Best Data Science with SAS Training Institute in Delhi NCR

• Introduction to Excel
• Working with Formulas and functions
• Conditional Formatting
• Advance Filtering, Sorting, Paste special 15different functions.
• Functions (Logical & Text, Mathematical, Statistical)
• Data Manipulation & Data Consolidation
• Data Analysis using Analysis Tool Pack (Histogram, Correlation, etc)
• Analysing Data using Pivot Tables
• Use in Quality Industry using Pareto Analysis
• Creating Charts & Graphs
• Data analytical tool (What -if analysis, Goal Seek, Data Table, Solver)
• Protecting Workbooks, Worksheets and Formula

Analytics using SAS

• Introduction to SAS, GUI
• Creation and Analysis of SAS Dataset (Data & Proc Steps - Statements & options)
• Debugging SAS Codes
• Importing different types of data & connecting to databases
• Data Understanding (Variable Attributes (Format, Informat, Length, Label etc))
• Concepts of Libraries, PDV, Data Execution, etc.
• SAS Procedures for data import /export / understanding (Proc import/Proc Export)

• Data Manipulation steps (Sorting, Filtering, Duplicates, Merging, Appending, Sub-setting, Derived Variables, Sampling, Datatype Conversions Renaming, Formatting, etc)
• Data manipulation tools (Operators, Functions, Procedures, Conditional, Loops, Arrays, etc)
• SAS Functions (Text, Numeric, Date, Utility Functions)
• SAS Procedures for data manipulation (Procsort, proc format, etc)
• SAS Options (System Level, procedure level)
• Debugging SAS Codes
• Importing different types of data & connecting to databases
• Data Understanding (Variable Attributes (Format, Informat, Length, Label etc))
• Concepts of Libraries, PDV, data execution, etc
• SAS Procedures for data import /export /understanding (Proc import/Proc Export)

ANNOVA

• Verify the assumptions of ANOVA
• Analyse differences between population means using the GLM and TTEST procedures

• Linear Regression - Introduction - Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable Significance, R-square/Adjusted R-Square)
• Validation of Linear Regression Models (Rerunning Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model Equation, Drivers etc)

Advanced SAS

• Introduction to Advanced SAS - Proc SQL &Macros
• Understanding select statement (From, where, group by, having, order by etc)
• Proc SQL - Data creation/extraction
• Proc SQL - Data Manipulation steps
• Proc SQL - Summarizing Data
• Proc SQL - Concept of sub queries, indexes etc
• SAS Macros - Creating/defining macro variables
• SAS Macros - Defining/calling macros
• SAS Macros- Concept of local/global variables
• SAS Macros - Debugging techniques

• Types of Data (Metric and Non-Metric)
• Descriptive statistics, Inferential Statistics
• Introduction to Data Analysis
• Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
• SAS Procedures for Data Analysis (proc freq/Procmeans/proc summary/proc tabulate/Procunivariate etc)
• SAS Procedures for Graphical Analysis (Proc Sgplot, proc gplot etc)

T-Test Interpretation

• Concept of Hypothesis Testing
• P-Value Approach in Accepting and Rejecting Hypothesis
• DV and IV
• Interpretation of Results - Business Validation - Implementation on new data

Chi Square Test

• Working on NOMINAL data
• Cramer’s V importance

• Logistic Regression - Introduction - Applications
• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification etc)
• Validation of Logistic Regression Models
• Interpretation of Results - Business Validation -Implementation on new data.

• Classroom Training
• Online Training
• Corporate Training
• On campus Training