Course Outline
Foundations: Data, Data, Everywhere
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Define and explain key concepts in data analytics, including data, data analysis, and the data ecosystem.
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Conduct a self-assessment of your analytical thinking, providing specific examples of its application.
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Discuss the role of spreadsheets, query languages, and data visualisation tools in data analytics.
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Describe the role of a data analyst with specific reference to relevant jobs and positions.
Ask Questions to Make Data-Driven Decisions
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Explain how each step of the problem-solving roadmap 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 data analyst tasks, including entering and organising 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, referring to their functions and components.
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Describe best practices for organising data.
Process Data from Dirty to Clean
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Define data integrity, with reference to types of integrity and risks to data integrity.
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Apply basic SQL functions for 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 data cleaning.
Analyse Data to Answer Questions
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Discuss the importance of organising your data before analysis, with references to sorting and filtering.
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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 Visualisation
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Describe the use of data visualisations to communicate data and the results of data analysis.
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Identify Tableau as a data visualisation tool and understand its uses.
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Explain what data-driven stories are, including references to their importance and attributes.
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Explain the 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 visualisations in R.
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Demonstrate an understanding of the basic formatting of R Markdown to create structure and emphasise content.
Google Data Analytics Capstone: Complete a Case Study
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Differentiate between a capstone, 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 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.