This section describes the process of creating the kdb+/Q application, beginning with the process of loading data from the database and creating the scripts that will serve as the backend for the application.
- Preface
- Too Big or Not Too Big
- Big Data Mining for the Masses
- The Analytics Toolkit
- Big Data With Hadoop
- Big Data Mining with NoSQL
- Spark for Big Data Analytics
- An Introduction to Machine Learning Concepts
- What is machine learning?
- Factors that led to the success of machine learning
- Machine learning, statistics, and AI
- Categories of machine learning
- Subdividing supervised machine learning
- Common terminologies in machine learning
- The core concepts in machine learning
- Leveraging multicore processing in the model
- Summary
- Machine Learning Deep Dive
- Enterprise Data Science
- Enterprise data science overview
- A roadmap to enterprise analytics success
- Data science solutions in the enterprise
- Enterprise data science – machine learning and AI
- Enterprise infrastructure solutions
- Tutorial – using RStudio in the cloud
- Summary
- Closing Thoughts on Big Data
- External Data Science Resources
- Other Books You May Enjoy
Creating the Q application
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