Datascience with SAS Training

Datascience with SAS Training

Tech and Telecom companies require huge volumes of unstructured data to be analyzed, and hence data scientists use machine learning techniques for which R and Python are more suitable. SAS is an expensive commercial software and is mostly used by large corporations with huge budgets.“More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean.

Datasscience with SAS Training Syllabus

Overview of SAS

Introduction and History of SAS

Significance of SAS software solutions in various industries

Demonstrate SAS Capabilities

Job Profile / career opportunities with SAS worldwide?

Base SAS Fundamentals

Explore SAS Windowing Environment

SAS Tasks

Working with SAS Syntax

Create and submit a SAS sample program

Data Access & Data Transformation

Accessing SAS Data libraries

Getting familiar with SAS Data set

Reading SAS data set

Introduction to reading data

Examine structure of SAS data set

Understanding of SAS works

Reading Excel worksheets

Using Excel data as input

Create as sample program to import and export excel sheets

Reading Raw data from External File

Introduction to raw data

Reading delimited raw data file (List Input)

Using standard delimited data as input

Using nonstandard delimited data as input

Reading raw data aligned to columns (Fixed or column input)

Reading raw data with special instructions (Formatted input)

Writing to an External file

Write data values from SAS data set to an external file

Data transformations (Data step processing)

Create multiple output datasets from single SAS dataset

Writing observations to one or more SAS datasets

Controlling which observations and variables to be written to output data

Creating subset of observations using

Where condition

Conditional processing using: IF statements

Processing Data Iteratively

Iterative DO loop processing with END statement

DO WHILE & DO UNTIL Statement

SAS Array statement

Summarizing data

Creating and Accumulating total variable (Retain)

Using Assignment statement

Accumulating totals for a group of data (BY group)

Manipulating Data

Sorting SAS data sets

Manipulating SAS data values

Presentation of user defined values /data/currency values using FORMAT procedure

SAS functions to manipulate char and num data

Convert data type form char-to num and num-to-char

SAS variables lists/ SAS variables lists range

Debugging SAS program

Accessing observations by creating index

Restructuring a SAS data set

Rotating with the data step

Using the transpose procedure

Combining SAS data sets

Concatenation

Interleaving

One to one reading

One to one merging (with non-matching)

Match merging (Merging types with IN=option)

SAS Access & SAS Connect

Validating and cleaning data

Detect and correct syntax errors

Examining data errors

Analysis & Presentation

SAS/REPORTS SAS/GRAPH

SAS/STATS SAS/ODS

Producing detailed /Summary Reports

Freq Report

Means Report

Tabulate Report

Proc report

Summary report

Univariate report

Contents report

Print report

Compare proc

Copy proc

Datasets proc

Proc append

Proc delete

Generating Statistical Reports using

Regression proc

Uni/Multivariate proc

Anova proc

Generating Graphical reports using

Producing Bar and Pie charts (GCHART Proc)

Producing plots (GPLOT Proc)

Presenting Output Report result in:

PDF

Text files

Excel

HTML Files

SAS/SQL Programming

Introduction and overview to SQL procedure

Proc SQL and Data step comparisons

Basics Queries

Proc SQL syntax overview

Specifying columns/creating new columns

Specifying rows/subsetting on rows

Ordering or sorting data

Formatting output results

Presenting detailed data

Presenting summarized data

Sub Queries

Non correlated sub queries

Correlated sub queries

SQL Joins (Combining SAS data sets using SQL Joins)

Introduction to SQL joins

Types of joins with examples

Simple to complex joins

Choosing between data step merges and SQL joins

SET Operators

Introduction to set operations

Except/Intersect/Union/Outer union operator

Additional SQL Procedures features

Creating views with SQL procedure

Dictionary tables and views

Interfacing Proc SQL with the macro programming language

Creating and maintaining indexes

SQL Pass-Through facility

SAS Macro Language

Introduction to macro facility

Generate SAS code using macros

Macro compilation

Creating macro variables

Scope or macro variables

Global/Local Macro variables

User defined /Automatic Macro variables

Macro variables references

Combing macro variables references with text

Macro functions

Quoting (Masking)

Creating macro variables in Data step (Call SYMPUT Routine)

Obtaining variable value during macro execution (SYMGET function)

Creating macro variables during PROC SQL execution (INTO Clause)

Creating a delimited list of values

Macro parameters

Strong Macro using Autocall Features

Permanently storing and using stored compiled macro program

SAS Macro debugging options to track problems

Basics Statistics

Standard deviation

Correlation Coefficients

Outliers

Linear regressions

Clustering

Chi Square