Data Analytics Master's Program

·     Introduction to Excel spreadsheet

·     Learning to enter data

·     Flling of series and custom fill list

·     Editing and deleting fields

·     Practical Exercise

·     Learning about relative and absolute referencing

·     The concept of relative formulae

·     The issues in relative formulae

·     Creating of absolute and mixed references

·     Practical Exercise

·     Creating names range

·     Using names in new formulae

·     Working with the name box

·     Selecting range

·     Names from a selection

·     Pasting names in formulae

·     Selecting names

·     Working with Name Manager

·     Practical Exercise

·     The various logical functions in Excel

·     The If function for calculating values and displaying text

·     Nested If functions

·     VLookUp and IFError functions

·     Practical Exercise

·     Learning about conditional formatting

·     The options for formatting cells

·     Various operations with icon sets

·     Data bars and colour scales

·     Creating and modifying Sparkline

·     Practical Exercise

·     Multi-level drop down validation

·     Restricting value from list only

·     Learning about error messages and cell drop down

·     Practical Exercise

·     Introduction to the various formulae in Excel

·     Sum, Sum IF & Sum IFs

·     Count, Count A, Count IF and Count Blank

·     Network days, Network days International

·     Today & Now function

·     Trim (Eliminating undesirable spaces)

·     Concatenate (Consolidating columns)

·     Practical Exercise

·     Lecture-8 Working with Dynamic table

·     Introduction to dynamic table in Excel

·     Data conversion

·     Table conversion

·     Tables for charts

·     VLOOKUP

·     Practical Exercise

·     Introduction to dynamic table in Excel

·     Data conversion

·     Table conversion

·     Tables for charts

·     VLOOKUP

·     Practical Exercise

·     Sorting in Excel

·     Various types of sorting

·     Alphabetical

·     Numerical

·     Row

·     Multiple column

·     Working with paste special

·     Hyperlinking

·     Using subtotal

·     Practical Exercise

·     The concept of data filtering

·     Understanding compound filter and its creation

·     Removing of filter

·     Using custom filter and multiple value filters

·     Working with wildcards

·     Practical Exercise

·     Creation of Charts in Excel

·     Performing operations in embedded chart

·     Modifying

·     Resizing

·     Dragging of chart

·     Practical Exercise

·     Introduction to the various types of charting techniques

·     Creating titles for charts

·     Axes

·     Learning about data labels

·     Displaying data tables

·     Modifying axes

·     Displaying gridlines and inserting trendlines

·     Textbox insertion in a chart

·     Creating a 2-axis chart

·     Creating combination chart

·     Practical Exercise

·     The concept of Pivot tables in Excel

·     Report filtering

·     Shell creation

·     Working with Pivot for calculations

·     Formatting of reports

·     Dynamic range assigning

·     The slicers

·     Creating of slicers

·     Practical Exercise

·     Data and file security in Excel

·     Protecting row, column, and cell

·     Different safeguarding techniques

·     Practical Exercise

·     Learning about VBA macros in Excel

·     Executing macros in Excel

·     The macro shortcuts

·     Applications

·     The concept of relative reference in macros

·     Practical Exercise

·     In-depth understanding of Visual Basic for Applications

·     The VBA Editor

·     Module insertion and deletion

·     Performing action with Sub

·     Ending Sub if condition not met

·     Practical Exercise

·     Learning about the concepts of workbooks & worksheets in Excel Protection of macro codes

·     Range coding

·     Declaring a variable

·     The concept of Pivot Table in VBA

·     Introduction to arrays

·     User forms

·     Getting to know how to work with databases within Excel

·     Practical Exercise

·     Learning how the If condition works

·     How to apply it in various scenarios

·     Working with multiple Ifs in Macro

·     Practical Exercise

·     Understanding the concept of looping

·     Deploying looping in VBA Macros

·     Practical Exercise

·     Studying about debugging in VBA

·     The various steps of debugging

·     Understanding breakpoints and way to mark it

·     The code for debugging and code commenting

·     Practical Exercise

·     The concept of message box in VBA

·     Learning to create the message box

·     Various types of message boxes

·     The IF condition as related to message boxes

·     Practical Exercise

·     Mastering the various tasks and functions using VBA

·     Understanding data separation

·     Auto filtering

·     Formatting of report

·     Combining multiple sheets into one

·     Merging multiple files together

·     Practical Exercise

·     Introduction to powerful data visualization with Excel Dashboard

·     Loading the data

·     Managing data and linking the data to tables and charts

·     Creating Reports using dashboard features

·     Practical Exercise

·     Learning to create charts in Excel

·     The various charts available

·     The steps to successfully build a chart

·     Personalization of charts

·     Formatting and updating features

·     Various special charts for Excel dashboards

·     Understanding how to choose the right chart for the right data

·     Practical Exercise

·     Creation of Pivot Tables in Excel

·     Learning to change the Pivot Table layout

·     Generating Reports

·     The methodology of grouping and ungrouping of data

·     Practical Exercise

·     Learning to create Dashboards

·     The various rules to follow while creating Dashboards

·     Creation of dynamic dashboards

·     Knowing what is data layout

·     Introduction to thermometer chart and its creation

·     How to use alerts in the Dashboard setup

·     Practical Exercise

·     How to insert a Scroll bar to a data window

·     Concept of Option buttons in a chart

·     Use of combo box drop-down

·     List box control Usage

·     How to use Checkbox Control

·     Practical Exercise

·     Understanding data quality issues in Excel

·     Linking of data

·     Consolidating and merging data

·     Working with dashboards for Excel Pivot Tables

·     Practical Exercise

·     What is statistics?

·     How is this useful

·     What is this course for

·     Practical Exercise

·     Converting data into useful information

·     Collecting the data

·     Understand the data

·     Finding useful information in the data

·     Interpreting the data

·     Visualizing the data

·     Practical Exercise

·     Descriptive statistics

·     Let us understand some terms in statistics

·     Variable

·     Practical Exercise

·     Dot Plots

·     Histogram

·     Stemplots

·     Box and whisker plots

·     Outlier detection from box plots and Box and whisker plots

·     Practical Exercise

·     What is probability?

·     Set & rules of probability

·     Bayes Theorem

·     Practical Exercise

·     Probability Distributions

·     Few Examples

·     Student T- Distribution

·     Sampling Distribution

·     Student t- Distribution

·     Poison distribution

·     Practical Exercise

·     Stratified Sampling

·     Proportionate Sampling

·     Systematic Sampling

·     P – Value

·     Stratified Sampling

·     Practical Exercise

·     Cross Tables

·     Bivariate Analysis

·     Multi variant Analysis

·     Dependence and Independence tests ( Chi-Square )

·     Analysis of Variance

·     Correlation between Nominal variables

·     Practical Exercise

·     Different Sectors Using Data Science

·     The Purpose and Components of Python

·     The Data Analytics Process

·     Exploratory the Data Analysis (EDA)

·     EDA-Quantitative Technique

·     EDA - Graphical Technique

·     The Data Analytics Conclusion or Predictions

·     The Data Analytics Communication

·     The Data Types for Plotting

·     Practical Exercise

·     Introduction to the Statistics

·     About Statistical and Non-statistical Analysis

·     The Major Categories of Statistics

·     About the Statistical Analysis Considerations

·     The Population and Sample

·     What is the Statistical Analysis Process?

·     The Data Distribution

·     Dispersion

·     Practical Exercise

·     The Companies using Python

·     Different Applications where Python is used

·     Discuss Python Scripts on UNIX/Windows

·     Values, Types, Variables

·     Operands and Expressions

·     Conditional Statements

·     Loops

·     Command Line Arguments

·     Writing to the screen

·     Practical Exercise

·     Python files I/O Functions

·     Numbers

·     Strings and related operations

·     Tuples and related operations

·     Lists and related operations

·     Dictionaries and related operations

·     Sets and related operations

·     Practical Exercise

·     Functions

·     Function Parameters

·     Global Variables

·     Variable Scope and Returning Values

·     Lambda Functions

·     Object-Oriented Concepts

·     Standard Libraries

·     Modules Used in Python

·     The Import Statements

·     Module Search Path

·     Package Installation Ways

·     Errors and Exception Handling

·     Handling Multiple Exceptions

·     Practical Exercise

·     Introduction To Statistic

·     Distributions and Hypothesis Tests

·     Distributions of One Variable

·     Hypothesis Testing

·     Typical Analysis Procedure

·     Data Screening and Outliers

·     Normality Check

·     Hypothesis Concept

·     Errors

·     p-Value

·     Sample Size

·     Chi Square

·     ANOVA

·     Practical Exercise

·     Pandas Introduction

·     Series

·     Data Frames

·     CSVs

·     Data from URLs

·     Describing Data with Pandas

·     Selecting and Viewing Data with Pandas

·     Manipulating Data

·     Practical Exercise

·     NumPy Introduction

·     NumPy Data Types and Attributes

·     Creating NumPy Arrays

·     NumPy Random Seed

·     Viewing Arrays and Matrices

·     Manipulating Arrays

·     Standard Deviation and Variance

·     Reshape and Transpose

·     Dot Product vs Element Wise

·     Store Sales

·     Comparison Operators

·     Sorting Arrays

·     Turn Images Into NumPy Arrays

·     Practical Exercise

·     Introduction to the SciPy

·     About the SciPy Sub Package - Integration and Optimization

·     What is SciPy sub package?

·     Know About the SciPy Sub Package - Statistics, Weave & IO

·     Practical Exercise

·     What Is Machine Learning?

·     AI/Machine Learning/Data Science

·     Practical Exercise

·     Machine Learning Framework

·     Types of Machine Learning

·     Types of Data

·     Types of Evaluation

·     Features In Data

·     Modelling - Splitting Data

·     Modelling - Picking the Model

·     Modelling - Tuning

·     Modelling – Comparison

·     Practical Exercise

·     Introducing Our Tools

·     Windows Environment Setup

·     Linux Environment Setup

·     Jupiter Notebook Walkthrough

·     Practical Exercise

·     Matplotlib Introduction

·     Importing And Using Matplotlib

·     Anatomy Of A Matplotlib Figure

·     Scatter Plot And Bar Plot

·     Histograms And Subplots

·     Subplots Option 2

·     Plotting From Pandas DataFrames

·     Customizing Your Plots

·     Saving And Sharing Your Plots

·     Practical Exercise

·     Scikit-learn Introduction

·     Scikit-learn Cheat sheet

·     Typical Scikit-learn Workflow

·     Debugging Warnings In Jupiter

·     Splitting Your Data

·     Clean, Transform, Reduce

·     Convert Data To Numbers

·     Handling Missing Values With Pandas

·     Handling Missing Values With Scikit-learn

·     Choosing The Right Model For Your Data

·     Practical Exercise

·     Types of Regression Algorithms

·     Simple Linear Regression

·     Multiple Linear Regression

·     Logistic Regression,

·     Polynomial Regression

·     Support Vector Regression

·     Ridge Regression

·     Lasso Regression

·     ElasticNet Regression

·     Bayesian Regression

·     Decision Tree Regression

·     Random Forest Regression

·     Practical Exercise

·     Types of Classification Algorithms

·     Logistic Regression/Classification

·     K-Nearest Neighbours

·     Support Vector Machines

·     Kernel Support Vector Machines

·     Naive Bayes

·     Decision Tree Classification

·     Random Forest Classification

·     Practical Exercise

·     K-Means Clustering

·     A Simple Example of Clustering

·     Clustering Categorical Data

·     How to Choose the Number of Clusters

·     Pros and Cons of K-Means Clustering

·     To Standardize or not to Standardize

·     Relationship between Clustering and Regression

·     Market Segmentation with Cluster Analysis

·     Species Segmentation with Cluster Analysis

·     Advanced Statistical Methods - Other Types of Clustering

·     Types of Clustering

·     Dendrogram

·     Heatmaps

·     Practical Exercise

·     The concepts of text-mining

·     Use cases

·     Text Mining Algorithms

·     Quantifying text

·     TF-IDF

·     Beyond TF-IDF

·     Practical Exercise

·     What is time series?

·     Techniques and applications

·     Time series components

·     Moving average

·     Smoothing techniques

·     Exponential smoothing

·     Univariate time series models

·     Multivariate time series analysis

·     Sentiment analysis in Python (Twitter sentiment analysis)

·     Text analysis

·     Rolling Mean For Detecting Temporal Variation

·     Simple Exponential Smoothing (SES)

·     Holt extended simple exponential smoothing

·     Holt Winters

·     Auto Regression Model (AR): Consider Previous Time Steps

·     Implement a Basic ARIMA Model

·     Automated ARIMA & Account for Seasonality (SARIMA)

·     Practical Exercise

·     Introduction to Natural Language Processing (NLP)

·     Introduction to text mining

·     Importance and applications of text mining

·     How NLP works with text mining

·     Writing and reading to word files

·     OS modules

·     Natural Language Toolkit (NLTK) environment & text mining

·     Principal Component Analysis (PCA)

·     Practical Exercise

·     Business Intelligence,

·     Business Analytics,

·     understanding Business Analytics and R

·     History of R

·     The R environment

·     What is Statistical Programming?

·     Practical Exercise

·     Calculating with R

·     Named storage

·     Functions

·     Exact or approximate?

·     R is case-sensitive

·     Listing the objects in the workspace

·     Vectors

·     Vector arithmetic

·     Missing values and other special values

·     Character vectors

·     Factors

·     Matrices and arrays

·     Data frames

·     Dates and times

·     Practical Exercise

·     Boolean algebra

·     Logical operations in R

·     Relational operators

·     Data input and output

·     Changing directories

·     dump() and source()

·     Redirecting R output

·     Saving and retrieving image files

·     Practical Exercise

·     High-level plots

·     Choosing a high-level graphic

·     Low-level graphics functions

·     Practical Exercise

·     Flow control

·     Managing complexity through functions

·     Miscellaneous programming tips

·     Some general programming guidelines

·     Debugging and maintenance

·     Efficient programming

·     Practical Exercise

·     Monte Carlo simulation

·     Generation of pseudorandom numbers

·     Simulation of other random variables

·     Monte Carlo integration

·     Advanced simulation methods

·     Practical Exercise

·     Vectors and matrices in R

·     Matrix arithmetic

·     Eigenvalues and eigenvectors

·     Practical Exercise

·     The golden Section search method

·     Newton–Raphson

·     The Nelder–Mead simplex method

·     Built-in functions

·     Linear programming

·     Practical Exercise

·     Modes and Classes

·     Data Storage in R

·     Testing for Modes and Classes

·     Structure of  R Objects

·     Conversion of Objects

·     Missing Values

·     Working with Missing Values

·     Practical Exercise

·     Reading Vectors and Matrices

·     Data Frames: read. Table

·     Comma- and Tab-Delimited Input Files

·     Fixed-Width Input Files

·     Extracting Data from R Objects

·     Connections

·     Reading Large Data Files

·     Generating Data

·     Working with Sequences

·     Spreadsheets

·     Saving and Loading R Data Objects

·     Working with Binary Files

·     Practical Exercise

·     A Brief Guide to SQL

·     ODBC

·     Using the RODBC Package

·     The DBI Package

·     Accessing a MySQL Database

·     Performing Queries

·     Normalized Tables

·     Getting Data into MySQL

·     More Complex Aggregations

·     Practical Exercise

·     Date

·     The chron Package

·     POSIX Classes

·     Working with Dates

·     Time Intervals

·     Time Sequences

·     Practical Exercise

·     Using Factors

·     Numeric Factors

·     Manipulating Factors

·     Creating Factors from Continuous Variables

·     Factors Based on Dates and Times

·     Interactions

·     Practical Exercise

·     Basics of Subscripting

·     Numeric Subscripts

·     Character Subscripts

·     Logical Subscripts

·     Subscripting Matrices and Arrays

·     Practical Exercise

·     Basics of Character Data

·     Displaying and Concatenating Character

·     Working with Parts of Character Values

·     Regular Expressions in R

·     Basics of Regular Expressions

·     Breaking Apart Character Values

·     Using Regular Expressions in R

·     Substitutions and Tagging

·     Practical Exercise

·     Road Map for Aggregation

·     Mapping a Function to a Vector or List

·     Mapping a function to a matrix or array

·     Mapping a Function Based on Groups

·     Practical Exercise

·     Modifying Data Frame Variables

·     Recoding Variables

·     The recode Function

·     Reshaping Data Frames

·     Practical Exercise

·     Random sampling

·     Probability calculations and combinatory

·     Discrete distributions

·     Continuous distributions

·     The built-in distributions in R

·     Practical Exercise

·     Summary statistics for a single group

·     Graphical display of distributions

·     Histograms

·     Empirical cumulative distribution

·     Q–Q plots

·     Boxplots

·     Summary statistics by groups

·     Graphics for grouped data

·     Histograms

·     Bar-plots

·     Dot-charts

·     Pie-charts

·     Practical Exercise

·     One-sample t test

·     Wilcoxon signed-rank test

·     Two-sample t test

·     Comparison of variances

·     Two-sample Wilcoxon test

·     The paired t test

·     Practical Exercise

·     Simple linear regression

·     Residuals and fitted values

·     Prediction and confidence bands

·     Correlation

·     Pearson correlation

·     Spearman’s ρ

·     Kendall’s τ

·     Practical Exercise

·     Plotting multivariate data

·     Model specification and output

·     Model search

·     Practical Exercise

·     Polynomial regression

·     Regression through the origin

·     Design matrices and dummy variables

·     Linearity over groups

·     Interactions

·     Two-way ANOVA with replication

·     Practical Exercise

·     One-way analysis of variance

·     Pairwise comparisons and multiple testing

·     Relaxing the variance assumption

·     Graphical presentation

·     Bartlett’s test

·     Kruskal–Wallis test

·     Two-way analysis of variance

·     Graphics for repeated measurements

·     The Friedman test

·     The ANOVA table in regression analysis

·     Practical Exercise

·     Single proportions

·     Two independent proportions

·     k proportions, test for trend

·     Practical Exercise

·     The principles of power calculations

·     Power of one-sample and paired t tests

·     Power of two-sample t test

·     Practical Exercise

·     Recoding variables

·     The cut function

·     Manipulating factor levels

·     Working with dates

·     Text Mining

·     Recoding multiple variables

·     Per-group and per-case procedures

·     Time splitting

·     Practical Exercise

·     Generalized linear models

·     Logistic regression on tabular data

·     The analysis of deviance table

·     Connection to test for trend

·     Likelihood profiling

·     Presentation as odds-ratio estimates

·     Practical Exercise

·     Essential concepts

·     Survival objects

·     Kaplan–Meier estimates

·     Practical Exercise

·     Basic ideas

·     The Poisson distribution

·     Survival analysis with constant hazard

·     Models with piecewise constant intensities

·     Practical Exercise

·     Basic usage

·     Finding starting values

·     Case Studies

·     Practical Exercise

·   Lecture-1 SAS Programs

·   Introduction to SAS programs

·   Submitting a SAS program

·   Working with SAS program syntax         

·   Practical Exercise

·   Lecture-2 Create temporary and permanent SAS data sets.

·   Use a DATA step to create a SAS data set from an existing SAS data set.

·   Example: Data Mylib.NewData;

·   Set Mylib.OldData;

·   <other SAS statements>

·   Run;

·   Practical Exercise

·   Lecture-3 Investigate SAS data libraries using base SAS utility procedures.

·   Use a LIBNAME statement to assign a library reference name to a SAS library.

·   Investigate a library programmatically using the CONTENTS procedure.

·   Practical Exercise

·   Lecture-4 Combine SAS data sets.

·   Concatenate data sets.

·   Merge data sets one-to-one.

·   Merge data sets one-to-many.

·   Practical Exercise

·   Lecture-5 Access an Excel workbook.

·   Use the SAS/ACCESS XLSX engine to read an .xlsx file.

·   Practical Exercise

·   Lecture-6 Create and manipulate SAS date values.

·   Explain how SAS stores date and time values.

·   Use SAS date and time formats to specify how the values are displayed.

·   Practical Exercise

·   Lecture-7 Export data to create standard and comma-delimited raw data files.

·   Create a simple raw data file by using the EXPORT procedure as an alternative to the DATA step.

·   Practical Exercise

·   Lecture-8 Control which observations and variables in a SAS data set are processed and output.

·   Use the WHERE statement in the DATA step to select observations to be processed.

·   Subset variables to be output by using the DROP and KEEP statements.

·   Use the DROP= and KEEP= data set options to specify columns to be processed and/or output.

·   Practical Exercise

·   Lecture-9 Sort observations in a SAS data set.

·   Use the SORT Procedure to re-order observations in place or output to a new dataset.

·   Practical Exercise

·   Lecture-10 Conditionally execute SAS statements.

·   Use IF-THEN/ELSE statements to process data conditionally.

·   Use DO and END statements to execute multiple statements conditionally.

·   Practical Exercise

·   Lecture-11 Use assignment statements in the DATA step.

·   Create new variables and assign a value.

·   Assign a new value to an existing variable.

·   Assign the value of an expression to a variable.

·   Assign a constant date value to a variable.

·   Practical Exercise

·   Lecture-12 Modify variable attributes using options and statements in the DATA step.

·   Change the names of variables by using the RENAME= data set option.

·   Use LABEL and FORMAT statements to modify attributes in a DATA step.

·   Define the length of a variable using the LENGTH statement.

·   Practical Exercise

·   Lecture-13 Accumulate sub-totals and totals using DATA step statements.

·   Use the BY statement to aggregate by subgroups.

·   Practical Exercise

·   Lecture-14 Use SAS functions to manipulate character data, numeric data, and SAS date values.

·   Use SAS functions such as SCAN, SUBSTR, TRIM, UPCASE, and LOWCASE to perform tasks such as the tasks shown below.

·   Replace the contents of a character value.

·   Trim trailing blanks from a character value.

·   Search a character value and extract a portion of the value.

·   Convert a character value to upper or lowercase.

·   Use SAS arithmetic, financial, and probability functions to create or modify numeric values by using the INT and ROUND functions.

·   Create SAS date values by using the functions MDY, TODAY,DATE, and TIME.

·   Extract the month, year, and interval from a SAS date value by using the functions YEAR, QTR, MONTH, and DAY.

·   Perform calculations with date and datetime values and time intervals by using the functions INTCK, INTNX, DATDIF and YRDIF.

·   Practical Exercise

·   Lecture-15 Use SAS functions to convert character data to numeric and vice versa.

·   Explain the automatic conversion that SAS uses to convert values between data types.

·   Use the INPUT function to explicitly convert character data values to numeric values.

·   Practical Exercise

·   Lecture-16 Process data using DO LOOPS.

·   Explain how iterative DO loops function.

·   Use DO loops to eliminate redundant code and to perform repetitive calculations.

·   Use conditional DO loops.

·   Use nested DO loops.

·   Practical Exercise

·   Lecture-17 Validate and clean data.

·   Use PROC FREQ to list unique values, with the nlevel option to show the number of distinct values, with the order=freq to check for duplicate or missing values.

·   Use PROC PRINT with the WHERE statement to display observations with invalid values.

·   Use PROC MEAN to validate the range of numeric variables.

·   Use PROC UNIVARIATE to display extreme observations and missing values and with the ID statement to display the value of identifying variable

·   Practical Exercise

·   Lecture-18 Generate list reports using the PRINT procedure.

·   Modify the default behavior of PROC PRINT by adding statements and options such as

·   Use the VAR statement to select and order variables.

·   Calculate totals with a SUM statement.

·   Select observations with a WHERE statement.

·   Use the ID statement to identify observations.

·   Use the BY statement to process groups.

·   Practical Exercise

·   Lecture-19 Generate summary reports and frequency tables using base SAS procedures.

·   Produce one-way and two-way frequency tables with the FREQ procedure.

·   Enhance frequency tables with options.

·   Use PROC FREQ to validate data in a SAS data set.

·   Calculate summary statistics and multilevel summaries using the MEANS procedure

·   Enhance summary tables with options.

·   Identify extreme and missing values with the UNIVARIATE procedure.

·   Practical Exercise

·   Lecture-20 Enhance reports through the use of user-defined formats, titles, footnotes and SAS System reporting.

·   Use the LABEL statement to define descriptive column headings.

·   Control the use of column headings with the LABEL and SPLIT=options in Proc Print output.

·   Practical Exercise

·   Lecture-21 Generate reports using ODS statements.

·   Identify the Output Delivery System destinations.

·   Create HTML, PDF, RTF, and files with ODS statements.

·   Use the STYLE=option to specify a style template.

·   Create files that can be viewed in Microsoft Excel.

·   Practical Exercise

·   Lecture-22 Identify and resolve programming logic errors.

·   Use the PUTLOG Statement in the Data Step to help identify logic errors.

·   Use PUTLOG to write the value of a variable, formatted values, or to write values of all variables.

·   Use PUTLOG with Conditional logic.

·   Use temporary variables N and ERROR to debug a DATA step.

·   Practical Exercise

·   Lecture-23 Recognize and correct syntax errors.

·   Identify the characteristics of SAS statements.

·   Define SAS syntax rules including the typical types of syntax errors such as misspelled keywords, unmatched quotation marks, missing semicolons, and invalid options.

·   Use the log to help diagnose syntax errors in a given program.

·   Practical Exercise

·   Lecture-24 Examine and resolve data errors.

·   Given a SAS program, use the log to determine the reason for a data error.

·   Practical Exercise

·   Lecture-1 Generate detail reports by working with a single table, joining tables, or using set operators 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.

·   Practical Exercise

·   Lecture-2 Generate summary reports by working with a single table, joining tables, or using set operators 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.

·   Practical Exercise

·   Lecture-3 Construct sub-queries and in-line views within an SQL procedure step.

·   Subset data by using non-correlated subqueries.

·   Reference an in-line view with other views or tables (multiple tables).

·   Practical Exercise

·   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.

·   Practical Exercise

·   Lecture-5 Create and use user-defined and automatic macro variables within the SAS Macro Language.

·   Define and use 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

·   Practical Exercise

·   Lecture-6 Automate programs by defining and calling macros using the SAS Macro Language.

·   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.

·   Practical Exercise

·   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.

·   Practical Exercise

·   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.

·   Practical Exercise

·   Lecture-9 Create 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.

·   Practical Exercise

·   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.

·   Practical Exercise

·   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

·   Practical Exercise

·   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=.

·   Practical Exercise

·   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

·   Practical Exercise

·   Lecture-1 Introduction

·   Lecture-2 Analysis Methods and SAS/ETS Software

·   Options

·   How SAS/ETS Software Procedures Interrelate

·   Practical Exercise

·   Lecture-3 Simple Models: Regression

·   Linear Regression

·   Highly Regular Seasonality

·   Regression with Transformed Data

·   Practical Exercise

·   Lecture-4 Introduction

·   Terminology and Notation

·   Statistical Background

·   Practical Exercise

·   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

·   Practical Exercise

·   Lecture-6 Introduction

·   Statistical Background

·   Terminology and Notation

·   Practical Exercise

·   Lecture-7 Prediction

·   One-Step-Ahead Predictions

·   Future Predictions

·   Practical Exercise

·   Lecture-8 Model Identification

·   Stationarity and Invertibility

·   Time Series Identification

·   Chi-Squared Check of Residuals

·   Summary of Model Identification

·   Practical Exercise

·   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

·   Practical Exercise

·   Lecture-10 Seasonal Time Series

·   Introduction to Seasonal Modeling

·   Model Identification

·   Practical Exercise

·   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

·   Practical Exercise

·   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

·   Practical Exercise

·   Lecture-13 Further Examples

·   North Carolina Retail Sales

·   Construction Series Revisited

·   Milk Scare (Intervention)

·   Terrorist Attack

·   Practical Exercise

·   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

·   Practical Exercise

·   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

·   Practical Exercise

·   Lecture-16 Introduction

·   Some Simple Univariate Examples

·   A Simple Multivariate Example

·   Equivalence of Statespace and Vector ARMA Models

·   Practical Exercise

·   Lecture-17 More Examples

·   Some Univariate Examples

·   ARMA (1, 1) of Dimension

·   Practical Exercise

·   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

·   Practical Exercise

·   Lecture-20 Introduction Data Mining and Forecasting

·   Forecasting Data Model