SKILL BENCHMARK

Data Analytics Literacy

  • 18m
  • 18 questions
The Data Analytics Literacy benchmark measures whether a learner has exposure to data analytics concepts, including what data analytics is and why it's required, the various data analytics tools and frameworks available, and the different types of data analytics. A learner who scores high on this benchmark demonstrates that they have the foundational knowledge to start working on data analytics projects with training and supervision.

Topics covered

  • compare the roles of data science and data analysis in a business context
  • compare the roles of machine learning engineers and data scientists
  • define and compare data science, data analytics and machine learning and recognize their use cases for business management
  • define key characteristics and requirements for a reliable data collection pipeline
  • define the role of model validation when using machine learning for predictive modelling
  • describe how business intelligence analytics has developed from traditional to modern approaches
  • describe how descriptive analysis can be used to drive business decision-making
  • describe how predictive analytics can be used to drive business decision-making
  • identify how data visualization done correctly can become a key business driver
  • name advanced visualization techniques and describe their use cases
  • name multiple ways in which predictive modeling should be interpreted through a business context
  • name the steps and processes essential to any data science project
  • outline several ways to clean a dataset and describe why data cleaning is necessary
  • outline the evolution of data analytics, the changing perspectives with respect to it, and what's meant by descriptive, diagnostic, predictive, and prescriptive analytics
  • recognize the primary industrial and commercial data sources around us and use this knowledge to select a suitable data source for your business processes
  • specify how summary statistics can be used to explore and prepare a dataset and define what's meant by measures of frequency and central tendency
  • specify how summary statistics can be used to explore and prepare a dataset and describe measures of dispersion and statistics
  • specify the requirements for model implementation when using machine learning for predictive modeling