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BASE SAS MODULES Day 1: Introduction to the SAS System Components of Base SAS Software Output Produced by the SAS System Ways to Run SAS Programs Running Programs in the SAS Windowing Environment Introduction to DATA Step Processing The SAS Data Set introduction How the DATA Step Works: A Basic Introduction Supplying Information to Create a SAS Data Set Introduction to Raw Data Examine the Structure of the Raw Data: Factors to Consider Reading Unaligned Data Reading Data That Is Aligned in Columns Reading Data That Requires Special Instructions Reading Unaligned Data with More Flexibility Mixing Styles of Input Day 2: Introduction to Beyond the Basics with Raw Data Using INFILE statement and various options. Testing a Condition before Creating an Observation Creating Multiple Observations from a Single Record Reading Multiple Records to Create a Single Observation Problem Solving: When an Input Record Unexpectedly Does Not have enough values Day 3: Introduction to Starting with SAS Data Sets Understanding the Basics Input SAS Data Set for Examples Reading Selected Observations Reading Selected Variables Creating More Than One Data Set in a Single DATA Step Using the DROP=, KEEP= and WHERE= Data Set Options for Efficiency Introduction to DATA Step Processing Input SAS Data Set for Examples Adding Information to a SAS Data Set Defining Enough Storage Space for Variables Conditionally Deleting an Observation Day 4: Introduction to Working with Numeric Variables About Numeric Variables in SAS Input SAS Data Set for Examples Calculating with Numeric Variables Comparing Numeric Variables Storing Numeric Variables Efficiently Numeric Functions. Day 5: Introduction to Working with Character Variables Input SAS Data Set for Examples Identifying Character Variables and Expressing Character Values Handling Missing Values Creating New Character Values Saving Storage Space by Treating Numbers as Characters Character Functions Day 6: Introduction to Acting on Selected Observations Input SAS Data Set for Examples Selecting Observations Constructing Conditions Comparing Characters Introduction to Creating Subsets of Observations Input SAS Data Set for Examples Selecting Observations for a New SAS Data Set Conditionally Writing Observations to One or More SAS Data Sets Day 7: Introduction to Working with Grouped or Sorted Observations Input SAS Data Set for Examples Working with Grouped Data Working with Sorted Data Introduction to Using More Than One Observation in a Calculation Input File and SAS Data Set for Examples Accumulating a Total for an Entire Data Set Obtaining a Total for Each BY Group Writing to Separate Data Sets Using a Value in a Later Observation Day 8: Introduction to Using More Than One Observation in a Calculation Input File and SAS Data Set for Examples Accumulating a Total for an Entire Data Set Obtaining a Total for Each BY Group Writing to Separate Data Sets Using a Value in a Later Observation Introduction to Working with Dates Understanding How SAS Handles Dates Input File and SAS Data Set for Examples Entering Dates Displaying Dates Using Dates in Calculations Using SAS Date Functions Comparing Durations and SAS Date Values Day 9: Introduction to Combining SAS Data Sets Definition of Concatenating Definition of Interleaving Definition of Merging Definition of Updating Definition of Modifying 237 Comparing Modifying, Merging, and Updating Data Sets Day 10: Introduction to Concatenating SAS Data Sets Concatenating Data Sets with the SET Statement Concatenating Data Sets Using the APPEND Procedure Choosing between the SET Statement and the APPEND Procedure. Introduction to Interleaving SAS Data Sets Understanding BY-Group Processing Concepts Interleaving Data Sets Day 11: Introduction to Merging SAS Data Sets Understanding the MERGE Statement One-to-One Merging Match-Merging Choosing between One-to-One Merging and Match-Merging Introduction to Updating SAS Data Sets Understanding the UPDATE Statement Understanding How to Select BY Variables Updating a Data Set Updating with Incremental Values Understanding the Differences between Updating and Merging Handling Missing Values Day 12: Input SAS Data Set for Examples Modifying a SAS Data Set: The Simplest Case Modifying a Master Data Set with Observations from a Transaction Data Set Understanding How Duplicate BY Variables Affect File Update Handling Missing Values Introduction to Conditional Processing from Multiple SAS Data Sets Input SAS Data Sets for Examples Determining Which Data Set Contributed the Observation Combining Selected Observations from Multiple Data Sets Performing a Calculation Based on the Last Observation Day 13: Introduction to Analysing Your SAS Session with the SAS Log Understanding the SAS Log Locating the SAS Log Understanding the Log Structure Writing to the SAS Log Suppressing Information to the SAS Log Changing the Log’s Appearance Introduction to Directing SAS Output and the SAS Log Input File and SAS Data Set for Examples Routing the Output and the SAS Log with PROC PRINTTO Storing the Output and the SAS Log in the SAS Windowing Environment Redefining the Default Destination in a Batch or Non interactive Environment Introduction to Diagnosing and Avoiding Errors Understanding How the SAS Supervisor Checks a Job Understanding How SAS Processes Errors Distinguishing Types of Errors Diagnosing Errors Using a Quality Control Checklist Day 14: Introduction to Creating Detail and Summary Reports with the REPORT Procedure Understanding How to Construct a Report Input File and SAS Data Set for Examples Creating Simple Reports Creating More Sophisticated Reports Day 15: Introduction to Proc means Deriving descriptive statistics Introduction to Proc univariate and various options Day 16: Introduction to Proc freq Calculating counts using Freq Outputting the counts into a dataset Proc Transpose introduction Using VAR, ID and BY statement efficiently in transpose Reshaping the data with required variables Day 17: Introduction to Producing Charts to Summarize Variables Understanding the Charting Tools Input File and SAS Data Set for Examples Charting Frequencies with the CHART Procedure Customizing Frequency Charts Creating High-Resolution Histograms Day 18: Introduction to Writing Lines to the SAS Log or to an Output File Understanding the PUT Statement Writing Output without Creating a Data Set Writing Simple Text Introduction to the Basics of Understanding and Customizing SAS Output Understanding Output Input SAS Data Set for Examples Locating Procedure Output Making Output Informative Controlling Output Appearance Controlling the Appearance of Pages Representing Missing Values Day 19: Introduction to Customizing SAS Output by Using the Output Delivery System Input Data Set for Examples Understanding ODS Output Formats and Destinations Selecting an Output Format Creating Formatted Output Day 20: Proc Format introduction Creating format catalogue Converting catalogue to dataset Storing formats permanently and finding out formats in a library Accessing a Permanent SAS Data Set with User-Defined Formats ADVANCED SAS MODULES Day 21: Getting Started with the Macro Facility Replacing Text Strings Using Macro Variables Generating SAS Code Using Macros More Advanced Macro Techniques Other Features of the Macro Language Introduction to SAS Programs and Macro Processing How SAS Processes Statements without Macro Activity How SAS Processes Statements with Macro Activity Introduction to Macro Variables Macro Variables Defined by SAS Macro Variables Defined by Users Using Macro Variables Displaying Macro Variable Values Referencing Macro Variables Indirectly Manipulating Macro Variable Values with Macro Functions Introduction to Macro Processing Defining and Calling Macros How the Macro Processor Compiles a Macro Definition How the Macro Processor Executes a Compiled Macro Summary of Macro Processing Day 22: Introduction to the Scopes of Macro Variables Global Macro Variables Local Macro Variables Writing the Contents of Symbol Tables to the SAS Log How Macro Variables Are Assigned and Resolved Introduction to Macro Expressions Defining Arithmetic and Logical Expressions How the Macro Processor Evaluates Arithmetic Expressions Day 23: Introduction to Macro Quoting Deciding When to Use a Macro Quoting Function and Which Function to Use Using Various Macro Functions %STR and %NRSTR Functions etc Using the %BQUOTE and %NRBQUOTE Functions Referring to Already Quoted Variables Deciding How Much Text to Mask with a Macro Quoting Function Using %SUPERQ Summary of Macro Quoting Functions and the Characters They Mask Unquoting Text How Macro Quoting Works Other Functions That Perform Macro Quoting Day 24: Introduction to Storing and Reusing Macros Saving Macros in an Auto call Library Saving Macros Using the Stored Compiled Macro Facility General Macro Debugging Information Troubleshooting Your Macros Debugging Techniques Introduction to Writing Efficient and Portable Macros Keeping Efficiency in Perspective Writing Efficient Macros Writing Portable Macros PROJECT CONTENT COVERED Day Creation of Efficacy tables. Creation of Standard and safety tables. Day 28 Creation of listings Day Creation of Graphs a)Bar Charts b)Scatter plot c)Line plot d)Box plot Day 31 Creation of Analysis datasets/derived datasets Day 32 Introduction to SDTM CRF Annotation (ONLY BASICS) Introduction to mapping specification (ONLY BASICS) Introduction to SDTM dataset Creation (ONLY BASICS) Validation in Open CDISC (ONLY BASICS) Day 33 Introduction to ADAM Standards (ONLY BASICS) Mock interview and providing assistance for interview preparation Contact:

The profession of sales is vast and is in more demand these days. Most of the people who wish to be a successful sales executive fail to achieve their goal due to a number of reasons. The major reasons include lack of training, improper strategy for sales, and improper contact with the customers etc. Skill Factori analyzes the essentials for a successful sales person and thus has come up with a course known as Diploma in Live marketing for individuals with an aspiration to grow high in the field of sales. What are the key features of the course? Diploma in Live marketing and sales is a 30 day long course and the following are its highlights •Classroom training for the first ten days •From day 11, the participants will be in the job field. •Practical teaching on how to collect raw data •Understanding the hierarchy of positions in a sales organization and how to report to a manager •Analysis of data, prospect data, selling data and referral data •During the last stage of the training, the candidates will learn how to convert the data into real time customers. During the period of training, the candidates should prospect at least 240 data and convert at least five of them to sales. Once the candidate is efficient enough to handle sales, he will be placed in a reputed organization. Why Skill Factori for Diploma in Live Management? Skill Factori has a unique curriculum and not only teaches the candidate to work smart, but train them on handling multiple sales in various fields. If you are intending to pursue a career in sales, here is why you need to come to us •We teach pressure management during sales •You will learn how to collect the exact data •Analyze the data in a simple and effective way •Preparation techniques for sales •Effective market research •Strategy management •Customer engagement