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Data Mining and Analysis

Data Mining and Analysis

Delve deep into the world of data analysis with our comprehensive training on Data Mining and Analysis. Understand, dissect, and forecast with enhanced predictive results by mastering techniques to process, infer, and classify vast data sets.

What will you learn?

In our Data Mining and Analysis course, participants will:

• Understand the intricacies of data preprocessing and transformation.

• Gain hands-on experience in statistical inference, sampling, and hypothesis testing.

• Familiarize themselves with advanced classification methods and comparison.

• Dive into the potential of neural networks, decision trees, and support vector machines.

• Explore clustering techniques and methods for model assessment & selection. After this intensive 4-day course, delegates will be fully equipped to harness big data's potential and derive actionable insights from vast information reservoirs.

Requirements:

Basic understanding of IT concepts. No prior knowledge in data analysis required, but familiarity with statistical methods is an advantage.

Basic IT knowledge: Familiarity with operating systems, hardware configurations, and basic network operations.

Optional: Previous experience with distributed systems will be beneficial but not mandatory.

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

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 and Confidence Intervals
  • Hypothesis Testing
  • Multivariate Linear Regression

Model Specification and Selection:

  • Subset Selection
  • Estimation, Validation, and Prediction

Classification Methods:

  • Logistic Regression
  • Linear Discriminant Analysis
  • K-nearest Neighbours
  • Naive Bayes
  • Comparison of Classification Methods

Neural Networks:

  • Fitting and Training Issues

Decision Trees:

  • Regression and Classification Trees
  • Comparing Trees and Linear Models

Ensemble Methods:

  • Bagging, Random Forests, Boosting

Support Vector Machines:

  • Maximal Margin Classifier
  • Support Vector Classifiers and Machines
  • Multi-class SVMs

Principal Components Analysis

Clustering:

  • K-means and K-medoids Clustering
  • Hierarchical and Density-based Clustering

Model Assessment and Selection:

  • Bias, Variance, Model Complexity
  • In-sample Prediction Error
  • Bayesian Approach
  • Cross-validation and Bootstrap Methods

Hands-on learning with expert instructors at your location for organizations.

5.622€*
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Level:
intermediate
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Duration:
28
Hours (days:
4
)
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Training customized to your needs
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Immersive hands-on experience in a dedicated setting
*Price can range depending on number of participants, change of outline, location etc.

Master new skills guided by experienced instructors from anywhere.

3.987€*
Graph Icon - Education X Webflow Template
Level:
intermediate
Clock Icon - Education X Webflow Template
Duration:
28
Hours (days:
4
)
Camera Icon - Education X Webflow Template
Training customized to your needs
Star Icon - Education X Webflow Template
Reduced training costs
*Price can range depending on number of participants, change of outline, location etc.