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Who should attend
This course is designed for IT, BI, business, and technical professionals looking to expand their knowledge of data analytics techniques and tools.
About the course
Data analytics is transforming business processes at both large and small organizations. As siloed data becomes more available through the deployments of environments, such as data lakes and company-wide data warehousing, opportunities are arising to apply analytics to improve efficiency, identify opportunity, and use predictions to take preemptive action. This course will introduce you to the fundamentals of data analytics, big data frameworks, open source analytics tools, design methodologies, and visualization libraries through hands-on case studies. You will come away from this class with a greater understanding of the universe of data analytics ranging from statistical analysis, machine learning, and data visualization solutions and the knowledge of how they can be interconnected to solve your organization’s upcoming challenges.
What You Will Learn
- Data from traditional databases, document stores, Hadoop, and open APIs, including open source data from social media
- Analytics application requirements for big data frameworks and tools
- Machine learning basics including traditional techniques and the basics of deep learning.
- Graph analysis for linked data
- Basics of natural language processing for unstructured data
How You Will Benefit
- Gain knowledge across a broad area of subject matter, including expert insights into the latest software tools available on the market.
- Understand the universe of data analytics solutions.
- Discover how data analytics solutions can be interconnected to solve your organization’s upcoming challenges.
SOURCES OF DATA
- Traditional databases
- NoSQL data stores
- Graph data stores
- Probability and distributions
- Hands-on exercise
- Principles of good visualization
- Hands-on tableau exercise
- Supervised versus unsupervised learning
- Basic ML techniques
- Deep learning overview
- Hands-on exercises
NATURAL LANGUAGE PROCESSING
- Tokenization, stemming, named entity recognition
- Context and grammar parsing
- Deep learning based techniques
Jason Poovey has an MS in Computer Engineering from North Carolina State University and is the Branch Head of the HPC, Data Analytics, and Software Engineering Branch at the Georgia Tech Research Institute. He has taught at North Carolina State University, Emory University, and Georgia Tech. At ...
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