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.
Course Outline:
SAS Programming 1: Essentials
- 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 Programming 2: Data Manipulation Techniques
- 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 Macro Language 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 SQL 1: Essentials
- 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.
Who should attend:
- Aspirants who want to learn machine learning techniques and build a strong SAS programming foundation to manipulate data, perform queries and analysis, and generate analytical insights.
- Additionally, wants to access the power of SAS for analysis for complex business problems through data mining and predictive modelling using the point-and-click interface.
Case Studies in the Curriculum
- Learn the application of machine learning through a comprehensive set of case studies designed by international industry experts in fields such as BFSI, Retail, E-Commerce, Cybercrime and much more
SAS Programming 3: Advanced Techniques and Efficiencies
- 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.
SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression
- 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.
Applied Analytics Using SAS Enterprise Miner
- 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
Apply association and sequence discovery to transaction data.
Prerequisites:
- Flair of any programming language is preferred however not necessary.
- Candidates with Engineering/MBA/MCA /Science/ Statistics degree or Analytics experience preferred.
The fee is inclusive of
- Fee: ₹1,36,000+GST | Duration: 144 Hours
- Training & Digital Badge
- Course material
- 2 attempts of Global certification
- Duration: 144 Hours of Training
The SAS Global Certification Program allows you to validate your SAS software skills and earn the globally-recognised credential endorsed by SAS.
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