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Apache Data Analytics is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system. Data Analytics is often used in place of traditional message brokers like JMS and AMQP because of its higher throughput, reliability and replication.

Data Analytics works in combination with Apache Storm, Apache HBase and Apache Spark for real-time analysis and rendering of streaming data. Data Analyticscan message geospatial data from a fleet of long-haul trucks or sensor data from heating and cooling equipment in office buildings. Whatever the industry or use case, Data Analytics brokers massive message streams for low-latency analysis in Enterprise Apache Hadoop.

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**TOPICS****1. Data Analytics for Analyst Programmers****Data Analytics/BASE****Introduction**- Introduction to Data Analytics
- Introduction to Analytics
- Editor File
- Log File
- Output File
- Result, Explorer windows
- Permanent Data Analytics datasets
- Environments where Data Analytics runs
- Data Analytics Libraries
- Data Analytics Data types
- Step boundaries and run-group processing

**Advanced Input Features**- Read Data and Raw Data files
- INFILE Statement
- Input Statement
- DATALINES, CARDS
- List INPUT
- Column INPUT
- Formatted INPUT
- Mixed INPUT
- :(colon) and & (Ampersand) Modifiers
- double trailing @@
- Black box Data Analytics programming
- / and # pointers,Single trailing @, Character Pointer
- MISSOVER,TRUNCOVER,SCANOVER
- DLM,DSD,Firstobs,Obs

**Data Step functions**- Character functions (left,right,tranwrd,translate ,index, indexw, indexc,find,trim, catx,catt, cat,cats,compress, scan,substr, upcase,lowcase,strip,etc.
- Numaricfunctions (int,ceil,floor,Round, abs, Min,Max, Sum,mean, lag, dif).
- Date function (Today, Datetime, Time, Timepart,Datepart,Day,Month, Year, Qtr,MDY etc).
- Data Analytics System options

**Data Analytics Structures and Flow**- Data step overview
- LENGTH
- FORMATS,IMFORMATS, LABLES
- Titles and Footnotes
- reading existing Data Analytics datasets with SET
- Assignment statements
- RENAME
- DROP and KEEP
- Subsetting observations
- Subsetting IF statement
- If, If-then, Else-if, If then Do.
- WHERE statement

**Data Analytics Do-loops**- Syntax
- How to create Do loops
- Conditional Do loops(DO until,DO while, by clause).
- Nesting Do loops
- Arrays

**Data Analytics Executable Statements**- Accumulating totals
- RETAIN and SUM
- SUM statement
- SELECT statement
- Deleting observations
- Numeric-character conversion
- OUTPUT,PUT

**Procedures**- Proc Print
- Proc Contents
- Proc Sort
- Proc Means
- Proc Freq
- Proc Report
- Proc Tabulate
- Proc Printto
- Proc Dataset
- Proc Compare
- Proc Transpose
- Proc Format (input, put function, creating permanent formats, informats)
- Proc Import
- Proc Export
- Proc Append
- Proc Summary

**Merging Data Analytics Datasets**- Syntax
- one to one merges
- Match merging
- Multiple OBS with the same BY variable
- Merging with identical variable names
- Merging without a common variable
- Update statement

**Data Analytics/ODS**- ODS/Trace/Select/Exclude
- ODS/HTML FILE
- ODS/PDF FILE
- ODS/RTF FILE
- PROC PRINT WITH STYLE OPTION
- PROC REPORT WITH STYLE OPTION
- PROC TABULATE WITH STYLE OPTION

**Data Analytics/GRAPH**- Proc PLOT
- Proc GPLOT
- Proc CHART
- Proc GCHART

**Data Analytics/SQL**- SQL DDL Statements (CREATE,ALTER,DROP)
- SQL DML Statements (INSERT,UPDATE,SELECT,DELETE)
- SQL Filter Clauses (WHERE, GROUP BY, HAVING, ORDER BY)
- SQL Horizontal Joins (INNER, LEFT,RIGHT, FULL, FULL with condition, Cartesian product)
- SQL Vertical Joins (UNION,UNION ALL,INTERSECT,EXCEPT)

**Data Analytics/MACROS**- An Introduction to Data Analytics Macros
- Functions of the Data Analytics macro processor
- Macro processor flow
- Macro and macro variable
- Defining and using a macro
- Creating macro variables- 3 ways
- Local and global Macro
- Automatic macro variables
- Avoid macro errors
- Positional macro parameters
- Keyword macro parameters
- Call Symput and symget
- System options for debugging macro

**Data Analytics/STATISTICS**- Proc UNIVARIATE
- Proc MEANS
- Proc FREQ(Chi-Square)
- Proc GLM
- Proc RANK
- Proc ANOVA
- Proc REG
- Proc LOGISTIC(logistic Regression)
- Proc TTEST(Paired)
- Proc CORR (correlation)

**Data Analytics/ACCESS**- How to connect with data server
- libname code
- Sql Code

**Projects**- 3 Projects have to be completed

**2. Advance Analytics Using Data Analytics****Introduction to Statistics/analytics**- Need for analytics
- Analytics use in different industries
- Challenges in adoption of analytics
- Overview of Course Contents

**Data understanding : Data types (nominal, Ordinal, Interval and ratio)****Parametric & Non-Parametric test****Estimation**

**Descriptive statistics**- Tabular & Graphical Method, Summary statistics,Means,Freq, Correlation,Rank etc

**Linear Regression**- fit a multiple linear regression model using the REG and GLM procedures
- Analyze the output of the REG procedure for multiple linear regression models
- Use the REG procedure to perform model selection
- Assess the validity of a given regression model through the use of diagnostic and residual analysis

**Logistic Regression**- Perform logistic regression with the LOGISTIC procedure
- Optimize model performance through input selection
- Interpret the output of the LOGISTIC procedure
- Score new data sets using the LOGISTIC and SCORE procedures

**Introduction to some statistical terminologies and inferences**- Population, Sample and random variables
- Point and interval Estimations
- Probability
- Discrete/Continuous probability Distributions

**Hypothesis Testing****T-Test**- One-Tailed, Two-Tailed, Z-Test

**Anova**- Verify the assumptions of ANOVA
- Analyze differences between population means using the GLM and TTEST procedures
- perform ANOVA post hoc test to evaluate treatment effects
- Detect and analyze interactions between factors

**CHI-SQUARE****Prepare Inputs for Predictive Model Performance**- Identify potential problems with input data
- use the DATA step to maipulate data with loops, arrays,conditional statements and functions
- Reduce the number of categorical levels in a predictive model
- Screen variables for irrelevance using the CORR procedure
- Screen variables for non-linearity using empirical logit plots

**Measure Model performance**- Apply the principles of honest assessment to model performance measurement
- Assess classifier performance using the confusion matrix
- Model selection and validation using training and validation data
- Create and interpret graphs for model comparison and selection
- Establish effective decision cut-off values for scoring

**Cluster Analysis : Case study on cluster Analysis****factor Analysis : Case Study on factor Analysis**

**3. SQL FOR ANALYST****SQL SERVER 2008****Introduction to SQL Server Concepts**

**Introduction to DBMS & RDBMS Concepts**- SQL Introduction
- Sql Commands

**Data Types****Data Definition Languages**- Create table command
- Alter table command
- Truncate table command
- Drop table command

**Constraints**- Not Null
- Unique Key
- Primary key
- Foreign Key
- Check
- Default

**Data Manipulation Language**- Select Command
- Insert Command
- Update Command
- Delete command

**Filter Clause**- WHERE Clause
- GROUP BY Clause
- Having Clause
- Order by clause

**Operators**- Arithmetic Operators
- Comparison Operators
- Logical Operators

**Range Operators**- IN/NOT IN
- Between

**Set Operators**- Union
- Union All
- Intersect
- Except

**SQL Functions**- Aggregate Function
- Date Function
- String Function

**Identity Properties****Column and table Alias Joins**- Simple join:
- Non equi join
- Equi join
- Self join
- inner join
- Outer join
- Left Outer join
- Right Outer join
- Full Outer join
- Cross join

**Different Types of queries**- Simple Queries
- Sub Queries
- Nested Sub Queries
- Correlated Sub Queries
- Temporary tables
- Common Table Expressions(CTEs)
- Derived Tables
- Scalar Expressions

**INDEXES****VIEWS****Stored Procedures****Triggers**- DDL,DML,LOGON,Triggers

**Cursors****Search expressions like****Dealing with Nulls views and Derived Tables****Exercises and Real Time examples**