To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends.
In this course, you gain the skills that data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data.
This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience.
The self-study e-learning includes:
- Annotatable course notes in PDF format.
- Virtual lab time to practice.
Learn How To
Express a business problem as a manageable analytical question. Identify the appropriate analysis to address the question. Visualize and explore data. Select statistical analyses that help answer the question. Translate complex statistical results into actionable business decisions.
Who Should Attend
Statisticians, market researchers, information technology professionals, data scientists, and business analysts who want to make better use of their data
Prerequisites
Before attending this course, you should have taken a college-level course in statistics, covering distribution analysis, hypothesis testing, and regression techniques or have equivalent knowledge. You can gain this knowledge from the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.
SAS Products Covered
SAS Enterprise Miner;SAS Text Miner;SAS Model Manager;SAS Visual Analytics;SAS Visual Statistics
Course Outline
OverviewDefining terms. Concepts of data science. Exploring and visualizing data.SegmentationPitfalls and good practices. Interpreting clusters.Predictive ModelingFundamentals. Decision trees. Random forests. Model comparison. Mass-scale predictive modeling. Model deployment and management.Design of ExperimentsWhy experiment? Types of business experiments. Incremental response modeling.Unstructured DataText analytics. Social networks.Communicating Your ResultsKnowing your audience. Creating effective messages. Demonstrating value.
