Course Outline
Foundations: Data, Data, Everywhere
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Define and articulate core data analytics concepts, including the definitions of data, data analysis, and the broader data ecosystem.
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Perform a self-assessment of analytical thinking skills, providing concrete examples of how this thinking is applied.
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Explore the critical roles played by spreadsheets, query languages, and data visualization tools within the field of data analytics.
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Outline the responsibilities of a data analyst, specifically referencing relevant job titles and roles.
Ask Questions to Make Data-Driven Decisions
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Explain how each stage of the problem-solving roadmap contributes to resolving common analysis scenarios.
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Discuss the integration of data into the decision-making process.
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Demonstrate proficiency in using spreadsheets for fundamental data analyst tasks, such as data entry and organization.
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Describe the core principles of structured thinking.
Prepare Data for Exploration
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Explain the key factors to consider when making decisions about data collection.
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Discuss the distinctions between biased and unbiased data sets.
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Describe databases, including their functions and core components.
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Describe best practices for organizing data effectively.
Process Data from Dirty to Clean
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Define data integrity, referring to various types of integrity and potential risks to data quality.
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Apply basic SQL functions to clean string variables within 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 data cleaning.
Analyze Data to Answer Questions
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Discuss the importance of organizing data before analysis, with specific reference to sorts and filters.
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Demonstrate an understanding of the processes involved in data conversion and formatting.
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Apply functions and syntax to create SQL queries that combine data from multiple database tables.
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Describe the use of functions to perform basic calculations on data in spreadsheets.
Share Data Through the Art of Visualization
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Describe the use of data visualizations to communicate data insights and analysis results.
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Identify Tableau as a data visualization tool and understand its applications.
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Explain the concept of data-driven stories, including their importance and key 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 fundamental R programming concepts, including functions, variables, data types, pipes, and vectors.
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Describe the options available for generating visualizations in R.
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Demonstrate an understanding of basic R Markdown formatting to create structure and emphasize content.
Google Data Analytics Capstone: Complete a Case Study
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Differentiate between a capstone project, a 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 and portfolios when communicating with recruiters and potential employers.
Requirements
- No degree or prior work experience is required.
Testimonials (3)
The final day which is the Machine Learning Topic
John Erick Baltazar - Globe Telecom
Course - Google BigQuery
It was a really good training course, well prepared and explained by the trainer with great hands on experience on GCP.
Mircea
Course - Google Cloud Platform Basics and Management
Responses with solutions and practical use.
