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Updated in [June 30th, 2023]

Introduction to Big Data is a course designed for those new to data science and interested in understanding the Big Data landscape. This course will provide an overview of the terminology and core concepts behind big data problems, applications, and systems. It will also introduce the Hadoop framework, which has made big data analysis easier and more accessible. At the end of this course, students will be able to describe the Big Data landscape, explain the V’s of Big Data, get value out of Big Data, identify big data problems, explain the architectural components and programming models used for scalable big data analysis, and summarize the features and value of core Hadoop stack components. No prior programming experience is necessary, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware and software requirements include a quad core processor, 8 GB RAM, 20 GB disk free, Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+, and VirtualBox 5+.

[Applications]
Upon completion of this course, participants will have a better understanding of the Big Data landscape and the core concepts behind big data problems, applications, and systems. They will be able to identify what are and what are not big data problems and be able to recast big data problems as data science questions. Participants will also be able to explain the V’s of Big Data and get value out of Big Data by using a 5-step process to structure their analysis. Additionally, they will be able to install and run a program using Hadoop.

[Career Paths]
One job position path recommended to learners of this course is Big Data Engineer. Big Data Engineers are responsible for designing, developing, and maintaining the infrastructure and systems that enable the collection, storage, and analysis of large datasets. They must have a deep understanding of the various technologies and tools used in Big Data, such as Apache Hadoop, Apache Spark, and Apache Kafka. They must also be able to develop and maintain data pipelines, create data models, and develop algorithms to process and analyze data.

The development trend of Big Data Engineering is towards automation and scalability. Big Data Engineers must be able to design and develop systems that can scale to handle large datasets and automate the data processing and analysis process. They must also be able to develop systems that can integrate with existing systems and be able to quickly adapt to changing requirements. Additionally, Big Data Engineers must be able to develop systems that are secure and reliable, and that can be easily maintained and monitored.

[Education Paths]
The recommended educational path for learners interested in Big Data is to pursue a Bachelor's degree in Computer Science or a related field. This degree will provide learners with the foundational knowledge and skills needed to understand and work with Big Data. The curriculum typically includes courses in programming, data structures, algorithms, databases, operating systems, computer networks, and software engineering. Additionally, learners may take courses in artificial intelligence, machine learning, and data mining.

The development trend for Big Data is to focus on the use of advanced analytics and machine learning techniques to extract insights from large datasets. This includes the use of predictive analytics, natural language processing, and deep learning. Additionally, the use of cloud computing and distributed computing technologies is becoming increasingly important for Big Data applications. As the demand for Big Data professionals continues to grow, the need for specialized skills and knowledge in these areas will become increasingly important.