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Updated in [June 30th, 2023]

This course, Big Data Analysis with Apache Spark, provides an overview of the skills required to become a data scientist. It covers the use of PySpark (part of Spark) to manipulate data sets using parallel processing. Students will learn how to use PySpark to perform log mining, textual entity recognition, and collaborative filtering exercises. This course requires a programming background and experience with Python, as well as previous experience with Spark equivalent to Introduction to Apache Spark.

[Applications]
The application of this course is to equip students with the skills to use PySpark to manipulate data sets using parallel processing. Students will be able to use the knowledge gained from this course to develop data-intensive products and services, such as recommendation, prediction, and diagnostic systems. Additionally, students will be able to use the skills learned to support and influence decisions in organizations.

[Career Paths]
The career path recommended to learners of this course is Big Data Analysis with Apache Spark. This job position involves using Apache Spark to analyze large datasets and extract meaningful insights from them. It requires a strong understanding of data science principles and the ability to use PySpark to manipulate data sets. The job also requires knowledge of parallel processing and the ability to use it to optimize data analysis.

The development trend for this job position is increasing demand. As organizations become more data-driven, the need for data analysts with expertise in Apache Spark is growing. Companies are looking for professionals who can use Apache Spark to analyze large datasets and extract meaningful insights from them. Additionally, the increasing availability of cloud computing services has made it easier for organizations to access and analyze large datasets, further increasing the demand for data analysts with expertise in Apache Spark.

[Education Paths]

The recommended educational path for learners is to pursue a Bachelor's degree in Data Science or a related field such as Computer Science, Statistics, or Mathematics. This degree will provide the foundational knowledge and skills necessary to understand and analyze data. It will also provide the opportunity to learn more advanced topics such as machine learning, artificial intelligence, and natural language processing. Additionally, the degree will provide the opportunity to gain experience with various data analysis tools and techniques, such as Apache Spark, Hadoop, and Tableau.

The development trend for data science degrees is to focus on the application of data science to real-world problems. This includes courses in data visualization, data mining, and predictive analytics. Additionally, courses in ethical considerations and data privacy are becoming increasingly important. As data science becomes more prevalent, the need for data scientists with a strong understanding of the ethical implications of data analysis will become more important.