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Business Analytics using SAS

Business Analytics Using SAS online training course focuses on linear regression, logistics regression, Analysis of Variance (ANOVA), and regression diagnostics.

  • 50000
  • 55000
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  • Course Includes
  • Live Class Practical Oriented Training
  • 120 + Hrs Instructor LED Training
  • 60 + Hrs Practical Exercise
  • 35 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option
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What you will learn

  • you will learn SAS macros, Machine Learning, PROC SQL, procedure, statistical analysis and decision trees.
  • You will Learn data mining, data analytics, modeling techniques, visualization of data, predictive analysis
  • Generate descriptive statistics & explore data with graphs. Perform analysis of variance & apply multiple comparison tec...
  • Perform linear regression and assess the assumptions. Use regression model selection techniques to aid in the choice of...
  • Explore data analysis and perform linear regression
  • Illustrate the use of One-Way ANOVA and Two-Way ANOVA
  • Conduct regression diagnostics and find out the influential observations. Identify the use of logistic regression and ta...
  • Interpretive predictive modeling and Adjustments for Oversampling. Demonstrate the use of nonlinearities and multilayer...

Requirements

  • Basic understanding of statistics covering p-values, hypothesis testing, analysis of variance, and regression

Description

|| About Business Analytics using SAS Training Course

Business Analytics Using SAS online training course is for those who want to enhance their predictive and statistical modeling skills to drive data-informed business outcomes. If modeling data for business outcomes is relevant in your job role or industry, this certificate is a valuable indication of your proficiency.  

 

Business Analytics for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum. A more advanced treatment of ANOVA and regression occurs in the Statistics ANOVA and Regression course. A more advanced treatment of logistic regression occurs in the Categorical Data Analysis Using Logistic Regression course and the Predictive Modeling Using Logistic Regression course. This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets.There are numerous hands-on practices integrated throughout the three courses of the program. Data examples are general enough to be applicable to a broad range of subject areas. Specific examples you will see in the courses address agriculture, manufacturing, health care, banking, retail, and nonprofit.

Course Content

Part-1 Access and Create Data Structures

·       Lecture-1 SAS Programs

·       Introduction to SAS programs

·       Submitting a SAS program

·       Working with SAS program syntax

·       Lecture-2 Create temporary and permanent SAS data sets.

·       Use a DATA step to create a SAS data set from an existing SAS data set.

·       Example:

·       Data Mylib.NewData;

·       Set Mylib.OldData;

·       <other SAS statements>

·       Run;

·       Lecture-3 Investigate SAS data libraries using base SAS utility procedures.

·       Use a LIBNAME statement to assign a library reference name to a SAS library.

·       Investigate a library programmatically using the CONTENTS procedure.

·       Lecture-4 Combine SAS data sets.

·       Concatenate data sets.

·       Merge data sets one-to-one.

·       Merge data sets one-to-many.

·       Lecture-5 Access an Excel workbook.

·       Use the SAS/ACCESS XLSX engine to read an .xlsx file.

·       Lecture-6 Create and manipulate SAS date values.

·       Explain how SAS stores date and time values.

·       Use SAS date and time formats to specify how the values are displayed.

·       Lecture-7 Export data to create standard and comma-delimited raw data files.

·       Create a simple raw data file by using the EXPORT procedure as an alternative to the DATA step.

·       Lecture-8 Control which observations and variables in a SAS data set are processed and output.

·       Use the WHERE statement in the DATA step to select observations to be processed.

·       Subset variables to be output by using the DROP and KEEP statements.

·       Use the DROP= and KEEP= data set options to specify columns to be processed and/or output.
Part-2 Manage Data

·       Lecture-9 Sort observations in a SAS data set.

·       Use the SORT Procedure to re-order observations in place or output to a new dataset.

·       Lecture-10 Conditionally execute SAS statements.

·       Use IF-THEN/ELSE statements to process data conditionally.

·       Use DO and END statements to execute multiple statements conditionally.

·       Lecture-11 Use assignment statements in the DATA step.

·       Create new variables and assign a value.

·       Assign a new value to an existing variable.

·       Assign the value of an expression to a variable.

·       Assign a constant date value to a variable.

·       Lecture-12 Modify variable attributes using options and statements in the DATA step.

·       Change the names of variables by using the RENAME= data set option.

·       Use LABEL and FORMAT statements to modify attributes in a DATA step.

·       Define the length of a variable using the LENGTH statement.

·       Lecture-13 Accumulate sub-totals and totals using DATA step statements.

·       Use the BY statement to aggregate by subgroups.

·       Lecture-14 Use SAS functions to manipulate character data, numeric data, and SAS date values.

·       Use SAS functions such as SCAN, SUBSTR, TRIM, UPCASE, and LOWCASE to perform tasks such as the tasks shown below.

·       Replace the contents of a character value.

·       Trim trailing blanks from a character value.

·       Search a character value and extract a portion of the value.

·       Convert a character value to upper or lowercase.

·       Use SAS arithmetic, financial, and probability functions to create or modify numeric values by using the INT and ROUND functions.

·       Create SAS date values by using the functions MDY, TODAY,DATE, and TIME.

·       Extract the month, year, and interval from a SAS date value by using the functions YEAR, QTR, MONTH, and DAY.

·       Perform calculations with date and datetime values and time intervals by using the functions INTCK, INTNX, DATDIF and YRDIF.

·       Lecture-15 Use SAS functions to convert character data to numeric and vice versa.

·       Explain the automatic conversion that SAS uses to convert values between data types.

·       Use the INPUT function to explicitly convert character data values to numeric values.

·       Lecture-16 Process data using DO LOOPS.

·       Explain how iterative DO loops function.

·       Use DO loops to eliminate redundant code and to perform repetitive calculations.

·       Use conditional DO loops.

·       Use nested DO loops.

·       Lecture-17 Validate and clean data.

·       Use PROC FREQ to list unique values, with the nlevel option to show the number of distinct values, with the order=freq to check for duplicate or missing values.

·       Use PROC PRINT with the WHERE statement to display observations with invalid values.

·       Use PROC MEAN to validate the range of numeric variables.

·       Use PROC UNIVARIATE to display extreme observations and missing values and with the ID statement to display the value of identifying variable(s)
Part-3 Generate Reports

·       Lecture-18 Generate list reports using the PRINT procedure.

·       Modify the default behavior of PROC PRINT by adding statements and options such as

·       use the VAR statement to select and order variables.

·       calculate totals with a SUM statement.

·       select observations with a WHERE statement.

·       use the ID statement to identify observations.

·       use the BY statement to process groups.

·       Lecture-19 Generate summary reports and frequency tables using base SAS procedures.

·       Produce one-way and two-way frequency tables with the FREQ procedure.

·       Enhance frequency tables with options.

·       Use PROC FREQ to validate data in a SAS data set.

·       Calculate summary statistics and multilevel summaries using the MEANS procedure

·       Enhance summary tables with options.

·       Identify extreme and missing values with the UNIVARIATE procedure.

·       Lecture-20 Enhance reports through the use of user-defined formats, titles, footnotes and SAS System reporting.

·       Use the LABEL statement to define descriptive column headings.

·       Control the use of column headings with the LABEL and SPLIT=options in Proc Print output.

·       Lecture-21 Generate reports using ODS statements.

·       Identify the Output Delivery System destinations.

·       Create HTML, PDF, RTF, and files with ODS statements.

·       Use the STYLE=option to specify a style template.

·       Create files that can be viewed in Microsoft Excel.
Part-4 Error Handling

·       Lecture-22 Identify and resolve programming logic errors.

·       Use the PUTLOG Statement in the Data Step to help identify logic errors.

·       Use PUTLOG to write the value of a variable, formatted values, or to write values of all variables.

·       Use PUTLOG with Conditional logic.

·       Use temporary variables N and ERROR to debug a DATA step.

·       Lecture-23 Recognize and correct syntax errors.

·       Identify the characteristics of SAS statements.

·       Define SAS syntax rules including the typical types of syntax errors such as misspelled keywords, unmatched quotation marks, missing semicolons, and invalid options.

·       Use the log to help diagnose syntax errors in a given program.

·       Lecture-24 Examine and resolve data errors.

·       Given a SAS program, use the log to determine the reason for a data error.
Part-1 Accessing Data Using SQL

·       Lecture-1 Generate detail reports in SQL

·       Use PROC SQL to perform SQL queries.

·       Select columns in a table with a SELECT statement and FROM clause.

·       Create a table from a query result set.

·       Create new calculated columns.

·       Assign an alias with the AS keyword.

·       Use case logic to select values for a column.

·       Retrieve rows that satisfy a condition with a WHERE clause.

·       Subset data by calculated columns.

·       Join tables - inner joins, full joins (coalesce function), right joins, left joins.

·       Combine tables using set operators - union, outer union, except, intersect.

·       Sort data with an ORDER BY clause.

·       Assign labels and formats to columns.

·       Lecture-2 Generate summary reports in the SQL.

·       Summarize data across and down columns using summary functions (AVG, COUNT, MAX, MIN, SUM).

·       Group data using GROUP BY clause.

·       Filter grouped data using HAVING clause.

·       Eliminate duplicate values with the DISTINCT keyword.

·       Lecture-3 Construct sub-queries and in-line views.

·       Subset data by using non-correlated subqueries.

·       Reference an in-line view with other views or tables (multiple tables).

·       Lecture-4 Use SAS SQL procedure enhancements.

·       Use SAS data set options with PROC SQL (KEEP=, DROP=, RENAME=, OBS=).

·       Use PROC SQL invocation options (INOBS=, OUTOBS=. NOPRINT, NUMBER)

·       Use SAS functions (SCAN, SUBSTR, LENGTH).

·       Access SAS system information by using DICTIONARY tables (members, tables, columns)

·       Use the CALCULATED keyword.
Part-2 Macro Processing

·       Lecture-5 Uuse user-defined and automatic macro variables

·       Use macro variable name delimiter. (.)

·       Use INTO clause of the SELECT statement in SQL to create a single variable or a list of variables.

·       Use the SYMPUTX routine in a DATA Step to create a single variable or a list of variables.

·       Control variable scope

·       Lecture-6 Automate programs by defining and calling macros.

·       Define a macro using the %MACRO and %MEND statements.

·       Calling a macro with and without parameters.

·       Document macro functionality with comments

·       Generate SAS Code conditionally by using the %IF-%THEN-%ELSE macro statements or iterative %DO statements.

·       Use the SAS AUTOCALL facility to permanently store and call macros.

·       Lecture-7 Use macro functions.

·       Use macro functions. (%SCAN, %SUBSTR, %UPCASE)

·       Use macro quoting functions. (%NRSTR, %STR)

·       Use macro evaluation functions. (%SYSEVALF)

·       Use %SYSFUNC to execute DATA step functions within the SAS Macro Language.

·       Lecture-8 Debug macros.

·       Trace the flow of execution with the MLOGIC option.

·       Examine the generated SAS statements with the MPRINT option.

·       Examine macro variable resolution with the SYMBOLGEN option.

·       Use the %PUT statement to print information to the log.

·       Lecture-9 Data-driven programs using SAS Macro Language.

·       Create a series of macro variables.

·       Use indirect reference to macro variables. (&&, etc.)

·       Incorporate DICTONARY tables in data driven macros.

·       Generate repetitive macro calls.
Part-3 Advanced Techniques

·       Lecture-10 Process data using 1 and 2 dimensional arrays.

·       Define and use character arrays.

·       Define and use numeric arrays.

·       Create variables with arrays.

·       Reference arrays within a DO loop.

·       Specify the array dimension with the DIM function.

·       Define arrays as temporary arrays.

·       Load initial values for an array from a SAS data set.

·       Lecture-11 Process data using hash objects.

·       Declare hash and hash iterator objects

·       Dataset argument

·       Ordered argument

·       Multidata argument

·       Use hash object methods

·       definekey()

·       definedata()

·       definedone()

·       find()

·       add()

·       output()

·       Use hash iterator object methods

·       first()

·       next()

·       last()

·       prev()

·       Use hash objects as lookup tables.

·       Use hash objects to create sorted data sets.

·       Use hash iterator objects to access data in forward or reverse key order.

·       Lecture-12 Use SAS utility procedures.

·       Specify a template using the PICTURE statement within the FORMAT Procedure*

·       Specify templates for date, time, and datetime values using directives.

·       Specify templates for numeric values using digit selectors.

·       PICTURE statement options: round, default, datatype, multiplier, prefix

·       Create custom functions with the FCMP procedure

·       Create character and numeric custom functions with single or multiple arguments.

·       Create custom functions based on conditional processing.

·       Use custom functions with the global option CMPLIB=.

·       Lecture-13 Use advanced functions.

·       Finding strings or words with the FINDC/FINDW functions.

·       Counting strings or words with the COUNT/COUNTC/COUNTW functions.

·       Retrieve previous values with the LAG function.

·       Regular expression pattern matching with PRX functions
Lecture-1 Introduction to Statistics

·       Fundamental Statistical Concepts

·       Examining Distributions

·       Confidence Intervals for the Mean

·       Hypothesis Testing
Lecture-2 Analysis of Variance (ANOVA)

·       One-Way ANOVA: Two Populations

·       ANOVA with More than Two Populations

·       Two-Way ANOVA with Interactions
Lecture-3 Regression

·       Exploratory Data Analysis

·       Simple Linear Regression

·       Concepts of Multiple Regressions

·       Model Building and Interpretation
Lecture-4 Regression Diagnostics

·       Examining Residuals

·       Influential Observations

·       Collinearity
Lecture-5 Categorical Data Analysis

·       Describing Categorical Data

·       Tests of Association

·       Introduction to Logistic Regression

·       Multiple Logistic Regressions

·       Logit Plots (Self-Study)
Lecture-6 Predictive Modeling

·       Introduction

·       Analytical Challenges
Lecture-7 Fitting the Model

·       The Model

·       Adjustments for Oversampling
Lecture-8 Preparing the Input Variables

·       Missing Values     

·       Categorical Inputs

·       Variable Clustering

·       Subset Selection
Lecture-9 Classifier Performance

·       Honest Assessment

·       Misclassification

·       Allocation Rules

·       Overall Predictive Power
Lecture-10 Nonlinearities and Interactions

·       Detection

·       Polynomials

·       Multilayer Perceptions

Fees

Offline Training @ Vadodara

  • Classroom Based Training
  • Practical Based Training
  • No Cost EMI Option
60000 55000

Online Training preferred

  • Live Virtual Classroom Training
  • 1:1 Doubt Resolution Sessions
  • Recorded Live Lectures*
  • Flexible Schedule
55000 50000

Corporate Training

  • Customized Learning
  • Onsite Based Corporate Training
  • Online Corporate Training
  • Certified Corporate Training

Certification

  • Upon the completion of the Classroom training, you will have an Offline exam that will help you prepare for the Professional certification exam and score top marks. The BIT Certification is awarded upon successfully completing an offline exam after reviewed by experts
  • Upon the completion of the training, you will have an online exam that will help you prepare for the Professional certification exam and score top marks. BIT Certification is awarded upon successfully completing an online exam after reviewed by experts.
  • This course is designed to clear SAS Certifications: Exam ID A00-215 SAS Certified Associate: Programming Fundamentals Using SAS 9.4, Exam ID A00-231SAS Certified Specialist: Base Programming Using SAS 9.4, ID A00-232 SAS® Certified Professional: Advanced Programming Using SAS® 9.4 and A00-240 SAS® Certified Statistical Business Analyst Using SAS®9:Regression and Modeling

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