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.

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.

Define data integrity based on integrity types and data integrity risk. Apply basic SQL functions to clean up string variables in a database. Developing basic SQL queries for use on databases. Describe the process involved in verifying data cleansing results.

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

Describe using data visualizations to talk about data and the results of data analysis. Identify Tableau as a data visualization tool and understand its uses. Explain which data-driven stories include reference to their importance and attributes. Explain the principles and practices associated with effective presentations.

Describe the R programming language and its programming environment. Demonstrate understanding of basic R Markdown formatting to create structure and emphasize content. Describe options for generating visualizations in R.  Explain the fundamental concepts associated with programming in R.