BWC-DATAWM.AOP1 ISBN: 978-1-64459-791-0
Data Warehouse and Data Mining Fundamentals
Intermediate Self-paced · 1 year access
9 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
7 Interactive Lessons · 55 topics01 Modeling for Analytical Databases 4 topics · 1 LiveLab +
- Data Warehouses
- Data Marts
- Modeling Analytical Data Structures
- Loading Data into Analytical Databases
1 LiveLab in this lesson — see the labs panel →
02 Enterprise Data Modeling 2 topics · 1 LiveLab +
- Enterprise Data Management
- The Enterprise Data Model
1 LiveLab in this lesson — see the labs panel →
03 Introduction to Analytics 9 topics +
- What’s in a Name?
- Why the Sudden Popularity of Analytics and Data Science?
- The Application Areas of Analytics
- The Main Challenges of Analytics
- A Longitudinal View of Analytics
- A Simple Taxonomy for Analytics
- The Cutting Edge of Analytics: IBM Watson
- Summary
- References
04 Introduction to Predictive Analytics and Data Mining 8 topics · 2 LiveLab +
- What Is Data Mining?
- What Data Mining Is Not
- The Most Common Data Mining Applications
- What Kinds of Patterns Can Data Mining Discover?
- Popular Data Mining Tools
- The Dark Side of Data Mining: Privacy Concerns
- Summary
- References
2 LiveLab in this lesson — see the labs panel →
05 Standardized Processes for Predictive Analytics 8 topics +
- The Knowledge Discovery in Databases (KDD) Process
- Cross-Industry Standard Process for Data Mining (CRISP-DM)
- SEMMA
- SEMMA Versus CRISP-DM
- Six Sigma for Data Mining
- Which Methodology Is Best?
- Summary
- References
06 Data and Methods for Predictive Analytics 13 topics · 1 LiveLab +
- The Nature of Data in Data Analytics
- Preprocessing of Data for Analytics
- Data Mining Methods
- Prediction
- Classification
- Decision Trees
- Cluster Analysis for Data Mining
- k-Means Clustering Algorithm
- Association
- Apriori Algorithm
- Data Mining and Predictive Analytics Misconceptions and Realities
- Summary
- References
1 LiveLab in this lesson — see the labs panel →
07 Algorithms for Predictive Analytics 11 topics · 4 LiveLab +
- Naive Bayes
- Nearest Neighbor
- Similarity Measure: The Distance Metric
- Artificial Neural Networks
- Support Vector Machines
- Linear Regression
- Logistic Regression
- Time-Series Forecasting
- Summary
- References
- Summary
4 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
9 LiveLabs- Designing a Star Schema Fact Table
- Developing an Enterprise Conceptual Model
- Creating a Decision Tree in Python
- Creating a Decision Tree in KNIME
- Running k-Means Clustering Algorithm in KNIME
- Using the k-Nearest Neighbor Algorithm
- Using ANN and SVM for Prediction Type Analytics Problems
- Implementing Linear Regression in Python
- Implementing Linear Regression Model in KNIME
Labs run in your browser — nothing to install.
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