SAS - Data Science Complete Course

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Can be taken anytime
Course Type
Professional Training Course
Yes (Details)
Course Fee
$200 $60 only

Course Overview

In this SAS Course , you'll become an expert in analytics techniques using the SAS data science tool. You'll learn how to apply data manipulation and optimization techniques; advanced statistical concepts like clustering, linear regression, and decision trees; data analysis methods to solve real-world business problems and predictive modelling techniques. This SAS course will give you practical knowledge you can apply on your next data analysis job.

This course will enable you to:

  • Understand the role of data scientists, various analytics techniques, and widely used tools
  • Gain an understanding of SAS, the role of GUI, library statements, importing and exporting of data and variable attributes
  • Gain an in-depth understanding of statistics, hypothesis testing, and advanced statistical techniques like clustering, decision trees, linear regression, and logistic regression
  • Learn the various techniques for combining and modifying datasets like concatenation, interleaving, one-to-one merging and reading. You will also learn the various SAS functions and procedure for data manipulation
  • Understand PROC SQL, its syntax, and master the various PROC statements and subsequent statistical procedures used for analytics including PROC UNIVARIATE, PROC MEANS, PROC FREQ, PROC CORP, and more.
  • Understand the power of SAS Macros and how they can be used for faster data manipulation and for reducing the amount of regular SAS code required for analytics
  • Gain an in-depth understanding of the various types of Macro variables, Macro function SYMBOLGEN System options, SQL clauses, and the %Macro statement
  • Learn and perform data exploration techniques using SAS
  • Understand various time series models and work on those using SAS
  • Model, formulate and solve data optimization by using SAS and OPTMODEL procedure

Who should take this course

There is an increasing demand for skilled data scientists across all industries that make this course suitable for participants at all levels of experience. We recommend this data science training especially for the following professionals:

  • Graduates looking to build a career in analytics and data science
  • Analytics professionals who want to work with SAS
  • IT professionals looking for a career switch in the fields of analytics
  • Software developers interested in pursuing a career in analytics
  • Experienced professionals who would like to harness data science in their fields


Internationally Accepted Certificate

Course content

Duration of Course:

  • 80+ hours

Topics Covered are:

Chapter 1: Introduction to sas:

  • Introduction
  • Need for sas
  • Who uses sas
  • What is sas?
  • Overview of base sas software
  • Data management facility
  • Structure of sas dataset
  • Sas program
  • Programming language
  • Elements of the sas language
  • Rules for sas statements
  • Rules for most sas names
  • Special rules for variable names
  • Types of variables
  • Data analysis and reporting utilities
  • Traditional output
  • Ways to run sas programs
  • Sas windowing environment
  • Noninteractive mode
  • Batch mode
  • Interactive line mode
  • Running programs in the sas windowing environment

Chapter 2: How sas works:

  • Writing your first sas program
  • A simple program to read raw data and produce a report
  • Enhancing the program
  • More on comment statements
  • Internal processing in sas
  • How sas works
  • The compilation phase
  • The execution phase
  • Processing a data step: a walkthrough
  • Creating the input buffer and the program data vector
  • Writing an observation to the sas data set
  • Four types of sas libraries
  • Sas libraries
  • Work library
  • Sashelp library
  • Sasuser library

Chapter 3:Reading raw data into sas:

  • What is raw data
  • Definitions
  • Data values
  • Numeric value
  • Character value
  • Standard data
  • Nonstandard data
  • Numeric data
  • Character data
  • Choosing an input style
  • List input
  • Modified list input
  • Column input
  • Formatted input
  • Named input
  • Instream data
  • Creating multiple records from single input row
  • Reading data from external files
  • Reading blank separated values (list or free form data):
  • Reading raw data separated by commas (.csv files):
  • Reading in raw data separated by tabs (.txt files):
  • Using informats with list input
  • Supplying an informat statement with list input
  • Using list input with embedded delimiters
  • Reading raw data that are aligned in columns:
  • Method 1: column input
  • Method 2: formatted input
  • Using more than one input statement: the single trailing @
  • Reading column data that is on more than one line
  • Mixed-style input:
  • Infile options for special situations
  • Flowover
  • Missover
  • Truncover
  • Pad
  • Using lrecl to read very long lines of raw data
  • Checking your data after it has been read into sas

Chapter 4: Reading data from a dataset:

  • Introduction
  • Set statement overview
  • Automatic variables in sas
  • Interleave multiple sas data sets
  • Combine multiple sas data sets
  • Creating & modifying variables
  • Creating multiple datasets in a single data-step
  • Subsetting observations
  • Conditional sas statements
  • Logical and special operators
  • The sas supervisor and the set statement
  • Efficiency and the set statement
  • Know your data
  • Set statement data set options
  • Drop and keep options
  • Rename option
  • Firstobs and obs options
  • In option -
  • Where option -
  • Other set statement options
  • End option
  • Key option
  • Nobs option
  • Point option
  • Do loops and the set statement
  • Introduction to retain statement
  • Carry over values from one observation to another
  • Compare values across observations
  • Assign initial values
  • Determining column order in output dataset
  • Sas system options

Chapter 5: Reading data from a dataset:

  • Input sas data set for example
  • Selecting observations for a new sas data set
  • Deleting observations based on a condition
  • Accepting observations based on a condition
  • Comparing the delete and subsetting if statements
  • Methods of creating new data sets with a subset
  • Subsetting records from an external file with a subsetting if statement
  • Subsetting observations in a data step with a where statement
  • Subsetting observations in a proc step with a where statement
  • Subsetting observations in proc sql
  • Difference between if and where

Chapter 6: Sas informats and formats:

  • Overview
  • Using sas informats
  • Input statement
  • Input function
  • Inputn and inputc functions
  • Attrib and informat statements
  • Using sas formats
  • Format statement in procedures
  • Put statement
  • Put function
  • Putn and putc functions
  • Bestw. Format
  • Additional comments

Chapter 7: Sas functions:

  • Categories of functions
  • Sas character functions
  • Functions that change the case of characters
  • Upcase
  • Lowcase
  • Propcase
  • Functions that remove characters from strings
  • Function: compbl
  • Function: compress
  • Functions that search for characters
  • Function: anyalnum
  • Function: anyalpha
  • Function: anydigit
  • Function: anypunct
  • Function: anyspace
  • Function: notalnum
  • Function: notalpha
  • Function: notdigit
  • Function: notupper
  • Functions that search strings
  • Find and findc
  • Index, indexc, and indexw
  • Functions to verify data
  • Function verify
  • Functions that extract parts of strings
  • Function: substr (on the left-hand side of the equal sign)
  • Function: substrn
  • Functions that join two or more strings together
  • Function: cat
  • Function: cats
  • Function: catt
  • Function: catx
  • Functions that remove blanks from strings
  • Function: left
  • Function: right
  • Function: trim
  • Function: trimn
  • Function: strip
  • Functions that compare strings
  • Function: compare
  • Functions that divide strings into ''words''
  • Function: scan
  • Function: scanq
  • Functions that substitute letters or words in strings
  • Function: translate
  • Function: tranwrd
  • Functions that compute the length of strings
  • Function: length
  • Function: lengthc
  • Function: lengthm
  • Function: lengthn
  • Functions that count the number of letters or substrings in a string
  • Function: count
  • Function: countc
  • Miscellaneous string functions
  • Function: missing
  • Function: repeat
  • Function: reverse
  • Sas date and time functions
  • Introduction
  • What is a sas date and time literal?
  • Date and time functions
  • Functins to create date and time values
  • Functions to takie datetime values apart
  • Functions to get quarter ,year or day of the date
  • Functions that work with intervals
  • Using formats for date and time
  • System options fordate and time

Chapter 8: an Introduction to arrays and array processing:

  • Why do we need arrays?
  • Basic array concepts
  • Array statement
  • Array references
  • Variable name array reference
  • Using array indexes
  • One dimension arrays
  • Multi-dimension arrays
  • Temporary arrays
  • Sorting arrays
  • Determining array bounds: lbound and hbound functions
  • When to use arrays
  • Common errors and misunderstandings
  • Invalid index range
  • Function name as an array name
  • Array referenced in multiple data steps, but defined in only one

Chapter 9: By - group processing:

  • Definitions for by-group processing
  • By-group processing
  • By value
  • By group
  • First.variable and last.variable
  • Modifying sas data sets: examples.
  • Invoking by-group processing
  • Preprocessing input data for by-group processing

About Course Provider

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