude, trends, in patterns in the data for a purpose - ex) monthly sales from sales data - Knowledge: human beliefs or perceptions about relationships among facts or concepts relevant to that area - After putting data into context, aggregated, and analyzed it, can use it to make decisions for org - Explicit knowledge: knowledge that can be expressed into words or numbers - Tacit knowledge: includes insights and intuitions, and is difficult to transfer to another person by means of simple communications - Wisdom: can combine their knowledge and experience to produce a deeper understanding of a topic Big Data - Big data: massively large data sets that conventional data processing technologies do not have sufficient power to analyze them - ex) Walmart must process millions of customer transactions every hour across the world. Storing and analyzing that much data is beyond the power of traditional data management tools Finding Value in Data: Business Intelligence - Business intelligence: describe the process that organizations use to take data they are collecting and analyze it in the hopes of obtaining a competitive advantage Data Visualization - Data visualization: graphical representation of information and data (i.e. charts, graphs, maps) - Can quickly summarize data in a way that is more intuitive and can lead to new insights and understandings - Graphical representation of data can quickly make meaning of large amounts of data -

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Many times the first step towards a deeper analysis and understanding of the data collected by an org - ex) Tableau Data Warehouses - Data warehouse: extract data from one or more of the organization’s databases and load it into the data warehouse (which is itself another database) for storage and analysis - Needs to meet the following criteria: - Uses non-operational data -- data warehouse is using a copy of data from the active databases that the company uses in its day-to-day operations, so the data warehouse must pull data from the existing databases on a regular, scheduled basis - Data is time-variant -- whenever data is loaded into the data warehouse, it receives a time stamp, which allows for comparisons between different time periods - Data is standardized -- need to put data in same units, definitions, date formats, etc. - Extraction-transformation-load (ETL): when a standard date format is agreed upon and all data loaded into the data warehouse would have to be converted to use this standard format - 2 main schools of thought when designing data warehouse: bottom-up and top-down - Bottom-up approach: starts by creating small data warehouses, called data marts, to solve specific business problems. As these data marts are created, they can be combined into a larger data warehouse - Top-down approach: start by creating an enterprise-wide data warehouse and then, as specific business needs are identified, create smaller data marts from the data warehouse Data Mining and Machine Learning - Data mining: process of analyzing data to find previously unknown and interesting trends, patterns, and associations in order to make decisions - Generally accomplished through automated means against extremely large data sets, such as a data warehouse - Machine learning: used to analyze data and build models without being explicitly programmed to do so - 2 main branches: supervised learning & unsupervised learning - Supervised learning: organization has data about past activity that has occurred and wants to replicate it - Called “supervised” learning because we are directing (supervising) the analysis towards a result - Unsupervised learning: organization has data and wants to understand the relationship(s) between different data points - Called “unsupervised” learning because no specific outcome is expected? Sidebar: What is data science? What is data analytics? - Data science: describe the analysis of large data sets to find new knowledge

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Focus is generally on analyzing large data sets using various programming methods and software tools to create new knowledge for their organization

Lecture Tableau overview - Tableau - dynamic, interactive reporting, business intelligence, data visualization software tool produced by Tableau Software - Can help anyone see and understand their data - Connect to almost any database, drag and drop to create visualizations, and share with a click - Provide more value to clients w/ more interactive dashboards - 4 join options: inner, left, right, full outer Tableau products suite - Tableau Desktop - From creating reports, charts, formatting them, putting them together as a dashboard, all the work is done on Tableau Desktop - Tableau Reader - A free desktop application that you can use to open and interact with data visualizations built in Tableau Desktop - Tableau Server - The dashboards you create can be shared with other users using Tableau Server - Tableau Online - A hosted version of Tableau Server in the cloud - Tableau Mobile - A free App that lets users explore and share content published to Tableau Server Tableau file types - Tableau Workbook (.twb) - Stores a visualization without source data - Tableau Data Extract (.tde) - Stores Tableau data as filtered and aggregated extract - Tableau Packaged Workbook (.twbx) - Stores extracted data and visualizations for viewing in Tableau - We are using this in class - Can go back to actual sourced data Understanding Tableau worksheets Dimensions and measures - When you first connect to a data source, Tableau assigns any fields that contain: - Discrete categorical information (e.g. fields where the values are Strings or Boolean values) to the Dimensions area in the Data pane - Quantitative, numerical information (i.e. fields where the values are numbers) to the Measures area in the Data pane - When you drag a field from the Measures area to Rows or Columns, Tableau creates a continuous axis -

Shown as a green pill

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When you drag a field from the Dimensions area to Rows or Columns, Tableau creates column or row headers (discrete) -

Shown as a blue pill

“Show Me” - Show Me highlights the visualization type that best matches the data fields you added to the columns and rows - Any view type that is not gray will generate a view of your data

Class 8 - Tableau Part 2 -

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Can set 1/+ categorical filters as context filters - Context filter = independent filter; everything else is dependent bcs it passes through the ind filter Adding context creates dependent filters Create filter by… - Dragging & dropping category into “filters” box - To find “top X” of smth, click “top” then choose value of “X” Creating calculated field is making a custom field “IF” & “SUM” example on Tableau - IF SUM([Profit]) > 0 THEN "positive" ELSE "negative" END - Can drag & drop “sign of profit filter” that you made to “colours” to colour-code which values are pos vs neg -- can see data on an aggregated level If there’s a start & end date in data, use “DATEDIFF” Will add a dimension in for any qualitative product so calculated field will use a dimension; thus, both dimensions and measures can be used Once you add a filter for a subcategory, you MUST add to context so that it applies (or “locks in”) to the specific product/country you’re looking for -- otherwise, it finds the top 3 subcategories for ex for ALL products/countries Total costs = SUM([Sales]) - SUM([Profit])...