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Course Outline

Foundations: Data, Data, Everywhere

  • Define and explain key concepts in data analytics, including data, data analysis, and the data ecosystem.

  • Conduct a self-assessment of your analytical thinking, providing specific examples of its application.

  • Discuss the role of spreadsheets, query languages, and data visualisation tools in data analytics.

  • Describe the role of a data analyst with specific reference to relevant jobs and positions.

Ask Questions to Make Data-Driven Decisions

  • Explain how each step of the problem-solving roadmap contributes to common analysis scenarios.

  • Discuss the use of data in the decision-making process.

  • Demonstrate the use of spreadsheets to complete basic data analyst tasks, including entering and organising 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, referring to their functions and components.

  • Describe best practices for organising data.

Process Data from Dirty to Clean

  • Define data integrity, with reference to types of integrity and risks to data integrity.

  • Apply basic SQL functions for cleaning string variables in a database.

  • Develop basic SQL queries for use on databases.

  • Describe the process involved in verifying the results of data cleaning.

Analyse Data to Answer Questions

  • Discuss the importance of organising your data before analysis, with references to sorting and filtering.

  • 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 Visualisation

  • Describe the use of data visualisations to communicate data and the results of data analysis.

  • Identify Tableau as a data visualisation tool and understand its uses.

  • Explain what data-driven stories are, including references to their importance and attributes.

  • Explain the 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 visualisations in R.

  • Demonstrate an understanding of the basic formatting of R Markdown to create structure and emphasise content.

Google Data Analytics Capstone: Complete a Case Study

  • Differentiate between a capstone, a 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 and portfolios when communicating with recruiters and potential employers.

Requirements

  • No degree or prior experience is required.
 35 Hours

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