*Lifetime Access.
*Course completion certificate, Certification documents and materials, interview questions and job assistance included

SAP HANA SP09 Duration of Course:

30+ hours

SAP HANA SP09 Topics Covered are:

Unit 1: Introduction to SAP HANA

1. Introduction to SAP HANA
2. SAP In-Memory Strategy
3. HANA compare to BWA

Unit 2: Look & Feel

1. In-Memory Computing Studio
2. Administration view
3. Navigator View
4. System Monitor
5. Information Modeler

Unit 3: Architecture

1. Architecture Overview
2. IMCE and Surroundings
3. Row Store
4. Column Store
5. Loading data into HANA
6. Data Modelling
7. Reporting
8. Persistent Layer
9. Backup & Recovery

Unit 4: Data Provisioning

1. Method 1 – Data Provisioning using FLAT FILES
2. Method 2 – Data provisioning using BODS 4.2 (ETL Based Approach – Building AGILE Data Marts)
   a. Features of SAP Data Services solution for SAP HANA
   b. Process of loading data from ECC to SAP HANA using the ETL method
3. Method 3 – Data provisioning using SLT
   a. SAP Landscape Replication server for HANA
   b. Key benefits of SLT replication server
   c. Key benefits of Trigger-Based Approach
   d. Architecture for SAP source replication
   e. Architecture for Non-SAP source replication
   f. Configuration and monitoring Dashboard
   g. Creating new Configuration for SAP Sources
   h. Creating New Configuration for Non-SAP sources
   i. Result of Creating new Configuration
   j. Launching Data provisioning UI in HANA studio
   k. Start Load/Replication
   l. Stop/Suspend replication
   m. Status Monitoring in HANA Studio
   n. SLT based transformation Concept
   o. Advanced replication settings
   p. Change of table structuring and partitioning
   q. Filtering and selective data replication
4. Method 4 – Data provisioning using Direct Extractor Connection (DCX)
   a. Using SAP provided Business Content Extractor
   b. ABAP Data Flows for Table and Pool clusters
5. Method 5 – SAP HANA Smart Data Access
6. Method 6 – Remote DATA Sync
   a. Smart Data Streaming

Unit 5 : Modeling

1. Purpose of Information Modeler
2. Levels of Modeling in SAP HANA
3. Attribute Views
4. Analytic Views
5. Calculation Views
6. Explaining Predictive Modeling
7. Discovering SAP HANA Live
8. Creating Advanced Calculation Views using GUI and SCRIPT methods
9. Creating Attribute Views, Analytical Views, Calculation Views for FI scenarios, COPA scenarios, Sales Scenarios, Purchasing Scenarios and Marketing Scenarios
10. Creating Calculation Views with Dimension, Cube and STAR-Join
11. Creating Decision Tables and Analytic Privileges
12. Using Hierarchies (Level Based and Parent-Child Hierarchies)
13. Creating Restricted and Calculated Measures
14. Defining and using Filter Operations
15. Using Variables, input parameters
16. Explaining new aggregation function for measures
17. SAP HANA SQL Introduction
18. SQL Script and Procedures
19. Using Currency Conversions
20. Creating Hyperlinks
21. Persistency Considerations
22. SAP HANA Engine Overview
23. Choosing Views for HANA
24. Using SAP HANA Information Composer for Modeling
25. Processing Information Models
26. Validating Models
27. Comparing Versions of Information Objects
28. Checking Model References
29. Generate Auto Documentation
30. Understand Virtual Data Model
31. Discovering and consuming HANA Live views
32. Building a Virtual Data Model with CDS Views
33. Connecting Tables
    Joins (Inner, Left Outer, Right Outer, Full Outer, Text, Referential and Union)
34. Managing Modelling Content
    a. Manage Schemas b. Import and Export data Models c. Copy Information Objects

Unit 6: Reporting

1. HANA, Reporting Layer
2. Connectivity options
3. SAP Business Objects BI 4.1
4. Designing Complex Universes in IDT based on HANA Tables and HANA Views
5. WEBi 4.0 on HANA
6. Crystal Report for Enterprise with HANA
7. Designing the Dashboards using Query Browser on HANA Universes using Dashboard Design 4.0
8. SAP Business Objects BI 4.1 Explorer
9. Designing Information Space based on SAP HANA Information Model using BO Explorer 4.1
10. Exploring the Data using BO 4.1 explorer based on Information Spaces created on HANA
11. Creating Analysis Views using Analysis edition for OLAP (HANA OLAP connection)
12. Analysis edition for Microsoft Excel, Microsoft Powerpoint
13. SAP Visual Intelligence on HANA
14. Crystal Reports via ODBC/JDBC Connections
15. Others & MS Excel 2010

Unit 7: User Management

1. Understand and Creation of Users
2. Creation of Roles and Privileges
3. Creation of Role Hierarchy
4. Generating SAP HANA Live Privileges
5. Assignment of Users to Roles
6. Authentication

Unit 8: Security and Authorizations

1. User Management and Security
2. Types of Privileges
3. Template Roles
4. Administrative

Unit 9: Concepts of SAP BW 7.4 ON HANA

1. BW 7.4 powered by SAP HANA
2. In-memory optimized Infocubes
3. In-memory optimized DSO’s
4. Migration concepts of BW 7.0 on traditional Database to BW 7.4 on SAP HANA
5. Migrating standard Infocubes to In memory optimized Infocubes using migration Tool
6. Migrating standard DSO’s to In-memory optimized DSO’s using migration Tool

Unit 10: Text Search and Analysis

1. Implementing Full Text Search and Text analysis
2. Defining Data Types and Full Text Indexes
3. Using Full Text Search
4. Developing Predictive Models

Unit 11: SP12 new Features and Functionality

1. SAP HANA Smart Data Access (New and Changed)
2. SAP HANA Predictive Analysis Library
3. SAP HANA Graph
4. SAP HANA Client Interfaces
5. SAP HANA XS Advanced Development
6. SAP HANA Modeling (New and Changed)
7. SAP Web IDE for SAP HANA and SAP HANA Runtime Tools
8. SAP HANA Interactive Education (SHINE) for XS Advanced

Go to Courses Page – https://knowasap.com/courses/

In this R Programming course, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities. Leverage the power of R by completing this R online course today!

R programming along with a substantial knowledge of statistics can help candidates to have a great career in data Analytics. R is also an widely used tool in many big firms like top Banks, IT, Retail, Healthcare, Pharma, Supply chain and logistics firms. Analyzing large data-sets can be done in a shorter period with the help of R programming. There is a huge shortage in the market for professionals with skills in R programming which makes it more interesting to pursue. Since R is a free software it is being widely used which creates a lot of opportunities for professional who are looking to pursue a career in R Programming.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

*Lifetime Access.
*Course completion certificate, Certification documents and materials, interview questions and job assistance included.

 Duration of Course:

40+ hours

 Topics Covered are:

Module 1: Essential to R programming

1: An Introduction to R

• History of S and R
• Introduction to R
• The R environment
• What is Statistical Programming?
• Why use a command line?
• Your first R session

2: Introduction to the R language

• Starting and quitting R
  • Recording your work
• Basic features of R
  • Calculating with R
  • Named storage
  • Functions
  • Exact or approximate?
  • R is case-sensitive
  • Listing the objects in the workspace
  • Vectors
  • Extracting elements from vectors
  • Vector arithmetic
  • Simple patterned vectors
  • Missing values and other special values
  • Character vectors
  • Factors
  • More on extracting elements from vectors
  • Matrices and arrays
  • Data frames
  • Dates and times
• Built-in functions and online help
  • Built-in examples
  • Finding help when you don’t know the function name
  • Built-in graphics functions
  • Additional elementary built-in functions
• Logical vectors and relational operators
  • 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
  • Data frames and the read.table function

3: Programming statistical graphics

• High-level plots
  • Bar charts and dot charts
  • Pie charts
  • Histograms
  • Box plots
  • Scatterplots
  • QQ plots
• Choosing a high-level graphic
• Low-level graphics functions
  • The plotting region and margins
  • Adding to plots
  • Setting graphical parameters

4: Programming with R

• Flow control
  • The for() loop
  • The if() statement
  • The while() loop
  • Newton’s method for root finding
  • The repeat loop, and the break and next statements
• Managing complexity through functions
  • What are functions?
  • Scope of variables
• Miscellaneous programming tips
  • Using fix()
  • Documentation using#
• Some general programming guidelines
  • Top-down design
• Debugging and maintenance
  • Recognizing that a bug exists
  • Make the bug reproducible
  • Identify the cause of the bug
  • Fixing errors and testing
  • Look for similar errors elsewhere
  • The browser() and debug()functions
• Efficient programming
  • Learn your tools
  • Use efficient algorithms
  • Measure the time your program takes
  • Be willing to use different tools
  • Optimize with care

5: Simulation

• Monte Carlo simulation
• Generation of pseudorandom numbers
• Simulation of other random variables
  • Bernoulli random variables
  • Binomial random variables
  • Poisson random variables
  • Exponential random numbers
  • Normal random variables
• Monte Carlo integration
• Advanced simulation methods
  • Rejection sampling
  • Importance sampling

6: Computational linear algebra

• Vectors and matrices in R
  • Constructing matrix objects
  • Accessing matrix elements; row and column names
  • Matrix properties
  • Triangular matrices
  • Matrix arithmetic
• Matrix multiplication and inversion
  • Matrix inversion
  • The LU decomposition
  • Matrix inversion in R
  • Solving linear systems
• Eigenvalues and eigenvectors
  • Advanced topics
  • The singular value decomposition of a matrix
  • The Choleski decomposition of a positive definite matrix
  • The QR decomposition of a matrix
  • The condition number of a matrix
  • Outer products
  • Kronecker products
  • apply()

7: Numerical optimization

• The golden section search method
• Newton–Raphson
• The Nelder–Mead simplex method
• Built-in functions
• Linear programming
  • Solving linear programming problems in R
  • Maximization and other kinds of constraints
  • Special situations
  • Unrestricted variables
  • Integer programming
  • Alternatives to lp()
  • Quadratic programming

Module 2: Data Manipulation Techniques using R programming

1: Data in R

• 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

2: Reading and Writing Data

• 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
  • Sequences
  • Random Numbers
  • Permutations
  • Random Permutations
  • Enumerating All Permutations
• Working with Sequences
• Spreadsheets
  • The RODBC Package on Windows
  • The gdata Package (All Platforms)
• Saving and Loading R Data Objects
• Working with Binary Files
• Writing R Objects to Files in ASCII Format
  • The write Function
  • The write.table function
  • Reading Data from Other Programs

 3: R and Databases

• A Brief Guide to SQL
  • Navigation Commands
  • Basics of SQL
  • Aggregation
  • Joining Two Databases
  • Subqueries
  • Modifying Database Records
• ODBC
• Using the RODBC Package
• The DBI Package
• Accessing a MySQL Database
• Performing Queries
• Normalized Tables
• Getting Data into MySQL
• More Complex Aggregations

4: Dates

• as.Date
• The chron Package
• POSIX Classes
• Working with Dates
• Time Intervals
• Time Sequences

5: Factors

• Using Factors
• Numeric Factors
• Manipulating Factors
• Creating Factors from Continuous Variables
• Factors Based on Dates and Times
• Interactions

6: Subscripting

• Basics of Subscripting
• Numeric Subscripts
• Character Subscripts
• Logical Subscripts
• Subscripting Matrices and Arrays
• Specialized Functions for Matrices
• Lists
• Subscripting Data Frames

 7: Character Manipulation

• 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

 8: Data Aggregation

• Table
• 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
• There shape Package
• Loops in R

9:  Reshaping Data

• Modifying Data Frame Variables 
• Recoding Variables
• The recode Function
• Reshaping Data Frames
• The reshape Package
• Combining Data Frames
• Under the Hood of merge

Module 3: Statistical Applications using R programming

1:  Basics

• First steps
  • An overgrown calculator
  • Assignments
  • Vectorized arithmetic
  • Procedures
  • Graphics
• R language essentials
  • Expressions and objects
  • Functions and arguments
  • Vectors
  • Quoting and escape sequences
  • Missing values
  • Functions that create vectors
  • Matrices and arrays
  • Factors
  • Lists
  • Data frames
  • Indexing
  • Conditional selection
  • Indexing of data frames
  • Grouped data and data frames
  • Implicit loops
  • Sorting

 2: The R environment

• Session management
  • The workspace
  • Textual output
  • 3 Scripting
  • Getting help
  • Packages
  • Built-in data
  • attach and detach
  • subset, transform, and within
• The graphics subsystem
  • Plot layout
  • Building a plot from pieces
  • Using par
  • Combining plots
• R programming
  • Flow control
  • Classes and generic functions
• Data entry
  • Reading from a text file
  • Further details on read.table
  • The data editor
  • Interfacing to other programs

 3: Probability and distributions

• Random sampling
• Probability calculations and combinatorics
• Discrete distributions
• Continuous distributions
• The built-in distributions in R
  • Densities
  • Cumulative distribution functions
  • Quantiles
  • Random numbers

 4:  Descriptive statistics and graphics

• 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
  • Parallel boxplots
  • Stripcharts
• Tables
  • Generating tables
  • Marginal tables and relative frequency
• Graphical display of tables
  • Barplots
  • Dotcharts
  • Piecharts

 5: One- and two-sample tests

• One-sample t test
• Wilcoxon signed-rank test
• Two-sample t test
• Comparison of variances
• Two-sample Wilcoxon test
• The paired t test
• The matched-pairs Wilcoxon test

 6: Regression and correlation

• Simple linear regression
• Residuals and fitted values
• Prediction and confidence bands
• Correlation
• Pearson correlation
• Spearman’s ρ
• Kendall’s τ

 7: Analysis of variance and the Kruskal–Wallis test

• 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

 8: Tabular data

• Single proportions
• Two independent proportions
• k proportions, test for trend
• r × c tables

 9: Power and the computation of sample size

• The principles of power calculations
  • Power of one-sample and paired t tests
  • Power of two-sample t test
  • Approximate methods
  • Power of comparisons of proportions
• Two-sample problems
• One-sample problems and paired tests
• Comparison of proportions

 10: Advanced data handling

• Recoding variables
  • The cut function
  • Manipulating factor levels
  • Working with dates
• Recoding multiple variables
• Conditional calculations
• Combining and restructuring data frames
  • Appending frames
  • Merging data frames
  • Reshaping data frames
  • Per-group and per-case procedures
  • Time splitting

 11: Multiple Regression

• Plotting multivariate data
• Model specification and output
• Model search

 12: Linear models

• Polynomial regression
• Regression through the origin
• Design matrices and dummy variables
• Linearity over groups
• Interactions
• Two-way ANOVA with replication
• Analysis of covariance
  • Graphical description
  • Comparison of regression lines
• Diagnostics

 13: Logistic regression

• 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
• Logistic regression using raw data
• Prediction
• Model checking

 14: Survival analysis

• Essential concepts
• Survival objects
• Kaplan–Meier estimates
• The log-rank test
• The Cox proportional hazards model

 15:  Rates and Poisson regression

• Basic ideas
  • The Poisson distribution
  • Survival analysis with constant hazard
• Fitting Poisson models
• Computing rates
• Models with piecewise constant intensities

 16: Nonlinear curve fitting

• Basic usage
• Finding starting values
• Self-starting models
• Profiling
• Finer control of the fitting algorithm