Data Analytics Literacy (Beginner Level)

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