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Updated in [February 21st, 2023]

Big Data Use Cases | E-Commerce Data Analysis Using Hadoop | Big Data Case Study Part 4.
Big Data Use Cases | Table Creation & Data Creation On E-commerce | Big Data Case Study Part 5.
Big Data Use Cases | E-Commerce Rules Checking & Validation | Big Data Case Study Part 6.
Big Data Use Cases | E-Commerce Data Analysis | Big Data Case Study Part 7.

(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
This course provides learners with the knowledge and skills to analyze e-commerce data using Hadoop. Learners will learn how to create tables and data, check and validate rules, and analyze e-commerce data.

Possible Development Paths include data engineering, data science, and software engineering. Learners can also pursue further studies in big data, machine learning, and artificial intelligence.

Learning Suggestions for learners include understanding the fundamentals of Hadoop, learning the basics of data analysis, and exploring the different types of data analysis techniques. Learners should also become familiar with the different types of e-commerce data and the tools used to analyze them. Additionally, learners should practice their skills by working on real-world projects.

[Applications]
After completing this course, participants can apply the knowledge gained to analyze e-commerce data using Hadoop. They can create tables and data on e-commerce, check and validate e-commerce rules, and analyze e-commerce data. Additionally, they can use the case studies presented in the course to gain a better understanding of how to apply the concepts to real-world scenarios.

[Career Paths]
1. Big Data Analyst: Big Data Analysts are responsible for analyzing large datasets to identify trends and patterns. They use a variety of tools and techniques, such as Hadoop, to process and analyze data. The demand for Big Data Analysts is growing rapidly as businesses increasingly rely on data-driven decisions.

2. Data Scientist: Data Scientists are responsible for extracting insights from large datasets. They use a variety of techniques, such as machine learning and natural language processing, to uncover hidden patterns and trends. Data Scientists are in high demand as businesses look to leverage data to gain a competitive edge.

3. Business Intelligence Developer: Business Intelligence Developers are responsible for developing and maintaining data warehouses and data marts. They use a variety of tools, such as Hadoop, to store and process large datasets. Business Intelligence Developers are in high demand as businesses look to leverage data to make better decisions.

4. Data Engineer: Data Engineers are responsible for designing and building data pipelines. They use a variety of tools, such as Hadoop, to ingest, store, and process large datasets. Data Engineers are in high demand as businesses look to leverage data to gain a competitive edge.