1. BI reporting and Data
Analysis Training
DV Analytics Training Institute
dvanalytics.training@gmail.com
http://dvanalyticstraininginstitute.blogspot.in
https://www.facebook.com/DvAnalyticsTrainingInstitute
9591793303
Koramangala 1st block, Jakasandra,Near HDFC
Bank,Bangalore-560034

2. SAS Programming Base and Advanced
SAS Base Programming:
SAS Programming 1:
 Introduction SAS system and Getting Familiar to SAS environment
 Creating Libraries and Datasets using Data Step
 Producing List Reports using Proc Step
 Data Manipulation Techniques using
 Data Step Vs Proc Step
 Format Vs Informat
 Reading raw data files using Infile and Proc Import statement
 PDV
 Examining Errors in SAS programing
 Conditional processing using If, Where, Keep, Drop statement
 Remove Duplicate records using Proc Sort
 Combining SAS dataset using SAS Merge and Set statement

 Summary Reports
 Proc Means, Proc Freq, Proc Summary, Proc Univariate, Proc Report, Proc Tabulate
SAS Programming 2:
 Introduction to Base SAS programming with Statements, Options and Functions
 Controlling Input and Output observation
 Data Manipulation Techniques using
 Writing Multiple Dataset
 Data Transformation
 Transposing and Expanding Dataset
 SAS Functions (Numeric and Character)
 Writing to External File
 Creating An Accumulating Total Variable
 Combining Duplicate Records Using First. And Last.
 Reading Delimited Raw Data File in .txt (text File),.csv (CSV File),.xlsx (Excel File) and .accdb (Access Database)
 DSD, DLM, MISSOVER,TRUNCOVER,STOPOVER and FLOWOVER options used in reading raw data file
 Connecting SAS to Other Database Server
 Debugging Techniques
 Put Statement
 Debug Options
 Processing Data Interactively
 DO Loop
 SAS Arrays
SAS Advanced Programming:
SAS SQL Processing
 Accessing Data Using SQL
 Generate detail reports by working with a single table or joining tables using PROC SQL and the appropriate options
 Generate summary reports by working with a single table or joining tables using PROC SQL and the appropriate options
 Construct sub queries within a PROC SQL step
 Compare solving a problem using the SQL procedure versus using traditional SAS programming techniques
 Access Dictionary Tables using the SQL procedure
 Demonstrate advanced PROC SQL skills by creating and updating tables, updating data values, working with indexes using the
macro interface/creating macro variables with SQL, defining integrity constraints, SQL views and SET operators
Macro Processing
 Creating and using user-defined and automatic macro variables within the SAS Macro Language
 Automate programs by defining and calling macros using the SAS Macro Language

3.  Understand the use of macro functions
 Recognize various system options that are available for macro debugging and displaying values of user-defined and automatic
macro variables in the SAS log
Advanced Programming Techniques
 Demonstrate advanced data set processing techniques such as updating master data sets, transposing data, combining/merging
data, sampling data, using generation data sets, integrity constraints and audit trails
 Reduce the space required to store SAS data sets and numeric variables within SAS data sets by using compression techniques,
length statements or DATA step views
 Develop efficient programs by using advanced programming techniques such as permanent formats and array processing
 Use SAS System options and SAS data set options for controlling memory usage
 Control the processing of variables and observations in the DATA step
 Create sorted or indexed data in order to avoid unnecessary sorts, eliminate duplicate data and to provide more efficient data
access and retrieval
 Use PROC DATASETS to demonstrate advanced programming skills (e.g. renaming columns, displaying metadata, creating indexes,
creating integrity constraints, creating audit trails)
SAS Project-Practical
EXCEL Base and Advanced
Excel Base:
 Introduction MS Excel
 Navigation technique in Excel
 Cells Reference, Range, Rows and Columns
 Format Paint, Border Style and Designing, Cell Merging, Conditional Formatting, Sorting and filtering, Data Validation,
Data consolidation
 Data Import and Export
 Basic Pivot Table, Chart
 Excel Formulas and Functions like IF and Nested IF, Vlook-up, HLook-up, Sum,Sum IF,Match, Offset and Index etc.
 Running Manual Excel Macro and Recording
Excel Advanced:
 Advanced Data Manipulation Techniques
 Advanced Pivot Design
 Advanced Pivot Options for reporting
 Power Pivot technique
 Excel Dashboard using Excel functions and VBA Macros
 Excel VBA Programming
Excel Project-Practical
ACCESS Base and Advanced
ACCESS Base and Advanced:
 Introduction MS ACCESS
 Navigation technique in ACCESS and Access Objects
 Creating Database, Tables, Field Properties
 Access Queries (Select, Make Table, Append, Update, Delete, Crosstab, Union and Union All)
 Data Import and Export in Access
 Access Pivot Table, Chart
 Access Join
 Forms and Reports
 Access Formulas and Functions

4.  Access Modules using Access VBA
 Access Data Manipulation technique using SQL queries
Access Project-Practical
Qlikview and Tableau BI Dashboard Making
 Introduction to Qlikview
 Various data & dash board related options
 Creating dashboards using Qlikview
 Introduction to tableau
 Various data & dash board related options
 Creating dashboards using Tableau
Basic and Advanced Data analytics
 Introduction to basic descriptive statistics
 Introduction to basic statistical analysis
o Hands-on exercises
 Data exploration & Data preparation
o Hands-on exercises
 Linear Regression model building
o Hands-on exercises on simple linear model
o Hands-on exercises on multiple linear models
 Logistic Regression model building
o Hands-on exercises on Logistic Regression
 Customer segmentation using cluster analysis
o Hands-on exercises on sample data
 Decision tree models
o Hands on exercises on sample data
 Hypothesis testing with examples
o Hands on exercises on sample data
 Time series forecasting
o Hands on excesses on prediction
 Step by step process of credit risk model building
Data analysis practical project
 Practical Data importing, Data cleaning
 Analysis design
 Creating the BI report
 Designing the analysis solution
 Performing the analysis and building a predictive model
 Presentation of result
 Final documentation