Introduction to Data Science: Building Recommender Systems

3 Days Instructor-led

Data scientists build information platforms to ask and answer previously unimaginable questions. Learn how data science helps companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities. Cloudera University’s three-day course helps participants understand what data scientists do and the problems they solve. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field.

Skills Gained

• Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:
  • The role of data scientists, vertical use cases, and business applications of data science
  • Where and how to acquire data, methods for evaluating source data, and data transformation and preparation
  • Types of statistics and analytical methods and their relationship
  • Machine learning fundamentals and breakthroughs, the importance of algorithms, and data as a platform
  • How to implement and manage recommenders using Apache Mahout and how to set up and evaluate data experiments
  • Steps for deploying new analytics projects to production and tips for working at scale

Who Can Benefit

• This course is suitable for developers, data analysts, and statisticians with basic knowledge of Apache Hadoop: HDFS, MapReduce, Hadoop Streaming, and Apache Hive. Students should have proficiency in a scripting language; Python is strongly preferred, but familiarity with Perl or Ruby is sufficient.

Certification Exam

• Data Science Essentials plus the Data Science Challenge constitute the Cloudera Certified Professional: Data Scientist (CCP:DS). Certification is a great differentiator; it helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.

Please contact us for a detailed course outline.