Data Analytics and Methodologies
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.