Google Data Analytics Professional Certificate

Certificate of completion - https://www.coursera.org/account/accomplishments/professional-cert/7YAGB7QFPFH7

Click here to view the capstone project, which includes detailed explantion for each step.

Or copy the URL below then paste it into a browser:
https://kevinvchin.github.io/Google-Data-Analytics-Professional-Certificate/Cyclistic/index.html

This repository contains the capstone project for the Google Data Analytics Professional Certificate program.

The program is offered through an eight-course series which contains a comprehensive curriculum towards specific aspects of data analytics. These courses are:

1. Foundations: Data, Data, Everywhere

   • Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems.
   • 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.

2. Ask Questions to Make Data-Driven Decisions

   • Explain how the problem-solving road map applies to typical 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.

3. Prepare Data for Exploration

   • Explain what 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.

4. Process Data from Dirty to Clean

   • Define different types of data integrity and identify risks to data integrity.
   • Apply basic SQL functions to clean string variables in a database.
   • Develop basic SQL queries for use on databases.
   • Describe the process of verifying data cleaning results.

5. Analyze Data to Answer Questions

   • Discuss the importance of organizing your data before analysis by using sorts and filters.
   • Convert and format data.
   • Apply the use of functions and syntax to create SQL queries to combine data from multiple database tables.
   • Describe the use of functions to conduct basic calculations on data in spreadsheets.

6. 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.

7. 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 in R Markdown to create structure and emphasize content.

8. Google Data Analytics Capstone: Complete a Case Study

   • Differentiate between a capstone project, 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.