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Increase your knowledge in Big Data with these Courses
- Describe the Big Data landscape including examples of real-world big data problems including the three key sources of Big Data: people, organizations, and sensors.
- Explain the V's of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis, and reporting.
- Get value out of Big Data by using a 5-step process to structure your analysis.
- Identify what are and what are not big data problems and be able to recast big data problems as data science questions.
- Provide an explanation of the architectural components and programming models used for scalable big data analysis.
- Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model.
- Install and run a program using Hadoop!
- Recognize different data elements in your own work and in everyday life problems
- Explain why your team needs to design a Big Data Infrastructure Plan and Information System Design
- Identify the frequent data operations required for various types of data
- Select a data model to suit the characteristics of your data
- Apply techniques to handle streaming data
- Differentiate between a traditional Database Management System and a Big Data Management System
- Appreciate why there are so many data management systems
- Design a big data information system for an online game company
- At the end of the course, you will be able to:
- Retrieve data from example database and big data management systems
- Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications
- Identify when a big data problem needs data integration
- Execute simple big data integration and processing on Hadoop and Spark platforms
- Design an approach to leverage data using the steps in the machine learning process.
- Apply machine learning techniques to explore and prepare data for modeling.
- Identify the type of machine learning problem in order to apply the appropriate set of techniques.
- Construct models that learn from data using widely available open-source tools.
- Analyze big data problems using scalable machine learning algorithms on Spark.