Description

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.

Duration

Not Mentioned

Career Option

• Developer
• Software Engineer

Opportunities

Not Mentioned

Major Companies Using Data Analytics

• Cisco
• Lumeris Austin
• GAP
• INC
• Adobe

Course Content

TOPICS

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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 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

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

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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

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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

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