Data Processing, Exploratory Analysis and Visualization

Coursera MOOC / Non-credit USD 49
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Data Processing, Exploratory Analysis and Visualization

About this course

This course introduces distributed computing frameworks and big data visualization techniques. Learners will explore MapReduce, work with Apache Spark, implement transformations with PySpark, and use Spark SQL for large-scale analysis. The course concludes with building compelling dashboards and reports using Power BI for actionable business insights. By the end of this course, you will be able to: - Explain distributed computing and MapReduce concepts - Process large datasets using Apache Spark and PySpark - Apply Spark SQL for advanced queries and transformations - Create dashboards and visualizations using Power BI Tools & Software: Apache Spark, PySpark, Azure Databricks, Power BI Skills: Distributed computing, Data analysis, PySpark, Spark SQL, Data visualization

What you'll learn

  • Process large datasets using Apache Spark and PySpark
  • Write advanced queries and transformations with Spark SQL
  • Apply MapReduce and distributed computing concepts to data processing tasks
  • Build dashboards and visualizations using Power BI
  • Work with Azure Databricks for cloud-based data analysis

Course objectives

  • Explain distributed computing and MapReduce concepts
  • Implement transformations with PySpark for large-scale data processing
  • Apply Spark SQL for advanced queries and transformations
  • Create dashboards and reports using Power BI for actionable business insights

Skills you'll gain

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