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Course Outline
Foundations: Data, Data, Everywhere
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Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystem
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Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking
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Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics
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Describe the role of a data analyst with specific reference to jobs/positions
Ask Questions to Make Data-Driven Decisions
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Explain how each step of the problem-solving road map contributes to common analysis scenarios.
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Discuss the use of data in the decision-making process.
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Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data.
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Describe the key ideas associated with structured thinking.
Prepare Data for Exploration
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Explain factors to consider when making decisions about data collection
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Discuss the difference between biased and unbiased data
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Describe databases with references to their functions and components
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Describe best practices for organizing data
Process Data from Dirty to Clean
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Define data integrity with reference to types of integrity and risk to data integrity
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Apply basic SQL functions for use in cleaning string variables in a database
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Develop basic SQL queries for use on databases
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Describe the process involved in verifying the results of cleaning data
Analyze Data to Answer Questions
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Discuss the importance of organizing your data before analysis with references to sorts and filters
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Demonstrate an understanding of what is involved in the conversion and formatting of data
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Apply the use of functions and syntax to create SQL queries for combining data from multiple database tables
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Describe the use of functions to conduct basic calculations on data in spreadsheets
Share Data Through the Art of Visualization
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Describe the use of data visualizations to talk about data and the results of data analysis
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Identify Tableau as a data visualization tool and understand its uses
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Explain what data driven stories are including reference to their importance and their attributes
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Explain principles and practices associated with effective presentations
Data Analysis with R Programming
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Describe the R programming language and its programming environment
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Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors
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Describe the options for generating visualizations in R
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Demonstrate an understanding of the basic formatting R Markdown to create structure and emphasize content
Google Data Analytics Capstone: Complete a Case Study
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Differentiate between a capstone, case study, and a portfolio
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Identify the key features and attributes of a completed case study
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Apply the practices and procedures associated with the data analysis process to a given set of data
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Discuss the use of case studies/portfolios when communicating with recruiters and potential employers
Requirements
- No degree or experience required.
35 Hours
