The SAS Certified Advanced Analytics Professional program represents Level 2 of the SAS Academy for Data Science. Students learn a variety of machine learning and predictive modeling techniques, and how to apply the techniques to big distributed and in-memory data sets. Students also learn about experimentation in business, optimization, and time series forecasting, as well as the essential communication skills necessary to deliver value to the organization.

The course work, hands-on practice, and projects help prepare students for the three exams needed to become a SAS Certified Advanced Analytics Professional. Exams are included.

Naucz się

• Apply machine learning and predictive modeling techniques to big distributed and in-memory data sets.
• Detect patterns in your data.
• Set up and run business experiments.
• Optimize your data.
• Use time-series forecasting techniques.
• Effectively communicate results and recommendations to executive-level management.

Kto powinien uczestniczyć

Individuals with a least six months of programming experience in SAS or another programming language, and at least six months of experience using mathematics, statistics, or both in a business environment. A master's degree or higher in a quantitative or technical field is recommended but not required.

Dostępne formaty	Długość
e-szkolenie:	210 godziny/1 licencja roczna

To enroll in the advanced analytics certification program, you need at least six months of programming experience in SAS or another programming language, and at least six months of experience using mathematics, statistics, or both in a business environment. We also recommend that you first complete the Statystyka 1: wprowadzenie do analizy wariancji, regresji i regresji logistycznej course, which is available as an instructor-led or free online e-learning course.

The curriculum includes the following courses:

• Analityka praktyczna w SAS Enterprise Miner
• Modelowanie sieci neuronowych
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• Modelowanie predykcyjne z użyciem regresji logistycznej
• Data Mining Techniques: Predictive Analytics on Big Data
• Using SAS to Put Open Source Models into Production
• Analiza tekstu w SAS Text Miner
• Podstawy modelowania szeregów czasowych
• Experimentation in Data Science
• Optimization Concepts for Data Science