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Data Science with Tableau

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Introductory

This course offers an accelerated intensive learning experience with Tableau – the growing standard in business intelligence for data visualization and dashboard creation. Without prior experience, students will learn to work with multiple data sources, create compelling visualizations, and roll out their data science products for continuous, scalable outputs to key stakeholders. By building insight and weaving narrative, students will be empowered to harness data in a striking way that provides value to organizations large and small.

Data Science with Tableau

This course offers an accelerated intensive learning experience with Tableau – the growing standard in business intelligence for data visualization and dashboard creation. Without prior experience, students will learn to work with multiple data sources, create compelling visualizations, and roll out their data science products for continuous, scalable outputs to key stakeholders. By building insight and weaving narrative, students will be empowered to harness data in a striking way that provides value to organizations large and small.

Course Overview

How can data visualizations be standardized and pipelined for viewing by key decision makers & analysts across an organization? Made to be compelling, informative, and appealing to the eye? Draw upon data sources as various as relational database servers (e.g. SQL), spreadsheets (e.g. Google Sheets), Salesforce, or web-based data? Process ‘big data’ in a live manner for the most up-to-date output? Through our accelerated introduction, students will become fluid in using Tableau’s features to achieve all of these objectives.

This course offers an accelerated intensive learning experience with Tableau – the growing standard in business intelligence for data visualization and dashboard creation. Without prior experience, students will learn to work with multiple data sources, create compelling visualizations, and roll out their data science products for continuous, scalable outputs to key stakeholders. By building insight and weaving narrative, students will be empowered to harness data in a striking way that provides value to organizations large and small. We utilized 4 user cases drawn from finance (public data from major stock exchanges) and sitcom data (Game of Throne 1 ).

This course robustly covers 90-95% of what’s needed for the qualified associate Tableau Certificate.

Prerequisites

Know how to use Mac, Windows. Familiarity with relational databases will be nice but not required:

  • gain a greater appreciation for the logic underlying Tableau’s features
  • utilize their capstone project to visualize ‘big data’ by accessing a variety of server- or cloud-based sources
  • Before the course begins, pre-work will be available for students interested in strengthening their ability to access and extract data from relational databases (e.g. SQL-based servers).

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course. Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.

Syllabus

Week 1

  • Importance of Tableau and How It Fits With Computing, Learning, & Analysis Today
  • Connecting with Data & Using Multiple Sources (Relational Logic, Joining, Blending)
  • Organizing your Data (Sorting, Filtering, Hierarchies, Groups, Subsets, Labeling, Aggregation)
  • Measures in View (Individual/Blended/Dual Axes, Cross-tabs, Highlight Tables, Reference Lines)

Week 2

  • Calculations in Tableau (Calculation Syntax & Functions, Level of Detail)
  • Creating and Using Parameters To Power Interactive Visualizations
  • Visualizing Relationships Between Numerical Values (Scatter Plots, Heat Maps)
  • Visualizing Breakdowns of the Whole (Boxplots, Tree Maps, Donut Chart, Sunburst Diagrams)

Week 3

  • Visualizing Structure, Relationships, & Trends through Machine Learning (Clustering, Regression, Forecasting, Integrating R Libraries)
  • Mapping Data Geographically
  • Bringing Together Visualization To Offer Insight & Narrative (Dashboards, Stories)

Week 4

  • Develop capstone project using your own data and have your own goal
  • Project deliverable sheet
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