## Course content

- Introduction
- LinkedIn datasets
- Connections between users and invitations
- Messages between users
- Final remarks

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# Tag: data mining

## Mining and Analyzing LinkedIn Data

## Course content

## Data Mining with Python Training Course

## Duration

## Requirements

## Overview

## Course Outline

## Data Mining and Analysis Training Course

## Duration

## Overview

### Objective:

## Course Outline

## Data Mining with R Training Course

## Duration

## Requirements

## Overview

## Course Outline

**Sources of methods**

### Pre processing of data

**Data mining main tasks**

**Data mining**

## Data Mining Training Course

## Duration

## Requirements

## Overview

## Course Outline

### Introduction

### Sources of methods

### What is involved?

### Data mining main tasks

### Data mining

### Use and applications

### Data dredging, data fishing, data snooping

## Data Science for Big Data Analytics Training Course

## Duration

## Overview

## Course Outline

### Introduction to Data Science for Big Data Analytics

### Introduction to Data Analytics lifecycle

*From this point most of the training time (80%) will be spent on examples and exercises in R and related big data technology.*

### Getting started with R

### Getting started with Hadoop

### Integrating R and Hadoop with RHadoop

### Pre-processing and preparing data

### Exploratory data analytic methods in R

### Data Visualizations

### Regression (Estimating future values)

### Classification

### Assessing model performance and selection

### Ensemble Methods

### Support vector machines for classification and regression

### Identifying unknown groupings within a data set

### Discovering connections with Link Analysis

### Association Pattern Mining

### Constructing recommendation engines

### Text analysis

## Data Mining with Excel Training Course

## Duration

## Requirements

## Overview

## Course Outline

## Data Mining with Weka Training Course

## Duration

## Requirements

## Overview

## Course Outline

## Data Mining & Machine Learning with R Training Course

## Duration

## Requirements

## Overview

## Course Outline

### Introduction to Data mining and Machine Learning

### Regression

### Classification

### Cross-validation and Resampling

### Unsupervised Learning

### Advanced topics

### Multidimensional reduction

## Data Mining with Weka Training Course

## Duration

## Requirements

## Overview

## Course Outline

- Introduction
- LinkedIn datasets
- Connections between users and invitations
- Messages between users
- Final remarks

14 hours (usually 2 days including breaks)

- An understanding of Python programming.
- An understanding of Python libraries in general.

**Audience**

- Data analysts
- Data scientists

This instructor-led, live training (online or onsite) is aimed at data analysts and data scientists who wish to implement more advanced data analytics techniques for data mining using Python.

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

- Understand important areas of data mining, including association rule mining, text sentiment analysis, automatic text summarization, and data anomaly detection.
- Compare and implement various strategies for solving real-world data mining problems.
- Understand and interpret the results.

**Format of the Course**

- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.

**Course Customization Options**

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

Introduction

Overview of Data Mining Concepts

Data Mining Techniques

Finding Association Rules

Matching Entities

Analyzing Networks

Analyzing the Sentiment of Text

Recognizing Named Entities

Implementing Text Summarization

Generating Topic Models

Detecting Data Anomalies

Best Practices

Summary and Conclusion

28 hours (usually 4 days including breaks)

Delegates be able to analyse big data sets, extract patterns, choose the right variable impacting the results so that a new model is forecasted with predictive results.

- Data preprocessing
- Data Cleaning
- Data integration and transformation
- Data reduction
- Discretization and concept hierarchy generation

- Statistical inference
- Probability distributions, Random variables, Central limit theorem
- Sampling
- Confidence intervals
- Statistical Inference
- Hypothesis testing

- Multivariate linear regression
- Specification
- Subset selection
- Estimation
- Validation
- Prediction

- Classification methods
- Logistic regression
- Linear discriminant analysis
- K-nearest neighbours
- Naive Bayes
- Comparison of Classification methods

- Neural Networks
- Fitting neural networks
- Training neural networks issues

- Decision trees
- Regression trees
- Classification trees
- Trees Versus Linear Models

- Bagging, Random Forests, Boosting
- Bagging
- Random Forests
- Boosting

- Support Vector Machines and Flexible disct
- Maximal Margin classifier
- Support vector classifiers
- Support vector machines
- 2 and more classes SVM’s
- Relationship to logistic regression

- Principal Components Analysis
- Clustering
- K-means clustering
- K-medoids clustering
- Hierarchical clustering
- Density based clustering

- Model Assesment and Selection
- Bias, Variance and Model complexity
- In-sample prediction error
- The Bayesian approach
- Cross-validation
- Bootstrap methods

14 hours (usually 2 days including breaks)

Good R knowledge.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

- Artificial intelligence
- Machine learning
- Statistics
- Sources of data

- Data Import/Export
- Data Exploration and Visualization
- Dimensionality Reduction
- Dealing with missing values
- R Packages

- Automatic or semi-automatic analysis of large quantities of data
- Extracting previously unknown interesting patterns
- groups of data records (cluster analysis)
- unusual records (anomaly detection)
- dependencies (association rule mining)

- Anomaly detection (Outlier/change/deviation detection)
- Association rule learning (Dependency modeling)
- Clustering
- Classification
- Regression
- Summarization
- Frequent Pattern Mining
- Text Mining
- Decision Trees
- Regression
- Neural Networks
- Sequence Mining
- Frequent Pattern Mining

**Data dredging, data fishing, data snooping**

21 hours (usually 3 days including breaks)

Fair knowledge about relational data structures, SQL

Course can be provided with any tools, including free open-source data mining software and applications

- Data mining as the analysis step of the KDD process (“Knowledge Discovery in Databases”)
- Subfield of computer science
- Discovering patterns in large data sets

- Artificial intelligence
- Machine learning
- Statistics
- Database systems

- Database and data management aspects
- Data pre-processing
- Model and inference considerations
- Interestingness metrics
- Complexity considerations
- Post-processing of discovered structures
- Visualization
- Online updating

- Automatic or semi-automatic analysis of large quantities of data
- Extracting previously unknown interesting patterns
- groups of data records (cluster analysis)
- unusual records (anomaly detection)
- dependencies (association rule mining)

- Anomaly detection (Outlier/change/deviation detection)
- Association rule learning (Dependency modeling)
- Clustering
- Classification
- Regression
- Summarization

- Able Danger
- Behavioral analytics
- Business analytics
- Cross Industry Standard Process for Data Mining
- Customer analytics
- Data mining in agriculture
- Data mining in meteorology
- Educational data mining
- Human genetic clustering
- Inference attack
- Java Data Mining
- Open-source intelligence
- Path analysis (computing)
- Reactive business intelligence

35 hours (usually 5 days including breaks)

Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

- Data Science Overview
- Big Data Overview
- Data Structures
- Drivers and complexities of Big Data
- Big Data ecosystem and a new approach to analytics
- Key technologies in Big Data
- Data Mining process and problems
- Association Pattern Mining
- Data Clustering
- Outlier Detection
- Data Classification

- Discovery
- Data preparation
- Model planning
- Model building
- Presentation/Communication of results
- Operationalization
- Exercise: Case study

- Installing R and Rstudio
- Features of R language
- Objects in R
- Data in R
- Data manipulation
- Big data issues
- Exercises

- Installing Hadoop
- Understanding Hadoop modes
- HDFS
- MapReduce architecture
- Hadoop related projects overview
- Writing programs in Hadoop MapReduce
- Exercises

- Components of RHadoop
- Installing RHadoop and connecting with Hadoop
- The architecture of RHadoop
- Hadoop streaming with R
- Data analytics problem solving with RHadoop
- Exercises

- Data preparation steps
- Feature extraction
- Data cleaning
- Data integration and transformation
- Data reduction – sampling, feature subset selection,
- Dimensionality reduction
- Discretization and binning
- Exercises and Case study

- Descriptive statistics
- Exploratory data analysis
- Visualization – preliminary steps
- Visualizing single variable
- Examining multiple variables
- Statistical methods for evaluation
- Hypothesis testing
- Exercises and Case study

- Basic visualizations in R
- Packages for data visualization ggplot2, lattice, plotly, lattice
- Formatting plots in R
- Advanced graphs
- Exercises

- Linear regression
- Use cases
- Model description
- Diagnostics
- Problems with linear regression
- Shrinkage methods, ridge regression, the lasso
- Generalizations and nonlinearity
- Regression splines
- Local polynomial regression
- Generalized additive models
- Regression with RHadoop
- Exercises and Case study

- The classification related problems
- Bayesian refresher
- Naïve Bayes
- Logistic regression
- K-nearest neighbors
- Decision trees algorithm
- Neural networks
- Support vector machines
- Diagnostics of classifiers
- Comparison of classification methods
- Scalable classification algorithms
- Exercises and Case study

- Bias, Variance and model complexity
- Accuracy vs Interpretability
- Evaluating classifiers
- Measures of model/algorithm performance
- Hold-out method of validation
- Cross-validation
- Tuning machine learning algorithms with caret package
- Visualizing model performance with Profit ROC and Lift curves

- Bagging
- Random Forests
- Boosting
- Gradient boosting
- Exercises and Case study

- Maximal Margin classifiers
- Support vector classifiers
- Support vector machines
- SVM’s for classification problems
- SVM’s for regression problems

- Exercises and Case study

- Feature Selection for Clustering
- Representative based algorithms: k-means, k-medoids
- Hierarchical algorithms: agglomerative and divisive methods
- Probabilistic base algorithms: EM
- Density based algorithms: DBSCAN, DENCLUE
- Cluster validation
- Advanced clustering concepts
- Clustering with RHadoop
- Exercises and Case study

- Link analysis concepts
- Metrics for analyzing networks
- The Pagerank algorithm
- Hyperlink-Induced Topic Search
- Link Prediction
- Exercises and Case study

- Frequent Pattern Mining Model
- Scalability issues in frequent pattern mining
- Brute Force algorithms
- Apriori algorithm
- The FP growth approach
- Evaluation of Candidate Rules
- Applications of Association Rules
- Validation and Testing
- Diagnostics
- Association rules with R and Hadoop
- Exercises and Case study

- Understanding recommender systems
- Data mining techniques used in recommender systems
- Recommender systems with recommenderlab package
- Evaluating the recommender systems
- Recommendations with RHadoop
- Exercise: Building recommendation engine

- Text analysis steps
- Collecting raw text
- Bag of words
- Term Frequency –Inverse Document Frequency
- Determining Sentiments
- Exercises and Case study

14 hours (usually 2 days including breaks)

- A basic understanding of Excel

**Audience**

- Data Scientists

Data mining is the process of identifying patterns in big data with data science methods such as machine learning. Using Excel as a data analytical suite, users can perform data mining and analysis.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Excel for data mining.

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

- Explore data with Excel to perform data mining and analysis.
- Use Microsoft algorithms for data mining.
- Understand concepts in Excel data mining.

**Format of the Course**

- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.

**Course Customization Options**

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

Introduction

Data Mining Overview

- Crisp-DM
- Algorithms for data mining
- Data mining structures and models

Preparing the Development Environment

- Installing and configuring Excel

Data Mining Algorithms and Excel to SQL Server Analysis Services

- Using Microsoft algorithms
- Working with data mining structures and models

Data Mining Add-Ins

- Working with data modeling
- Identifying influencers and categories
- Predicting with a model

Summary and Conclusion

14 hours (usually 2 days including breaks)

- Basic knowledge of data mining process and techniques

**Audience**

- Data Analysts
- Data Scientists

Waikato Environment for Knowledge Analysis (Weka) is an open-source data mining visualization software. It provides a collection of machine learning algorithms for data preparation, classification, clustering, and other data mining activities.

This instructor-led, live training (online or onsite) is aimed at beginner to intermediate-level data analysts and data scientists who wish to use Weka to perform data mining tasks.

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

- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.

**Format of the Course**

- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.

**Course Customization Options**

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

Introduction

- Overview of Weka
- Understanding the data mining process

Getting Started

- Installing and configuring Weka
- Understanding the Weka UI
- Setting up the environment and project
- Exploring the Weka workbench
- Loading and Exploring the dataset

Implementing Regression Models

- Understanding the different regression models
- Processing and saving processed data
- Evaluating a model using cross-validation
- Serializing and visualizing a decision tree model

Implementing Classification Models

- Understanding feature selection and data processing
- Building and evaluating classification models
- Building and visualizing a decision tree model
- Encoding text data in numeric form
- Performing classification on text data

Implementing Clustering Models

- Understanding K-means clustering
- Normalizing and visualizing data
- Performing K-means clustering
- Performing hierarchical clustering
- Performing EM clustering

Deploying a Weka Model

Troubleshooting

Summary and Next Steps

14 hours (usually 2 days including breaks)

This course is part of the Data Scientist skill set (Domain: Analytical Techniques and Methods)

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off

- Linear regression
- Generalizations and Nonlinearity
- Exercises

- Bayesian refresher
- Naive Bayes
- Dicriminant analysis
- Logistic regression
- K-Nearest neighbors
- Support Vector Machines
- Neural networks
- Decision trees
- Exercises

- Cross-validation approaches
- Bootstrap
- Exercises

- K-means clustering
- Examples
- Challenges of unsupervised learning and beyond K-means

- Ensemble models
- Mixed models
- Boosting
- Examples

- Factor Analysis
- Principal Component Analysis
- Examples

14 hours (usually 2 days including breaks)

- Basic knowledge of data mining process and techniques

**Audience**

- Data Analysts
- Data Scientists

Waikato Environment for Knowledge Analysis (Weka) is an open-source data mining visualization software. It provides a collection of machine learning algorithms for data preparation, classification, clustering, and other data mining activities.

This instructor-led, live training (online or onsite) is aimed at beginner to intermediate-level data analysts and data scientists who wish to use Weka to perform data mining tasks.

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

- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.

**Format of the Course**

- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.

**Course Customization Options**

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

Introduction

- Overview of Weka
- Understanding the data mining process

Getting Started

- Installing and configuring Weka
- Understanding the Weka UI
- Setting up the environment and project
- Exploring the Weka workbench
- Loading and Exploring the dataset

Implementing Regression Models

- Understanding the different regression models
- Processing and saving processed data
- Evaluating a model using cross-validation
- Serializing and visualizing a decision tree model

Implementing Classification Models

- Understanding feature selection and data processing
- Building and evaluating classification models
- Building and visualizing a decision tree model
- Encoding text data in numeric form
- Performing classification on text data

Implementing Clustering Models

- Understanding K-means clustering
- Normalizing and visualizing data
- Performing K-means clustering
- Performing hierarchical clustering
- Performing EM clustering

Deploying a Weka Model

Troubleshooting

Summary and Next Steps