Course

Course Summary
Credit Type:
Course
ACE Course Number:
SKIL-0227
Organization:
Location:
Online
Length:
56 hours and 32 lab hours
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 4 reporting and data analytics
Lower-Division Baccalaureate 2 reporting and business analytics
Description

Objective:

The course objective is to provide you with the knowledge and skills required to transition from a Business Analyst to a Data Analyst. The course starts by exploring many of the important features in Excel, a powerful and commonly used data analysis tool. It then covers the most popular data visualization tools and techniques. Learners will also practice data analysis using Python. This includes gathering, exploration, cleaning, and transforming data using Python. BigML, which is a popular machine learning platform, is also covered.

Learning Outcomes:

  • explore the many important features and tools within Excel, such as operations, data import and export, customizing toolbars, and data visualizations using various bars and charts
  • discover various statistical analysis, data validations, data cleaning, data manipulations, pivot tables, and modelling relationships among data
  • visualize data with Tableau
  • create business reports using Power BI
  • explore Google Chart Tools
  • visualize data with Plotly
  • program in SQL with MariaDB
  • automate Excel and Access using VBA
  • drag and drop machine learning using Big ML
  • and analyze data using Python.

General Course Topics:

  • Complete guide to Excel 365: getting started
  • Complete guide to Excel 365: working with charts and Sparklines
  • Complete guide to Excel 365: using formatting, styles, and themes
  • Complete guide to Excel 365: linking, printing, and protecting workbooks
  • Complete guide to Excel 365: validating, cleaning, and performing lookups on data
  • Complete guide to Excel 365: what-if analysis, solver, and analysis ToolPak
  • Complete guide to Excel 365: Pivot, PowerPivot, and financial modeling
  • Tableau for data visualization: introduction
  • Tableau for data visualization: exploring visualizations and data formats
  • Tableau for data visualization: advanced features
  • Business reporting: getting started with Power BI desktop for data analysis
  • Business reporting: visualizing and merging data in Power BI
  • Business reporting: creating and formatting matrix visualizations in Power BI
  • Business reporting: leveraging Treemaps, matrices, and slicers in Power BI
  • Google chart tools: basic charts
  • Google chart tools: interacting with charts
  • Google chart tools: advanced visuals with charts
  • Plotly for data visualization: an introduction to Plotly chart studio
  • Plotly for data visualization: exploring chart studio visualizations
  • Plotly for data visualization: advanced charts and features in chart studio
  • SQL programming with MariaDB: getting started with MariaDB for data analysis
  • SQL programming with MariaDB: analyzing relational data
  • SQL programming with MariaDB: using joins, triggers, and stored procedures
  • Getting started with VBA
  • Using BigML
  • Analyzing data using Python
Instruction & Assessment

Instructional Strategies:

  • Laboratory
  • Practical Exercises

Methods of Assessment:

  • Examinations
  • Quizzes

Minimum Passing Score:

70%
Supplemental Materials