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Updated in [February 21st, 2023]

This course is about Big Data Use Cases, specifically Bank Data Analysis Using Hadoop. It is divided into three parts: Big Data Case Study Part 1, Big Data Case Study Part 2, and Big Data Case Study Part

By taking this course, users will gain an understanding of how to use Big Data to analyze banking data, including data integration and table creation, content analysis and archival.

Possible development paths for learners include becoming a data analyst, data scientist, or software engineer. They could also pursue a career in banking or finance, or use their knowledge to develop their own business.

Learning suggestions for learners include taking courses in data science, machine learning, and artificial intelligence. They should also become familiar with the Hadoop platform and related technologies, such as Apache Spark and Apache Hive. Additionally, they should practice their skills by working on real-world projects.

[Applications]
After completing this course, participants can apply the knowledge gained to analyze banking data using Hadoop. They can use the techniques learned to integrate data, create tables, and perform content analysis and archival. 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. They also develop algorithms and models to help organizations make better decisions. The demand for Big Data Analysts is increasing as organizations are increasingly relying on data-driven decisions.

2. Data Scientist: Data Scientists are responsible for extracting insights from large datasets. They use a variety of tools and techniques, such as Hadoop, to process and analyze data. They also develop algorithms and models to help organizations make better decisions. The demand for Data Scientists is increasing as organizations are increasingly relying on data-driven decisions.

3. Business Intelligence Developer: Business Intelligence Developers are responsible for developing and maintaining data warehouses and data marts. They use a variety of tools and techniques, such as Hadoop, to process and analyze data. They also develop algorithms and models to help organizations make better decisions. The demand for Business Intelligence Developers is increasing as organizations are increasingly relying on data-driven decisions.

4. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines. They use a variety of tools and techniques, such as Hadoop, to process and analyze data. They also develop algorithms and models to help organizations make better decisions. The demand for Data Engineers is increasing as organizations are increasingly relying on data-driven decisions.