Google Data Analytics Professional Training Course

Course Outline

Foundations: Data, Data, Everywhere

Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystem
Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking
Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics
Describe the role of a data analyst with specific reference to jobs/positions

Ask Questions to Make Data-Driven Decisions

Explain how each step of the problem-solving road map contributes to common analysis scenarios.
Discuss the use of data in the decision-making process.
Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data.
Describe the key ideas associated with structured thinking.

Prepare Data for Exploration

Explain factors to consider when making decisions about data collection
Discuss the difference between biased and unbiased data
Describe databases with references to their functions and components
Describe best practices for organizing data

Process Data from Dirty to Clean

Define data integrity with reference to types of integrity and risk to data integrity
Apply basic SQL functions for use in cleaning string variables in a database
Develop basic SQL queries for use on databases
Describe the process involved in verifying the results of cleaning data

Analyze Data to Answer Questions

Discuss the importance of organizing your data before analysis with references to sorts and filters
Demonstrate an understanding of what is involved in the conversion and formatting of data
Apply the use of functions and syntax to create SQL queries for combining data from multiple database tables
Describe the use of functions to conduct basic calculations on data in spreadsheets

Share Data Through the Art of Visualization

Describe the use of data visualizations to talk about data and the results of data analysis
Identify Tableau as a data visualization tool and understand its uses
Explain what data driven stories are including reference to their importance and their attributes
Explain principles and practices associated with effective presentations

Data Analysis with R Programming

Describe the R programming language and its programming environment
Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors
Describe the options for generating visualizations in R
Demonstrate an understanding of the basic formatting R Markdown to create structure and emphasize content

Google Data Analytics Capstone: Complete a Case Study

Differentiate between a capstone, case study, and a portfolio
Identify the key features and attributes of a completed case study
Apply the practices and procedures associated with the data analysis process to a given set of data
Discuss the use of case studies/portfolios when communicating with recruiters and potential employers