SAS Predictive Business Analyst (Combo)

Base Programmer

  • Essentials
    • The SAS programming process
    • Using SAS programming tools
    • Understanding SAS programming syntax
  • Accessing Data
    • Understanding SAS data structures
    • Accessing data through libraries
    • Importing data into SAS
  • Exploring and Validating Data
    • Data Exploration
    • Filtering and formatting data
    • Arranging data
  • Preparing Data
    • Reading and filtering data
    • Computing new columns
    • Conditional processing
  • Analysing and Reporting on Data
    • Enhancing reports with titles, footnotes, and labels
    • Creating frequency reports
    • Creating summary statistics reports
  • Exporting Results
    • Exporting data and reports
  • Using SQL in SAS
    • Using SQL
    • Joining tables using SQL in SAS
Data Manipulation Techniques:
  • Controlling DATA Step Processing
  • Accessing Data
  • Summarizing Data
  • Manipulating Data with Functions
  • Creating Custom Formats
  • Combining Tables
  • Processing loops
  • Restructuring tables
Essentials: Anyone starting to write SAS programs
Data Manipulation Techniques: Business analysts and SAS programmers
Essentails :
  • No prior SAS experience is needed
  • Experience using computer software
  • Understand file structures and system commands on your operating systems
  • Access data files on your operating systems
Data Manipulation Techniques:
  • Ability to use DATA code to subset rows and columns, compute new columns, and process data conditionally
  • Ability to use SORT procedure
  • Knowledge on applying SAS formats
Essentails : Enter the exciting work of data analytics and business intelligence by learning to:
  • Use SAS to write and submit SAS programs
  • Access SAS, Microsoft Excel, and text data
  • Explore and validate data
  • Prepare data by creating subsets of rows and computing new columns
  • Analyze and report on data
  • Export data and results to Excel, PDF, and other formats
  • Use SQL in SAS to query and join tables.
DATA Manipulation Techniques : Enter the exciting work of data analytics and business intelligence by learning to :
  • Create an accumulating column and process data in groups
  • Manipulate data with functions
  • Convert column type to other formats
  • Create custom formats
  • Concatenate, merge and restructure tables
  • Using loops
Essentails :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 days
Level : Fundamental
Languages : English

DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 days
Level : Intermdiate
Languages : English

Predictive Modeling

Predictive Modeling:
  • Introduction to Statistics
    • Fundamental statistical concepts
    • Examining distributions
    • Describing categorical data
    • Constructing confidence intervals
    • Performing simple tests of hypothesis
  • Analysis of Variance (ANOVA)
    • Performing one-way ANOVA
    • Performing multiple comparisons
    • Performing two-way ANOVA with and without interactions
  • Regression
    • Producing correlations
    • Fitting a simple linear regression model
    • Understanding multiple regression
    • Building and interpreting models
    • Understanding regression techniques
    • Exploring stepwise selection techniques
  • Regression Diagnostics
    • Examining residuals
    • Investigating influential observations and collinearity
  • Categorical Data Analysis
    • Describing categorical data
    • Tests for general and linear association
    • Understanding logistic regression and multiple logistic regression
    • Performing backward elimination with logistic regression
Applied Analytics Using SAS Enterprise Miner:
  • Introduction to SAS Enterprise Miner
  • Introduction to Predictive Modeling
    • Predictive Modeling Fundamentals and Decision Trees
    • Cultivating decision trees
    • Optimizing the complexity of decision trees
  • Regression Analysis
    • Working with regression data
    • Optimizing regression complexity
    • Interpreting regression models
    • Transforming inputs
    • Working with categorical data inputs
  • Neural Networks and Other Modeling Tools
    • Introduction to neural network models
    • Input selection
    • Training boundaries
  • Model Assessment
    • Model fitting
    • Statistical graphics
    • Adjusting for separate sampling
    • Working with profit matrices
  • Model Implementation
    • Internally scored data sets
    • Score code modules
  • Introduction to Pattern Discovery - Cluster Analysis
Predictive Modeling: Statisticians and business analysts who want to use a point-and-click interface to SAS
Applied Analytics Using SAS Enterprise Miner: Data analysts, qualitative experts, and business analysts who wish to use SAS Enterprise Miner
Predictive Modeling:
  • Familiarity with both SAS Enterprise Guide and basic statistical concepts
  • Should have completed an undergraduate course in statistics
  • Able to perform analysis and create data sets with SAS Enterprise Guide software
Applied Analytics Using SAS Enterprise Miner:
  • Should be acquainted with Microsoft Windows and Windows software
  • Should have at least an introductory-level familiarity with basic statistics and regression modelling
  • Previous SAS software experience is helpful but not required.
Predictive Modeling:
  • Generate descriptive statistics and explore data with graphs
  • Perform analysis of variance (ANOVA)
  • Perform linear regression
  • Learn how to identify potential outliers in multiple regression
  • Use chi-square statistics to detect associations among categorical variables
  • fit a multiple logistic regression model.
This course is designed for SAS Enterprise Guide users who want to perform statistical analyses. The course is written for SAS Enterprise Guide 7.1 along with SAS 9.4, but students with previous SAS Enterprise Guide versions will also get value from this course. An e-course is also available for SAS Enterprise Guide 5.1 and SAS Enterprise Guide 4.3.
Applied Analytics Using SAS Enterprise Miner: Learn how to
  • Build and understand predictive models such as decision trees and regression models
  • Compare and explain complex models
  • Modify data for better analysis results
  • Apply association and sequence discovery to transaction data
Predictive Modeling:
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 days
Level : Advanced
Languages : English

Applied Analytics Using SAS Enterprise Miner:
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 days
Level : Advanced
Languages : English

Visual Analytics: Fast Track

Visual Analytics: Fast Track
  • Getting Started with SAS Visual Analytics
  • Using the SAS Visual Analytics Explorer
    • Creating visualizations
    • Interacting with visualizations
  • Designing Reports with SAS Visual Analytics
    • Examining the SAS Visual Analytics Designer interface
    • Creating a simple report
    • Working with graphs
    • Providing multi-section reports
    • Working with gauges and tables
    • Working with text and images
  • Viewing SAS Visual Analytics Reports
    • Viewing reports on the Web
    • Viewing reports on a mobile device
Visual Analytics: Fast Track: BI content developers who need to learn how to use the functionality provided by SAS Visual Analytics
Visual Analytics: Fast Track:
  • No SAS experience or programming experience is required
  • Experience in using Microsoft Windows
Visual Analytics: Fast Track Learn how to use SAS Visual Analytics to:
  • Interact with the environment via the SAS Visual Analytics Hub
  • Access and prepare data for exploration, analysis, and reporting
  • Explore data using the SAS Visual Analytics Explorer
  • Create reports with the SAS Visual Analytics Designer
  • View reports using the SAS Visual Analytics Viewer and SAS Mobile BI.
Visual Analytics: Fast Track
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 4 Days
Level : Intermediate
Languages : English
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