Machine Learning For Data Analyst
SAS Machine Learning for Data Analysts brings in the capability to support the business and management with clear and insightful analysis on the data at hand. This includes data mining skills, advanced modelling techniques, testing and creating and explaining results in clear and concise reports.
SAS Programming 1: Essentials
SAS Programming 2: Data Manipulation Techniques
- Navigate the SAS windowing environment
- Navigate the SAS Enterprise Guide programming environment
- Read various types of data into SAS data sets
- Create SAS variables and subset data
- Combine SAS data sets
- Create and enhance listing and summary reports
- Validate SAS data sets.
SAS Macro Language 1: Essentials
- Control SAS data set input and output
- Combine SAS data sets
- Summarize, read, and write different types of data
- Perform DO loop and SAS array processing
- Transform character, numeric, and date variables.
SAS SQL 1: Essentials
- Perform text substitution in SAS code
- Automate and customize the production of SAS code
- Conditionally or iteratively construct SAS code
- Use macro variables and macro functions.
SAS Programming 3: Advanced Techniques and Efficiencies
- Query and subset data
- Summarize and present data
- Combine tables, including complex joins and merges
- Create and modify table views and indexes
- Replace multiple DATA and PROC steps with one SQL query.
SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression
- Benchmark computer resource usage
- Control memory, I/O, and CPU resources
- Create and use indexes
- Combine data horizontally
- Use hash and hiter DATA step component objects and arrays as lookup tables
- Compress SAS data sets
- Sample your SAS data sets
- Create and use SAS data views
- Safely reduce the length of numeric variables
- Create user-defined functions and informats.
Applied Analytics Using SAS Enterprise Miner
- Generate descriptive statistics and explore data with graphs
- Perform analysis of variance
- Perform linear regression and assess the assumptions
- Use diagnostic statistics to identify potential outliers in multiple regression
- Use chi-square statistics to detect associations among categorical variables
- Fit a multiple logistic regression model.
- Define a SAS Enterprise Miner project and explore data graphically
- Modify data for better analysis results
- Build and understand predictive models such as decision trees and regression models
- Compare and explain complex models
- Generate and use score code
The fees is inclusive of:
- Training & Digital Badge.
- Course material.
- 2 attempts of Global certification.
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