Tag: Predictive analytics

Day 1:

Module 1: KNIME Analytics Platform: Overview

• Installation
• Starting and customizing KNIME Analytics Platform
• Nodes, data and workflows
• The data science cycle

Module 2: Data Access

• Read Data from file
• Accessing REST Services

Module 3: ETL and Data Manipulation

• Row & Column filtering
• Aggregators
• Join & Concatenation
• Transformation: Conversion, Replacement, Standardization, and New Feature Generation
• Data Preparation for Time Series Analysis

Day 2:

Module 4: Exporting Data

• Write to a file
• Generating a Report

Module 5: Data Visualization

• Interactive Univariate Visual Exploration
• Interactive Multivariate Visual Exploration
• Advanced Visualization Features
   

Module 6: Predictive Analytics using KNIME

• Data Mining Basic Concepts
• Regressions
• Decision Tree Family
• Model Evaluation

Day 3:

Module 7: Controlling the flow

• Workflow Parameterization: Flow Variables
• Re-executing Workflow Parts: Loops
• Cleaning up your Workflow

Module 8: Hands on KNIME Analytics Platform based Case Study

Course Outline

Introduction

• What’s new in TIBCO Spotfire X?
• Overview of TIBCO Spotfire X features and architecture
• Understanding augmented and predictive analytics

Getting Started

• Installing TIBCO Spotfire X
• Upgrading Spotfire
• Navigating the UI

Loading Data

• Connecting to a database
• Configuring an on-demand data source
• Importing data from a file
• Transforming data

Processing Data

• Working with large data sets
• Connecting to streaming data
• Handling data in analysis flyout
• Managing data in data canvas
• Manipulating data tables
• Working with multiple data tables

Visualizing Data

• Types of charts, tables, maps, and other visualizations
• Interacting with visualized data
• Enhancing visualizations
• Collaborating using conversations, annotations, etc.
• Using tags, filters, lists, and other tools

Troubleshooting

Introduction

Alteryx Overview

• What is Alteryx?
• Alteryx features

Preparing the Development Environment

• Installing and configuring Alteryx

Input Tools

• Connecting to data sources
• Writing to a data source
• Working with the browse tool

Preparation Tools

• Cleansing, filtering, and sorting data
• Working with fields and columns

Join Tools

• Merging data sets
• Finding and replacing data
• Matching data

Transform and Parse Tools

• Calculating totals and averages
• Transposing data
• Using regular expressions

In-Database Tools

• Connecting in-database tools
• Streaming data

Reporting and Documentation Tools

• Creating tables and charts
• Managing tools

Macros and R Programming

• Creating macros
• Forecasting with predictive models
• Embedding R code

Summary and Conclusion

• Introduction
• Overview of Tableau and the TabPy API
• Exploring Use Cases of TabPy for Data Scientists
• Installing and Setting Up TabPy
• Setting Up Tableau Desktop with Python
• Configuring a TabPy Connection on Tableau
• Passing Expressions to Python
• Running Python Scripts on Tableau
• Estimating the Probability of Customer Churn Using Logistic Regression
• Getting Sentiment Scores for Reviews of Products Sold
• Scoring User Behavior using a Predictive Model
• Using Findings to Create an Efficient Conversion Funnel

Introduction

1.     Why is People Analytics relevant today?

a.     Industry trends

b.     Where People Analytics are heading

c.      Key messages from People Analytics leaders

2.     Introduction to the foundational pillars of People Analytics

a.      Workforce Planning Analytics Pillar

b.     Sourcing and Recruitment Analytics Pillar

c.      Acquisition/Hiring Analytics Pillar

d.     The other pillars and their relevance to Talent Analytics.

3.     Key metrics

a.      Workforce planning – KBI whitebook review of large organization

b.     Talent Analytics – KBI whitebook review of large organization

c.      Sourcing analytics – KBI whitebook review of large organization

4.      Workforce and Talent Acquisition Planning Analytics

a.      What Is Workforce Planning?

b.     Workforce Planning Analytics

c.      Why Should You Care About Workforce Planning Analytics?

d.     Key Components of Talent Acquisition Analytics

e.      Making an IMPACT with Workforce Planning Analytics

f.       Workforce Planning Analytics Best Practices: Dos and Don’ts

g.     Case studies review

5.      Talent Sourcing Analytics

a.      The Business Case for Talent Sourcing Today

b.     Why You Need to Care about Your Talent Sourcing Today

c.      Talent Sourcing in the Era of Big Data and Advanced

d.     Technology

e.      The Mobile Impact on Talent Sourcing

f.       Putting the IMPACT Cycle into Action

g.     Case studies review

6.      Talent Acquisition Analytics

a.      What Is Talent Acquisition Analytics?

b.     How Talent Acquisition Works

c.      Application Phase

d.     Preinterview Assessment Analytics

e.      Interviews: Separating the Wheat from the Chaff

f.       Putting It All Together: Predictive Analytics for Selection

g.     Case studies review

7.     Summary

a.      Outlining next steps

b.     Commitment

c.      Support needed – where to find it– 

Duration

14 hours (usually 2 days including breaks)

Requirements

Motivation to learn

Overview

Goal:

Learning to work with SPSS at the level of independence

The addressees:

Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques.

Course Outline

Using the program

• The dialog boxes
  • input / downloading data
  • the concept of variable and measuring scales
  • preparing a database
  • Generate tables and graphs
  • formatting of the report
• Command language syntax
  • automated analysis
  • storage and modification procedures
  • create their own analytical procedures

Data Analysis

• descriptive statistics
  • Key terms: eg variable, hypothesis, statistical significance
  • measures of central tendency
  • measures of dispersion
  • measures of central tendency
  • standardization
• Introduction to research the relationships between variables
  • correlational and experimental methods
• Summary: This case study and discussion

Duration

21 hours (usually 3 days including breaks)

Requirements

Understanding of traditional data management and analysis methods like SQL, data warehouses, business intelligence, OLAP, etc… Understanding of basic statistics and probability (mean, variance, probability, conditional probability, etc….)

Overview

Audience

If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc…) this course is for you.

It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.

It is not aimed at people configuring the solution, those people will benefit from the big picture though.

Delivery Mode

During the course delegates will be presented with working examples of mostly open source technologies.

Short lectures will be followed by presentation and simple exercises by the participants

Content and Software used

All software used is updated each time the course is run, so we check the newest versions possible.

It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

Course Outline

Quick Overview

• Data Sources
• Minding Data
• Recommender systems
• Target Marketing

Datatypes

• Structured vs unstructured
• Static vs streamed
• Attitudinal, behavioural and demographic data
• Data-driven vs user-driven analytics
• data validity
• Volume, velocity and variety of data

Models

• Building models
• Statistical Models
• Machine learning

Data Classification

• Clustering
• kGroups, k-means, the nearest neighbours
• Ant colonies, birds flocking

Predictive Models

• Decision trees
• Support vector machine
• Naive Bayes classification
• Neural networks
• Markov Model
• Regression
• Ensemble methods

ROI

• Benefit/Cost ratio
• Cost of software
• Cost of development
• Potential benefits

Building Models

• Data Preparation (MapReduce)
• Data cleansing
• Choosing methods
• Developing model
• Testing Model
• Model evaluation
• Model deployment and integration

Overview of Open Source and commercial software

• Selection of R-project package
• Python libraries
• Hadoop and Mahout
• Selected Apache projects related to Big Data and Analytics
• Selected commercial solution
• Integration with existing software and data sources

Duration

21 hours (usually 3 days including breaks)

Requirements

• Experience with Matlab
• No previous experience with data science is required

Overview

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

By the end of this training, participants will be able to:

• Create predictive models to analyze patterns in historical and transactional data
• Use predictive modeling to identify risks and opportunities
• Build mathematical models that capture important trends
• Use data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline

Introduction

• Predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing

Overview of Big Data concepts

Capturing data from disparate sources

What are data-driven predictive models?

Overview of statistical and machine learning techniques

Case study: predictive maintenance and resource planning

Applying algorithms to large data sets with Hadoop and Spark

Predictive Analytics Workflow

Accessing and exploring data

Preprocessing the data

Developing a predictive model

Training, testing and validating a data set

Applying different machine learning approaches (time-series regression, linear regression, etc.)

Integrating the model into existing web applications, mobile devices, embedded systems, etc.

Matlab and Simulink integration with embedded systems and enterprise IT workflows

Creating portable C and C++ code from MATLAB code

Deploying predictive applications to large-scale production systems, clusters, and clouds

Acting on the results of your analysis

Next steps: Automatically responding to findings using Prescriptive Analytics

Closing remarks

Duration

21 hours (usually 3 days including breaks)

Requirements

Understanding of traditional data management and analysis methods like SQL, data warehouses, business intelligence, OLAP, etc… Understanding of basic statistics and probability (mean, variance, probability, conditional probability, etc….)

Overview

Audience

If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc…) this course is for you.

It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.

It is not aimed at people configuring the solution, those people will benefit from the big picture though.

Delivery Mode

During the course delegates will be presented with working examples of mostly open source technologies.

Short lectures will be followed by presentation and simple exercises by the participants

Content and Software used

All software used is updated each time the course is run, so we check the newest versions possible.

It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

Course Outline

Quick Overview

• Data Sources
• Minding Data
• Recommender systems
• Target Marketing

Datatypes

• Structured vs unstructured
• Static vs streamed
• Attitudinal, behavioural and demographic data
• Data-driven vs user-driven analytics
• data validity
• Volume, velocity and variety of data

Models

• Building models
• Statistical Models
• Machine learning

Data Classification

• Clustering
• kGroups, k-means, the nearest neighbours
• Ant colonies, birds flocking

Predictive Models

• Decision trees
• Support vector machine
• Naive Bayes classification
• Neural networks
• Markov Model
• Regression
• Ensemble methods

ROI

• Benefit/Cost ratio
• Cost of software
• Cost of development
• Potential benefits

Building Models

• Data Preparation (MapReduce)
• Data cleansing
• Choosing methods
• Developing model
• Testing Model
• Model evaluation
• Model deployment and integration

Overview of Open Source and commercial software

• Selection of R-project package
• Python libraries
• Hadoop and Mahout
• Selected Apache projects related to Big Data and Analytics
• Selected commercial solution
• Integration with existing software and data sources

Duration

14 hours (usually 2 days including breaks)

Requirements

• An understanding of data science concepts

Overview

RapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. 

In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment. 

By the end of this training, participants will be able to:

• Install and configure RapidMiner
• Prepare and visualize data with RapidMiner
• Validate machine learning models
• Mashup data and create predictive models
• Operationalize predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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

Course Outline

Introduction

Installing and Configuring RapidMiner

Overview of RapidMiner Studio Interface and Mechanics

Recap of the Analytical Cycle

Overview of Repository

Importing Data

Preparing Data

Modeling 

Validation

Using Macros

Using Global Search

Buidling More Sophisticated Predictive Models

Evaluating Model Quality

Troubleshooting and Optimization

Summary and Conclusion