|By Bob Gourley||
|December 7, 2012 02:34 PM EST||
Cloudera University is offering a new training course on data science titled Introduction to Data Science – Building Recommender Systems. The course is coming to the Washington DC area 20-22 Feb 2012.
If history is our guide, this course will be booked fast. My recommendation: look over the course outline below and register right away. CTOvision readers can attend the course with a 10% discount, so be sure to use the code we provide below to book at the reduced price.
The code is: ClouderaFE_10
Here is more on the course plus the registration link:
Introduction to Data Science – Building Recommender Systems
This hands-on course is suitable for software engineers, data analysts and statisticians. It is problem-driven and focuses on helping participants understand what a data scientist does, the problems they typically solve and their approach to doing so. By taking a practical approach to the subject, including multiple hands-on exercises, participants will leave the course with skills they can immediately apply to real-world problems.
You Will Learn
- Describe the role and responsibilities of a data scientist
- Explain several ways in which data scientists create value for organizations across many industries
- Locate and acquire data from diverse sources
- Use transformation and normalization techniques to produce accurate, useful data sets
- Determine the most appropriate type of analysis to perform for a given problem
- Be able to implement an automated recommendation system
- Develop, evaluate and refine scoring systems for recommenders
- Understand the considerations involved in working at scale
- Identify meaningful, actionable and business-oriented results from the analysis
This course is suitable for software engineers, data analysts and statisticians. A basic knowledge of Hadoop is assumed: use of the HDFS file system, awareness of the MapReduce framework, Hadoop Streaming and Hive. Students should have proficiency in a scripting language; Python is strongly preferred, although students familiar with another language such as Perl or Ruby should be able to complete the exercises.
- Data Science Overview
- Use Cases
- Project Lifecycle
- Data Acquisition
- Evaluating Input Data
- Data Transformation
- Data Analysis and Statistical Methods
- Fundamentals of Machine Learning
- Recommender Overview
- Introduction to Apache Mahout
- Implementing Recommenders with Apache Mahout
- Experimentation and Evaluation
- Production Deployment and Beyond
- Appendix A : Hadoop Overview
- Appendix B: Mathematical Formulas
- Appendix C : Language and Tool Reference
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