Duration
14 hours (usually 2 days including breaks)
Requirements
- An understanding of Python programming.
- An understanding of Python libraries in general.
Audience
- Data analysts
- Data scientists
Overview
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.
Course Outline
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
Duration
28 hours (usually 4 days including breaks)
Overview
Objective:
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.
Course Outline
- 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
Duration
14 hours (usually 2 days including breaks)
Requirements
Good R knowledge.
Overview
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.
Course Outline
Sources of methods
- Artificial intelligence
- Machine learning
- Statistics
- Sources of data
Pre processing of data
- Data Import/Export
- Data Exploration and Visualization
- Dimensionality Reduction
- Dealing with missing values
- R Packages
Data mining main tasks
- 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)
Data 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
Duration
21 hours (usually 3 days including breaks)
Requirements
Fair knowledge about relational data structures, SQL
Overview
Course can be provided with any tools, including free open-source data mining software and applications
Course Outline
Introduction
- Data mining as the analysis step of the KDD process (“Knowledge Discovery in Databases”)
- Subfield of computer science
- Discovering patterns in large data sets
Sources of methods
- Artificial intelligence
- Machine learning
- Statistics
- Database systems
What is involved?
- Database and data management aspects
- Data pre-processing
- Model and inference considerations
- Interestingness metrics
- Complexity considerations
- Post-processing of discovered structures
- Visualization
- Online updating
Data mining main tasks
- 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)
Data mining
- Anomaly detection (Outlier/change/deviation detection)
- Association rule learning (Dependency modeling)
- Clustering
- Classification
- Regression
- Summarization
Use and applications
- 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
Data dredging, data fishing, data snooping
Duration
35 hours (usually 5 days including breaks)
Overview
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.
Course Outline
Introduction to Data Science for Big Data Analytics
- 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
Introduction to Data Analytics lifecycle
- Discovery
- Data preparation
- Model planning
- Model building
- Presentation/Communication of results
- Operationalization
- Exercise: Case study
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
- Installing R and Rstudio
- Features of R language
- Objects in R
- Data in R
- Data manipulation
- Big data issues
- Exercises
Getting started with Hadoop
- Installing Hadoop
- Understanding Hadoop modes
- HDFS
- MapReduce architecture
- Hadoop related projects overview
- Writing programs in Hadoop MapReduce
- Exercises
Integrating R and Hadoop with RHadoop
- Components of RHadoop
- Installing RHadoop and connecting with Hadoop
- The architecture of RHadoop
- Hadoop streaming with R
- Data analytics problem solving with RHadoop
- Exercises
Pre-processing and preparing data
- 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
Exploratory data analytic methods in R
- Descriptive statistics
- Exploratory data analysis
- Visualization – preliminary steps
- Visualizing single variable
- Examining multiple variables
- Statistical methods for evaluation
- Hypothesis testing
- Exercises and Case study
Data Visualizations
- Basic visualizations in R
- Packages for data visualization ggplot2, lattice, plotly, lattice
- Formatting plots in R
- Advanced graphs
- Exercises
Regression (Estimating future values)
- 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
Classification
- 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
Assessing model performance and selection
- 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
Ensemble Methods
- Bagging
- Random Forests
- Boosting
- Gradient boosting
- Exercises and Case study
Support vector machines for classification and regression
- Maximal Margin classifiers
- Support vector classifiers
- Support vector machines
- SVM’s for classification problems
- SVM’s for regression problems
- Exercises and Case study
Identifying unknown groupings within a data set
- 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
Discovering connections with Link Analysis
- Link analysis concepts
- Metrics for analyzing networks
- The Pagerank algorithm
- Hyperlink-Induced Topic Search
- Link Prediction
- Exercises and Case study
Association Pattern Mining
- 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
Constructing recommendation engines
- 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
- Text analysis steps
- Collecting raw text
- Bag of words
- Term Frequency –Inverse Document Frequency
- Determining Sentiments
- Exercises and Case study
Duration
14 hours (usually 2 days including breaks)
Requirements
- A basic understanding of Excel
Audience
Overview
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.
Course Outline
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
Duration
14 hours (usually 2 days including breaks)
Requirements
- Basic knowledge of data mining process and techniques
Audience
- Data Analysts
- Data Scientists
Overview
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.
Course Outline
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
Duration
14 hours (usually 2 days including breaks)
Requirements
This course is part of the Data Scientist skill set (Domain: Analytical Techniques and Methods)
Overview
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.
Course Outline
Introduction to Data mining and Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
Regression
- Linear regression
- Generalizations and Nonlinearity
- Exercises
Classification
- Bayesian refresher
- Naive Bayes
- Dicriminant analysis
- Logistic regression
- K-Nearest neighbors
- Support Vector Machines
- Neural networks
- Decision trees
- Exercises
Cross-validation and Resampling
- Cross-validation approaches
- Bootstrap
- Exercises
Unsupervised Learning
- K-means clustering
- Examples
- Challenges of unsupervised learning and beyond K-means
Advanced topics
- Ensemble models
- Mixed models
- Boosting
- Examples
Multidimensional reduction
- Factor Analysis
- Principal Component Analysis
- Examples
Duration
14 hours (usually 2 days including breaks)
Requirements
- Basic knowledge of data mining process and techniques
Audience
- Data Analysts
- Data Scientists
Overview
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.
Course Outline
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