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In the current market scenario, the demand for Data Scientist is increasing day by day. As a Data Scientist, you are required to analyze the data and predict the future trends. The only thing that you need to do is to find out the right data and use the right tools. There are various tools that are available for you to perform data analysis and you can choose the one that suits your needs. Some of the tools that are used in data analysis are R, Python, Tableau, SAS, etc. Data Scientists are required to work on many different platforms and tools and they should be able to switch between them easily.
| Section | Objectives |
|---|---|
| Topic 1: Performance and Optimization Basics | - Optimization concepts
|
| Topic 2: Data Ingestion and Storage Formats | - Reading and writing data
|
| Topic 3: Spark SQL | - SQL queries in Spark
|
| Topic 4: Spark DataFrame API | - DataFrame operations
|
| Topic 5: Transformations and Actions | - Core RDD/DataFrame operations
|
| Topic 6: Apache Spark Architecture and Fundamentals | - Spark architecture overview
|
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