Coursera MOOC / Non-credit 0

Data Processing, Exploratory Analysis and Visualization

Name: Data Processing, Exploratory Analysis and Visualization
Price: 49 USD
Availability: InStock

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

75/100

CourseAsk score

What the provider tells you: 39/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

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

Data Analysis #business intelligence #apache spark #pyspark #spark sql #power bi #azure databricks #distributed computing #mapreduce #big data #data visualization #data processing #dashboard design #cloud data analysis #data transformations

$49.00

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