SKILL BENCHMARK
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
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