Data for Leaders Proficiency (Advanced Level)

  • 25m
  • 25 questions
The Data for Leaders Proficiency benchmark will measure whether a learner has had significant exposure and experience with data technologies. You will be evaluated on your ability to recognize the concepts of data such as big data analytics, data architecture, data processing, data governance and management, and emerging new age architecture. A learner who scores high on this benchmark demonstrates independent knowledge across a variety of data technologies and platforms.

Topics covered

  • compare the concepts of data management, data governance, and data compliance
  • compare the functionality and use cases of Hadoop and cloud computing platforms
  • define the concept of Lambda architecture and outline its use cases
  • define the role of model validation when using machine learning for predictive modelling
  • describe how to perform anomaly detection during data mining
  • describe in-memory storage systems and their use cases and advantages using examples
  • describe the components of a data pipeline
  • describe the concept of columnar databases, which store data in a column-wise format
  • describe the purpose of the data validation process and name the major steps involved in it
  • identify key activities used for data stewardship
  • list activities involved in data quality management
  • list and compare notable data lake platforms
  • list and describe different cloud storage platforms available for businesses
  • list state-of-the-art approaches to data management
  • name multiple ways in which predictive modeling should be interpreted through a business context
  • name simple best practices when using Spark, like starting small or resolving skewness
  • name the most important performance optimization techniques in Apache Spark, such as file format selection, level of parallelism, and API selection
  • outline the features and functions of hybrid modern data warehouses
  • outline the main pillars and components of big data architecture
  • specify how to establish the business perspective of a data science project and how business goals relate to data analysis and predictive modeling
  • specify the importance of utilizing Predictive Analytics for Business
  • specify the risks in utilizing large databases and list the approaches to maintaining data security
  • specify the role of deep learning and artificial neural networks when dealing with data
  • specify the use cases, benefits, and challenges of popular key-value data stores
  • specify why unstructured data comes from variable sources and describe how it moves from its origin to storage and gets further analyzed and visualized