Data Literacy (Beginner Level)

  • 37m 30s
  • 25 questions
The data literacy benchmark will measure your ability to speak the language of data. You will be evaluated on your ability to recognize key topics such as; data science concepts, analytics, database types, predictive analytics, data visualization, data stewardship, data compliance, and data governance. A learner who scores high on this benchmark demonstrates that you have the skills to interpret data and incorporate it into your daily life.

Topics covered

  • compare the concepts of data management, data governance, and data compliance
  • compare the roles of data science and data analysis in a business context
  • define concepts essential to Data Science like Dataset, Database, Data Analytics, Data Aggregation, Time Series
  • define data lakes and describe their evolution from Hadoop
  • describe how predictive analytics can be used to drive business decision-making
  • describe how the NoSQL approach facilitates the horizontal distribution of large, structured, and unstructured data and specify when to use NoSQL and SQL databases
  • describe the concept of big data and the history behind it
  • describe the process of deciphering correlations, market trends, patterns, and customer behavior using big data
  • describe use cases of graph databases and specify why the relationship between data is as important as the data itself in such a database
  • distinguish between raw data, information, applicable knowledge, and general wisdom
  • identify key activities used for Data Stewardship
  • identify major issues in achieving Data Governance and Data Compliance
  • identify the most common strategies used for Data Management
  • list and compare different cloud analytics tools
  • list and describe different types of data pipeline tools
  • list the most commonly used Data Sources and formats
  • name and define major types of Machine Learning used in Business Management
  • name and define three main tiers of a Data Warehouse
  • name and describe common database types used in the industry
  • outline approaches to mastering raw data
  • 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 importance of data visualization and reporting and tools commonly used for the same
  • specify the importance of utilizing Predictive Analytics for Business
  • specify the risks in utilizing large databases and list the approaches to maintain Data Security
  • specify the role of Deep Learning and Artificial Neural Networks when dealing with data