BI reporting and Data
Analysis Training
DV Analytics Training Institute
dvanalytics.training@gmail.com
http://dvanalyticst...
SAS Programming Base and Advanced
SAS Base Programming:
SAS Programming 1:
 Introduction SAS system and Getting Familiar ...
 Understand the use of macro functions
 Recognize various system options that are available for macro debugging and disp...
 Access Modules using Access VBA
 Access Data Manipulation technique using SQL queries
Access Project-Practical
Qlikview...
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Bi reporting and data analysis training Contents

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Bi reporting and data analysis training Contents

  1. 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. 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. 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. 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

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