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SAP Certified Associate – SAP Analytics Cloud
A new solution in the SAP portfolio designed to address the demands of cloud-based data visualization is called SAP Analytics Cloud (SAC). It is supplied as a SaaS-based, all-in-one product. It addresses the requirements of predictive analytics, budget planning, and data visualization. The production of data reports is its primary purpose. Spreadsheets, on-premise databases, cloud databases, or a mix of all three can be used to obtain data using SAP Analytics cloud. By offering insightful information, SAP Analytics Cloud transforms transactional system raw data into accurate data.
Overview
Software as a service (SaaS) for business intelligence (BI), planning, and predictive analytics is provided by SAP Analytics Cloud. It maximizes data-driven decision making by offering a uniform and secure public cloud experience, built natively on SAP BTP. SAP Analytics Cloud is a software-as-a-service that offers a platform of features and capabilities for business planning, data analysis, and data visualization. It gives developers, data scientists, and business analysts a central location to carry out their everyday responsibilities related to financial planning, application development, and business intelligence (BI). SAP Business Technology Platform, formerly known as SAP Cloud Platform, powers it. SAC is simply a cloud-based HANA platform solution for reporting and visualization.
What you will Learn in this SAP Certified Associate – SAP Analytics Cloud course?
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Prepare for SAP Analytics Cloud Certification exam
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Become a top SAP Analytics Cloud Consultant
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Explain the role of SAP Analytics Cloud and how it fits within the SAP portfolio
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Understand the Analytic Capabilities of SAC – BI, Planning, Predictive, Application Design, Embedding
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Learn SAC Core Capabilities – Data Connectivity, Wrangling, Modeling, Administration Auditing, Visualizations, Collaboration, Mobile, APIs
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Explain the three Fundamental Components of SAP Analytics Cloud – Data, Models, Stories
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Understand SAC Modelling – deletes the blanks, duplicates, format, and data issues then prepares data for Reporting and Visualization
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Learn Data Modelling in SAC – convert any source data and present it in dimensions for analysis
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Learn Predictive Analysis in SAC – for forecasting the data by analyzing the historic data
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Build Stories – where you can explore and visualize your data for reporting, planning, and analysis
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Create Planning Data Dashboards in SAC
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Define users, teams, roles, and security in SAP Analytics CloudApply SAC digital boardroom skills to create visualizations
Who should go for SAP Certified Associate – SAP Analytics Cloud course?
- SAP Analytics Consultants
- SAP Analytics Cloud Development Application Developers
- Beginners & newbies aspiring for a career in SAP Analytics Cloud
- SAP Analytics Cloud (SAC) and SAP BO Specialists
- Business Processes Consultants – SAP Analytics
- SAP Analytics Cloud Developers – Data Modeling/Wrangling
- Business Analysts & Consultants
- Anyone interested to learn SAC and become SAC Consultant / Power user
- SAP Analytics Cloud Development Program/Project Managers
- Associate Principal – SAC
- Solution & Data Architects
- Technical Leads
- SAP Analytics Cloud Specialists
- Package Consultants – SAP BW HANA Analytics
- SAP HANA/SAC Consultants – BW/Analytics
- SAP Analytics Cloud Engineers
- SAP Analytics Cloud Development Application Leads
- SAP BW/HANA/SAC- Associates
Our Package
UNIT 1: Overview and Positioning
➢ Analytics Cloud Architecture Overview
➢ SAC vs other BI tools
➢ Benefits and core functionalities of SAC
➢ Cloud vs On-Premise vs Hybrid
➢ Analytics Cloud Client tools and Importance
UNIT 2: Modeling
➢ What is MODEL
➢ Components of MODEL
➢ Working with Dimension and Classification
➢ Configuring Geo-Dimension
➢ Working with Measures
➢ Working with Transformations
➢ Working with Variables
➢ Data Blending
UNIT 3: Business Intelligence
➢ Designing SAC Stories
➢ Working with Custom Templates
➢ Working with Standard Templates
➢ Working with Canvas-Responsive and Grid modes
➢ Working with Designer (Builder panel, Styling Panel)
➢ Filters in SAC
➢ Query level filters
➢ Story level filters
➢ Page-level filters
➢ Widget level filters
➢ Advanced Filters
➢ Linked Analysis
➢ Hyperlinking
➢ Conditional Formatting
➢ Customizing Measures
➢ Customizing Dimensions
➢ Data blending
➢ Working with Chart widget
➢ Working with a Table widget
➢ Working with Geo Map widget
➢ R language basics
➢ Generating R based Stories
➢ Import data connection from Google drive
UNIT 4: Augmented Analytics
➢ What is Augmented Analytics
➢ Smart Search
➢ Smart Discovery
➢ Smart Insights
UNIT 5: Planning with SAC
➢ How to develop planning data models in SAC
➢ Understand measures, accounts, hierarchies, currency conversion
➢ Manage versions of planning
➢ Create planning stories
➢ Planning functions – variance, forecast, version management
➢ What if analysis
➢ Allocations
➢ Spreading and Distributions
➢ Value Driver Tree- VDT
➢ Data actions and insights
➢ Collaboration
UNIT 6: Analytics Designer
➢ What is Analytics Designer
➢ Difference between SAC Stories vs Analytics Designer
➢ Analytics Designer overview and walkthrough
➢ Outline, Designer, Error, and reference panels
➢ Design mode vs Run mode vs View mode
➢ Designing basic Analytic application
➢ working with Container widgets
➢ Implementing filters
➢ working with Drop-down, Radio button, Checkbox components
➢ working with script variables
➢ working with script objects
➢ Configuring and implementing Dynamic Visibility
➢ Implementing Hyper linking and Explorer option
➢ Using APIs to integration with Smart discovery, smart insights
➢ Embedding the WebPages inside the Analytic designer
➢ Embedding SAC app inside other WebPages
UNIT 7: Predictive Scenario
➢ Predictive scenario overview
➢ SAC Stories vs SAC Applications vs SAC Predictive
➢ Working with Datasets, Variables
➢ Understand Regression
➢ Understand Logistic Regression, RoC Curves, AUC Curve
➢ Model performance and Confusion Matrix
➢ Profit Simulation for Classification
➢ Implementing Classification Precative Model
➢ Implementing Regression Predictive Model
➢ Residual and MAPE Concept in Regression
➢ Trend, Cycle, Residual and Variations concepts
➢ Implementing a Time series Predictive Model
➢ Generating predictive stories
UNIT 8: Administration
➢ SAC Administration Overview
➢ Roles (Standard vs Custom)
➢ Team
➢ Users
➢ Working with data loading and scheduling
➢ Cloud connector
➢ Analytics Cloud Agent
UNIT 9: SAC Roadmap and Certification
Upcoming Batch
April 20th (Weekends)
FRI & SAT (4 Weeks)
08:30 PM to 01:00 AM (CDT)
April 18th (Weekdays)
MON – FRI (18 Days)
10:00 AM to 12:00 PM (CDT)
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