This course explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The course content does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions, as well as a high-level understanding of the back-end components that enable these functions.
This qualification is of interest to professionals who work with Big Data. This includes project manager, data analyst, data scientist, business analyst, software engineers, application developers and IT architects. Big Data Foundation teaches them the analytical concepts they need to gain better insight into the data at their fingertips.
At the end of the course, you will have demonstrated an understanding of:
- The Big Data Analysis Lifecycle (from dataset identification to integration, analysis
- Common Analysis and Analytics Techniques
- A/B testing, Regression, Correlation, Text Analytics
- Sentiment Analysis, Time Series Analysis
- Network Analysis, Spatial Analysis
- Automated Recommendation, Classification, Clustering
- Machine Language, Natural Language, Semantics
- Data Visualization and Visual Analysis
- Assessing Hierarchies, Part-to-Whole Relationships
- Plotting Connections and Relationships, Mapping Geo-Spatial Data
- Foundational Big Data Technology Mechanisms
- Big Data Storage (Query Workload, Sharding, Replication, CAP, ACID, BASE)
- Big Data Processing (Parallel Data Processing, Distributed Data Processing,
SharedEverything/Nothing Architecture, SCV)
- Big Data & Cloud Computing
- BDSCP – Big Data Analysis & Technology Lab
- Exam Format: closed-book format. Computer-Based. Participants are not allowed to bring study materials.
- Passing Score: 70%
- Exam Duration: 60 minutes
- Exam Venue: Pearson VUE Testing Centre
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