Introduction to Data Science

The goal of this course is to make analytics approachable and comprehensible. After completing this course, you will be able to perform the main data-related tasks for the citizen data scientist using the point-and-click capabilities of SAS Visual Analytics and SAS Visual Statistics: accessing and manipulating data, exploring data using analytics, and building predictive models.

Learn how to

• Recognize descriptive, predictive, and prescriptive analytics.
• Distinguish between various analytical methods and their applications.
• Formulate data need and data structure for certain analytical methods.
• Build and compare data mining models.
• Interpret the results of a statistical model.
• Communicate about analytical results.

Who should attend

Business analysts and data analysts who want to know more about the concepts of analytics and data science

Formats available	Standard Duration (duration can vary, see event schedule for details)
Live Web Classroom:	10 half-day session(s)	System Requirements

Attendees should have a solid business domain knowledge, be comfortable working with data, and have a willingness to learn new methods for analyzing data. No programming knowledge is required. Basic statistical and mathematical knowledge is helpful.

This course addresses SAS Visual Analytics, SAS Visual Statistics, SAS Viya software.

Big Data, Analytics, and Data Science

• Introduction.
• The (citizen) data scientist.
• Skills for the data scientist.

The Analytics Life Cycle

• The analytics life cycle.
• Analytical methods and applications.
• Operationalization of analytics.

Introduction to the SAS Platform

• Introduction to the SAS Platform.
• SAS Visual Analytics: Exploring SAS Drive.
• SAS Visual Statistics.

Introduction to Visualizing Data

• Viewing Visual Analytics reports.

Introduction to Analyzing Data

• Working with data items.
• Exploring data with charts and graphs.
• Creating data items and applying filters.
• Performing data analysis.

Using Automated Explanation when Analyzing Data

• Automated explanation.

Introduction to Geographic Analysis

• Introduction to SAS Data Studio.
• Restructuring data.
• Analyzing geographic information.

Introduction to Forecasting

• Restructuring data.
• Forecasting.

Using Parameters to Create Advanced Reports

• Numeric parameters.
• Character parameters.
• Date parameters.

Case Study: Dutch Traffic Accidents

• Traffic accidents case study.
• Data dictionary.
• Case study scenarios.

Introduction to Basic Statistics

• Introduction to statistics.
• Distributions.
• Hypothesis testing.

Introduction to Regression

• Exploratory data analysis, correlations, and scatter plots.
• Simple linear regression.
• Regression diagnostics.

Basic Statistics for Big Data Analysis

• Data description.
• Introduction to the SAS Visual Statistics environment.
• Big data Analytics in SAS Viya.

Introduction to Cluster Analysis

• Segmentation concepts.
• Cluster analysis.

Introduction to Linear Regression

• Linear regression models.
• Model validation.

Introduction to Logistic Regression

• Logistic regression.
• Logistic regression and a nonparametric logistic regression in SAS Visual Statistics.
• Modeling with group-by variables.

Introduction to Decision Trees

• Overview of decision trees.
• Decision trees in SAS Visual Statistics.

Introduction to Comparing and Scoring Models

• Comparing models in SAS Visual Statistics.
• Score code in SAS Visual Statistics.

Case Study: Big Organics

• Short description of the case study.
• Exploration.
• Transform and modify.
• Clustering.
• Decision trees.
• Logistic regression.
• Forests.
• Neural networks.
• Model comparison.
• Export the champion model.

Appendix A: Data Science Reading Suggestions

Appendix B: Data Science Learning Suggestions

• SAS programming.
• SAS Machine Learning.
• SAS Academy for Data Science.