Data Analytics Using SAS Course

·       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.

·       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.

·       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

·       Lecture-2 Analysis Methods and SAS/ETS Software

·       Options

·       How SAS/ETS Software Procedures Interrelate

·       Lecture-3 Simple Models: Regression

·       Linear Regression

·       Highly Regular Seasonality

·       Regression with Transformed Data

·       Lecture-4 Introduction

·       Terminology and Notation

·       Statistical Background

·       Lecture-5 Forecasting

·       Forecasting with PROC ARIMA

·       Backshift Notation B for Time Series

·       Yule-Walker Equations for Covariances

·       Fitting an AR Model in PROC REG

·       Lecture-6 Introduction

·       Statistical Background

·       Terminology and Notation

·       Lecture-7 Prediction

·       One-Step-Ahead Predictions

·       Future Predictions

·       Lecture-8 Model Identification

·       Stationarity and Invertibility

·       Time Series Identification

·       Chi-Squared Check of Residuals

·       Summary of Model Identification

·       Lecture-9 Examples and Instructions

·       IDENTIFY Statement for Series

·       Example: Iron and Steel Export Analysis

·       Estimation Methods Used in PROC ARIMA

·       ESTIMATE Statement for Series

·       Nonstationary Series

·       Effect of Differencing on Forecasts

·       Examples: Forecasting IBM Series and Silver Series

·       Models for Nonstationary Data

·       Differencing to Remove a Linear Trend

·       Other Identification Techniques

·       Lecture-10 Seasonal Time Series

·       Introduction to Seasonal Modeling

·       Model Identification

·       Lecture-11 Models with Explanatory Variables

·       Case 1: Regression with Time Series Errors

·       Case 1A: Intervention

·       Case 2: Simple Transfer Function

·       Case 3: General Transfer Function

·       Case 3A: Leading Indicators

·       Case 3B: Intervention

·       Lecture-12 Methodology and Example

·       Case 1: Regression with Time Series Errors

·       Case 2: Simple Transfer Functions

·       Case 3: General Transfer Functions

·       Case 3B: Intervention

·       Lecture-13 Further Examples

·       North Carolina Retail Sales

·       Construction Series Revisited

·       Milk Scare (Intervention)

·       Terrorist Attack

·       Lecture-14 Regression with Time Series Errors and Unequal Variances

·       Autoregressive Errors

·       Example: Energy Demand at a University

·       Unequal Variances

·       ARCH, GARCH, and IGARCH for Unequal Variances

·       Lecture-15 Cointegration

·       Introduction

·       Cointegration and Eigenvalues

·       Impulse Response Function

·       Roots in Higher-Order Models

·       Cointegration and Unit Roots

·       An Illustrative Example

·       Estimating the Cointegrating Vector

·       Intercepts and More Lags

·       PROC VARMAX

·       Interpreting the Estimates

·       Diagnostics and Forecasts

·       Lecture-16 Introduction

·       Some Simple Univariate Examples

·       A Simple Multivariate Example

·       Equivalence of Statespace and Vector ARMA Models

·       Lecture-17 More Examples

·       Some Univariate Examples

·       ARMA (1, 1) of Dimension

·       Lecture-18 PROC STATESPACE

·       State Vectors Determined from Covariances

·       Canonical Correlations

·       Simulated Example

·       Lecture-18 PROC STATESPACE

·       State Vectors Determined from Covariances

·       Canonical Correlations

·       Simulated Example

·       Practical Exercise

·       Lecture-19 Periodic Data: Introduction

·       Example: Plant Enzyme Activity

·       PROC SPECTRA Introduced

·      Testing for White Noise

·      Harmonic Frequencies

·      Extremely Fast Fluctuations and Aliasing

·      The Spectral Density

·       Some Mathematical Detail (Optional Reading)

·       Estimating the Spectrum: The Smoothed Periodogram

·       Cross Spectral Analysis

·       Interpreting Cross-Spectral Quantities

·       Interpreting Cross-Amplitude and Phase Spectra

·       PROC SPECTRA Statements

·       Cross-Spectral Analysis of the Neuse River Data

·       Details on Gain, Phase, and Pure Delay

·       Lecture-20 Introduction Data Mining and Forecasting

·       Forecasting Data Model

·       Time Series Forecasting System

·       HPF Procedure

·       Scorecard Development

·       Business Goal Performance Metrics

·       Graphical Displays

·       Goal-Seeking Model Development