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Go Big with these Big Data Courses
- Knowledge and application of MapReduce
- Understanding the rate of occurrences of events in big data
- How to design algorithms for stream processing and counting of frequent elements in Big Data
- Understand and design PageRank algorithms
- Understand underlying random walk algorithms
- How to develop algorithms for the statistical analysis of big data;
- Knowledge of big data applications;
- How to use fundamental principles used in predictive analytics;
- Evaluate and apply appropriate principles, techniques, and theories to large-scale data science problems.
- Appreciate the software needs of an IoT project
- Understand how data is managed in an IoT network
- Apply software solutions for different systems and Big Data to your IoT concept designs
- Create Python scripts to manage large data files collected from sensor data and interact with the real world via actuators and other output devices.
- How to design algorithms
- Understand fundamental programming concepts including data abstraction, storage, and structures
- Understand computational thinking which includes decomposition, pattern recognition, and abstraction
- Data-driven problem and algorithm design for big data
- Interpretation of data representation and analysis
- Understand key mathematical concepts, including dimension reduction and Bayesian models
- How to use analytical tools such as R and Java
- Understand health data and big data analytic technology;
- Health data standards;
- Scalable machine learning algorithms such as online learning and fast similarity search;
- Big data analytics systems such as Hadoop family (Hive, Pig, HBase), Spark and Graph DB;
- Deep learning models and packages such as TensorFlow.