Data Analytics and Methodologies
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Data analytics is transforming business processes at organizations large and small. As siloed data becomes more available through the deployments of environments such as data lakes and company-wide data warehousing, opportunities arise to apply analytics to improve efficiency, identify opportunity, and use predictions to take pre-emptive action. This course will introduce participants to the fundamentals of data analytics, big data frameworks, open source analytics tools, design methodologies, and visualization libraries through hands-on case studies.
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
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.