Alteryx for Developers Training Course

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

Data Analysis with Tableau and Python Training Course

  • 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

Talent Acquisition Analytics Training Course

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– 

Statistics with SPSS Predictive Analytics Software Training Course

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

From Data to Decision with Big Data and Predictive Analytics Training Course

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

Matlab for Predictive Analytics Training Course

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

From Data to Decision with Big Data and Predictive Analytics Training Course

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

RapidMiner for Machine Learning and Predictive Analytics Training Course

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

Tasting Machine Learning with Minitab Predictive Analytics

Understand the concept of regression analysis and its applications in predictive modeling.

Understand the concepts of overfitting and underfitting.

Learn how to build a regression tree using Minitab.

Learn how to set up a binary logistic regression model using Minitab.

Practice building a classification tree and using it for prediction using Minitab.

Requirements

  • No prior programming knowledge is required. The tutorials are based on Minitab software version 21. If you want to try it yourself on the data files provided, you will need this software. The 30-day trial is free. The course assumes basic statistical knowledge.

Description

In this mini-course, “Tasting Machine Learning with Minitab Predictive Analytics”, you will gain an introduction to the world of predictive analytics and machine learning using Minitab statistical software.

Through five lectures, you will learn about regression analysis and classification, two fundamental techniques in predictive modeling. In the first two lectures, you will learn how to set up and verify regression models, as well as how to identify and address potential issues with overfitting or underfitting. In Lecture 3, you will explore regression trees, which are a powerful alternative to linear regression when the relationship between variables is non-linear.

In Lecture 4, you will delve into binary logistic regression, which is a technique used for predicting binary outcomes (such as “yes” or “no” responses). You will learn how to set up and evaluate a binary logistic regression model. Finally, in Lecture 5, you will discover classification trees, which are a type of decision tree used to classify objects or cases into different categories. You will learn how to build and interpret classification trees, and use them for prediction.

By the end of this mini-course, you will have gained practical experience in building and evaluating regression and classification models using Minitab, and an understanding of how these techniques can be applied in various real-world scenarios. Whether you are new to machine learning or looking to expand your knowledge, this mini-course is an excellent opportunity to explore the basics of predictive analytics with Minitab.

Who this course is for:

  • This course is for those who want a concise taste of the 4 basic methods of machine learning before embarking on a more detailed course.

Course content

Artificial intelligence (AI) vs. machine learning (ML)

You might hear people use artificial intelligence (AI) and machine learning (ML) interchangeably, especially when discussing big data, predictive analytics, and other digital transformation topics. The confusion is understandable as artificial intelligence and machine learning are closely related. However, these trending technologies differ in several ways, including scope, applications, and more.  

Increasingly AI and ML products have proliferated as businesses use them to process and analyze immense volumes of data, drive better decision-making, generate recommendations and insights in real time, and create accurate forecasts and predictions. 

So, what exactly is the difference when it comes to ML vs. AI, how are ML and AI connected, and what do these terms mean in practice for organizations today? 

We’ll break down AI vs. ML and explore how these two innovative concepts are related and what makes them different from each other.Get started for free

What is artificial intelligence?

Artificial intelligence is a broad field, which refers to the use of technologies to build machines and computers that have the ability to mimic cognitive functions associated with human intelligence, such as being able to see, understand, and respond to spoken or written language, analyze data, make recommendations, and more. 

Although artificial intelligence is often thought of as a system in itself, it is a set of technologies implemented in a system to enable it to reason, learn, and act to solve a complex problem. 

What is machine learning?

Machine learning is a subset of artificial intelligence that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data, learn from the insights, and then make informed decisions. 

Machine learning algorithms improve performance over time as they are trained—exposed to more data. Machine learning models are the output, or what the program learns from running an algorithm on training data. The more data used, the better the model will get. 

How are AI and ML connected?

While AI and ML are not quite the same thing, they are closely connected. The simplest way to understand how AI and ML relate to each other is:  

  • AI is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human 
  • ML is an application of AI that allows machines to extract knowledge from data and learn from it autonomously

One helpful way to remember the difference between machine learning and artificial intelligence is to imagine them as umbrella categories. Artificial intelligence is the overarching term that covers a wide variety of specific approaches and algorithms. Machine learning sits under that umbrella, but so do other major subfields, such as deep learning, robotics, expert systems, and natural language processing.

Differences between AI and ML

Now that you understand how they are connected, what is the main difference between AI and ML? 

While artificial intelligence encompasses the idea of a machine that can mimic human intelligence, machine learning does not. Machine learning aims to teach a machine how to perform a specific task and provide accurate results by identifying patterns. 

Let’s say you ask your Google Nest device, “How long is my commute today?” In this case, you ask a machine a question and receive an answer about the estimated time it will take you to drive to your office. Here, the overall goal is for the device to perform a task successfully—a task that you would generally have to do yourself in a real-world environment (for example, research your commute time). 

In the context of this example, the goal of using ML in the overall system is not to enable it to perform a task. For instance, you might train algorithms to analyze live transit and traffic data to forecast the volume and density of traffic flow. However, the scope is limited to identifying patterns, how accurate the prediction was, and learning from the data to maximize performance for that specific task.

Artificial intelligence

  • AI allows a machine to simulate human intelligence to solve problems
  • The goal is to develop an intelligent system that can perform complex tasks
  • We build systems that can solve complex tasks like a human
  • AI has a wide scope of applications
  • AI uses technologies in a system so that it mimics human decision-making
  • AI works with all types of data: structured, semi-structured, and unstructured
  • AI systems use logic and decision trees to learn, reason, and self-correct

Machine learning

  • ML allows a machine to learn autonomously from past data
  • The goal is to build machines that can learn from data to increase the accuracy of the output
  • We train machines with data to perform specific tasks and deliver accurate results
  • Machine learning has a limited scope of applications
  • ML uses self-learning algorithms to produce predictive models
  • ML can only use structured and semi-structured data
  • ML systems rely on statistical models to learn and can self-correct when provided with new data

Benefits of using AI and ML together

AI and ML bring powerful benefits to organizations of all shapes and sizes, with new possibilities constantly emerging. In particular, as the amount of data grows in size and complexity, automated and intelligent systems are becoming vital to helping companies automate tasks, unlock value, and generate actionable insights to achieve better outcomes. 

Here are some of the business benefits of using artificial intelligence and machine learning: 

Wider data ranges

Analyzing and activating a wider range of unstructured and structured data sources.

Faster decision-making

Improving data integrity, accelerating data processing, and reducing human error for more informed, faster decision-making.

Efficiency

Increasing operational efficiency and reducing costs.

Analytic integration

Empowering employees by integrating predictive analytics and insights into business reporting and applications.

Applications of AI and ML

Artificial intelligence and machine learning can be applied in many ways, allowing organizations to automate repetitive or manual processes that help drive informed decision-making.

Companies across industries are using AI and ML in various ways to transform how they work and do business. Incorporating AI and ML capabilities into their strategies and systems helps organizations rethink how they use their data and available resources, drive productivity and efficiency, enhance data-driven decision-making through predictive analytics, and improve customer and employee experiences.   

Here are some of the most common applications of AI and ML: 

Healthcare and life sciences

Patient health record analysis and insights, outcome forecasting and modeling, accelerated drug development, augmented diagnostics, patient monitoring, and information extraction from clinical notes.

Manufacturing

Production machine monitoring, predictive maintenance, IoT analytics, and operational efficiency.

Ecommerce and retail

Inventory and supply chain optimization, demand forecasting, visual search, personalized offers and experiences, and recommendation engines.

Financial services

Risk assessment and analysis, fraud detection, automated trading, and service processing optimization.

Telecommunications

Intelligent networks and network optimization, predictive maintenance, business process automation, upgrade planning, and capacity forecasting.