This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.

Course Objectives:

• Introduction to advanced statistical analysis
• Group variables: Factor Analysis and Principal Components Analysis
• Group similar cases: Cluster Analysis
• Predict categorical targets with Nearest Neighbor Analysis
• Predict categorical targets with Discriminant Analysis
• Predict categorical targets with Logistic Regression
• Predict categorical targets with Decision Trees
• Introduction to Survival Analysis
• Introduction to Generalized Linear Models
• Introduction to Linear Mixed Models

Pre-requisites:

• Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions.

Target Audience:

• Experience with IBM SPSS Statistics (navigation through windows; using dialog boxes)
• Knowledge of statistics, either by on the job experience, intermediate-level statistics oriented courses, or completion of the Statistical Analysis Using IBM SPSS Statistics (V25) course

Course Duration:

• 14 hours – 2 days

Course Content:

Introduction to advanced statistical analysis

• Taxonomy of models
• Overview of supervised models
• Overview of models to create natural groupings

 Group variables: Factor Analysis and Principal Components Analysis

• Factor Analysis basics
• Principal Components basics
• Assumptions of Factor Analysis
• Key issues in Factor Analysis
• Improve the interpretability
• Use Factor and component scores

 Group similar cases: Cluster Analysis

• Cluster Analysis basics
• Key issues in Cluster Analysis
• K-Means Cluster Analysis
• Assumptions of K-Means Cluster Analysis
• TwoStep Cluster Analysis
• Assumptions of TwoStep Cluster Analysis

Predict categorical targets with Nearest Neighbor Analysis

• Nearest Neighbor Analysis basics
• Key issues in Nearest Neighbor Analysis
• Assess model fit

Predict categorical targets with Discriminant Analysis

• Discriminant Analysis basics
• The Discriminant Analysis model
• Core concepts of Discriminant Analysis
• Classification of cases
• Assumptions of Discriminant Analysis
• Validate the solution

Course Customization Options

To request a customized training for this course, please contact us to arrange.

This course provides students with the knowledge and skills to analyze data with Power BI.

For this course will discuss the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will also show how to access and process data from a range of data sources including both relational and non-relational data. This course will also explore how to implement proper security standards and policies across the Power BI spectrum including datasets and groups. The course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution. Finally, this course will show how to build paginated reports within the Power BI service and publish them to a workspace for inclusion within Power BI.

Course Objectives:

• Describe self-service BI.
• Describe the Power BI suite of products.
• Connect to data sources and optimize data models.
• Shape and combine data from different sources.
• Model data.
• Create reports and manage solutions.
• Describe the Power BI developer API.
• Describe the Power BI mobile app.

Pre-requisites:

• Excellent knowledge of relational databases and reporting.
• Basic knowledge of data warehouse schema topology (including star and snowflake schemas)
• Some exposure to basic programming concepts (such as looping and branching)
• An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable
• Familiarity with Microsoft Office applications (particularly Excel)

Target Audience:

• The primary audience for this course is BI professionals who need to analyze data utilizing Power BI. The secondary audiences for this course are technically proficient business users

Course Duration:

• 21 hours – 3 days

Course Content:

1. Introduction to Self-Service BI Solutions

• Introduction to business intelligence
• Introduction to data analysis
• Introduction to data visualization
• Overview of self-service BI
• Considerations of self-service BI
• Microsoft tools for self-service BI

2. Introducing Power BI

• Power BI
• The Power BI service

3. Power BI

• Using Excel as a data source for Power BI
• The Power BI data model
• Using databases as a Power BI data source
• The Power BI service

4. Shaping and Combining Data

• Power BI desktop queries
• Shaping data
• Combining data

5. Modeling Data

• Relationships
• DAX queries
• Calculations and measures

6. Interactive Data Visualizations

• Creating Power BI reports
• Managing a Power BI solution

7. Direct Connectivity

• Cloud data
• Connecting to analysis services

8. Developer API

• The developer API
• Custom Visuals

9. Power BI mobile app

• Power BI mobile apps
• Using the Power BI mobile app
• Power BI embedded

Course Customization Options

To request a customized training for this course, please contact us to arrange.

This course offers hands-on experience with R and its major features, including installation, data analytics, data science and real time applications. On completion, participants will have a solid foundation for creating enterprise-ready models using R.

Course Objectives:

• End to End data science model creation using R
• Basic and Advance data analytics
• Reap insights out of data
• Visualizations using R

Pre-requisites:

• Basic programming background is preferred
• Good to know basic statistics even though it will be part of syllabus

Target Audience:

• Developers
• Data Analytics

Course Duration:

• 21 hours – 3 days

Course Content:

MODULE 1: INTRODUCTION TO R 

• Installing R
• Basics of Statistics
• Using R in real world

MODULE 2: CORE PROGRAMMING PRINCIPLES 

• Types of variables
• Using Variables
• Logical Variables and Operators
• The “While” Loop · Using the console
• The “For” Loop
• The “If” statement
• Law of Large Numbers

MODULE 3: FUNDAMENTALS OF R 

• What is a Vector?
• Let’s create some vectors
• Using the [] brackets
• Vectorized operations
• The power of vectorized operations
• Functions in R
• Packages in R
• Financial Statement Analysis

MODULE 4: MATRICES 

• Project Brief: Basketball Trends
• Matrices · Building Your First Matrix
• Naming Dimensions
• Colnames() and Rownames()
• Matrix Operations
• Visualizing With Matplot()
• Subsetting
• Visualizing Subsets
• Creating Your First Function
• Basketball Insights
• Basketball Free Throws

MODULE 5: Data Frames

• Project Brief: Demographic Analysis
• Importing data into R
• Exploring your dataset
• Using the $ sign
• Basic operations with a Data Frame
• Filtering a Data Frame
• Introduction to qplot
• Visualizing With Qplot: Part I
• Building Dataframes
• Merging Data Frames
• Visualizing With Qplot: Part II
• World Trends

MODULE 6: ADVANCED VISUALIZATION WITH GGPLOT2 

• Project Brief: Movie Ratings
• Grammar Of Graphics – GGPlot2
• What is a Factor?
• Aesthetics
• Plotting With Layers
• Overriding Aesthetics
• Mapping vs Setting
• Histograms and Density Charts
• Starting Layer Tips
• Statistical Transformations
• Using Facets
• Coordinates
• Perfecting By Adding Themes
• Movie Domestic % Gross

MODULE 7: REAL TIME CASE STUDIES

Course Customization Options

To request a customized training for this course, please contact us to arrange.

Tableau Fundamentals & Advance serves the beginner to intermediate Tableau user, targeted towards anyone who works with data – regardless of technical or analytical background. This course helps you implement important concepts, techniques& admin activities in Tableau. Move from simple to complex visualizations and combine them in interactive dashboards.

Course Objectives:

• Build advanced chart types and visualizations
• Build complex calculations to manipulate your data
• Work with statistics and statistical techniques
• Work with parameters and input controls
• Implement advanced geographic mapping techniques and use custom images to build spatial visualizations of non-geographic data
• Implement all options in working with data: Joining multiple tables, data blending, performance considerations and working with the Data Engine, and understand when to implement which connection method.
• Build better dashboards using techniques for guided analytics, interactive dashboard design and visual best practices
• Implement many efficiency tips and tricks
• Understand the basics of Tableau Server and other options for sharing your results

Pre-requisites:

• Experience with business intelligence and data visualization.
• Excel knowledge

Target Audience:

Designed for professionals in a variety of job roles who are currently using desktop or web-based data-management tools to perform numerical or general data analysis. This includes capturing and reporting on data to peers, executives, and clients. These professionals must also provide data visualizations in reports or explain data analysis through visualizations.

• Data Scientists, Business Intelligence Professionals, and Testing Professionals
• Statisticians, Business Analysts, and Project Managers
• Data Visualization Analysts and Functional Experts
• Graduates and professionals looking to move into the analytics domain

Course Duration:

• 35 hours – 5 days

Course Content:

Introduction 

• What is Visualization?
• What Is Dashboard?
• Why Tableau?
• Comparison of Tableau with other tools
• Limitations of Tableau
• New features in 2019

Connecting to Data 

• Connecting to files
• Connecting to data – Basics
• Editing Data connections and data sources
• Editing Metadata and saving data sources
• Joins in Tableau
• Data Source level Filters
• Connecting to Databases
• Connecting to Oracle, SQL Server, Google Analytics, Different RDBMS Databases
• Connecting to Big Databases like HDFS, Hive, Greenplum, Teradata
• Calling Stored Procedure in Tableau
• Join Types
• Custom SQLs

Creating Basic Visualizations 

• Getting Started in Tableau Desktop
• Elements of Visualization
• Understanding of Dimensions & Measures
• Basic Graphs
• Bar Chart
• Stacked Bar Chart
• Line Chart
• Area Chart
• Pie Chart
• Filled Maps
• Symbol Maps
• Scatter Plots
• Trend Line
• Bullet Chart
• Part to Whole Chart
• Tree Map
• Word cloud
• Bubble Chart
• Discrete Line Charts
• Discrete Area Charts
• Highlight Table
• Heat Map
• Cross Tabs
• Normal Tables

• Grand Totals, Sub-totals and Changing Aggregation

• Dual Axis Graphs

• Dual line chart
• Side by side bar chart
• Donut Chart
• Map and Pie Chart
• Map and Donut Chart
• Bar in Bar Chart
• Bar & Circle Chart

• Measure Name & Measure Values
• Multi-line chart
• Combo Charts
• Highlight table with Multiple Measures

Simplifying and Sorting Your Data

• Data Filtering
• Local Filters
• Global Filters
• Data Source Level Filters
• Context Filters
• Sorting
• Sorting Single Dimension Graphs
• Sorting Multi Dimension Graphs
• Sorting by Different Measures

Organizing your Data

• Using Groups
• Static Groups vs. Dynamic Groups
• Creating and Using Hierarchies
• Creating a Combined Field
• Using Sets

Slicing your Data by Date 

• Working with Dates
• Using Discrete Date Parts
• Creating Date Filters
• Defining a Fiscal Year
• Creating Custom Dates

Data Blending 

• What is Data Blending?
• Data Blending on Multiple columns

File Types in Tableau

• TWB vs TWBX, TDE, TDS vs TDSX, TBM

Connection Types

• Live Connection Vs Data Extracts
• Creating Extracts
• Using and Refreshing Extracts
• Incremental Refresh

Dashboard Creation 

• Creating Dashboards
• Actions in Tableau
• Navigation Buttons
• Dashboard Formatting
• Bringing Images, web pages in Tableau
• Usage of Containers
• Best Practices in Dashboard Designing

Calculations and Statistics 

• Edit in Shelf
• Introduction to Calculations
• Statistical Calculations
• Calculation Types
• Creating Calculated Fields
• Using Logic Statements
• Type Conversions and Date Calculations
• Using Quick Table Calculations
• Calculations and Aggregations
• Conditional Formatting

Using Parameters for Dynamic Values

• What if Analysis using parameters
• Parameters as Column Selectors
• Dynamic Reference lines using Parameters
• Dynamic Top N values using Parameters
• Graph selector using Parameters

Sharing Your Work

• Export to an Image File
• Exporting the Data Only
• Other Sharing Options
• Integrating Dashboard to Other Portals
• Publishing Dashboards on Tableau Public

Dashboard Best Practices

• Sizing
• Dashboard Designing
• Finalizing KPIs
• Dashboard Formatting
• Working with Big Data
• Working in Shared Environment
• Parallel Development & Integration
• Data Visualization Best Practices
• Making Efficient Dashboard

Story Telling

• Why Storytelling?
• How to Create Story Board
• Q&A & Windup Session

Course Customization Options

To request a customized training for this course, please contact us to arrange.

To request a customized training for this course, please contact us to arrange.

To request a customized training for this course, please contact us to arrange.

To request a customized training for this course, please contact us to arrange.

To request a customized training for this course, please contact us to arrange.

To request a customized training for this course, please contact us to arrange.

To request a customized training for this course, please contact us to arrange.