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Skillogic Business Analytics Course
 1. Basic matrix a
 2. Introduction to data mining algebra
 3. Dimension reduction techniques: Principal component Analysis(PCA)
 4. Singular Value Decomposition (SVD)
 5. Association rules
 6. Sequential pattern mining
 7. Recommender Systems (collaborative Filtering)
 8. Network Analytics: Degree centrality, Closeness Centrality etc.
 9. Cluster Analysis- Application on segmentation, anomaly detection
 10. Hierarchical clustering and K-means clustering with various distance
measures and for continuous/ categorical variables
 11. Overview of machine learning/supervised learning
 12. Data exploration methods: Understanding data(distributions,
visualizations), Data nuances, data transformations
 13. Basic classification algorithms
 13.1. Version spaces and decision trees classifier
 13.2. K-Nearest Neighbours and parzwen window
 13.3. Bayesian classifiers: naïve Bayes and other discriminant
classifiers
 13.4. Perceptron and Logistic regression
 13.5. Neur
 14. Advanced classification algorithms al networks
 14.1. Bayesian Networks
 14.2. Support Vector machines
 14.2. Support Vector machines
 16. Multi class classification problem
 17. Bagging(random forest) and Boosting( Gradient Boosted
Decision Trees)
 18. Regression Analysis
 19. Recommendation engines
 20. Information retrieval
 21. Practical tips in modelling: Bias Vs trade off,
Feature engineering and incorporating domain
knowledge.
Call us
USA : +1 646 586 9220
CANADA : +1 613 800 7530
UK : +44 203 5145850
NETHERLANDS : +31 85 888 7820
INDIA : +91 740 660 9000
Email : info@skillogic.com
For more details: http://in.skillogic.com/business-analytics-
training/business-analytics-certification-hyderabad

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Business Analytics Data Mining Syllabus

  • 2.  1. Basic matrix a  2. Introduction to data mining algebra  3. Dimension reduction techniques: Principal component Analysis(PCA)  4. Singular Value Decomposition (SVD)  5. Association rules  6. Sequential pattern mining  7. Recommender Systems (collaborative Filtering)  8. Network Analytics: Degree centrality, Closeness Centrality etc.  9. Cluster Analysis- Application on segmentation, anomaly detection  10. Hierarchical clustering and K-means clustering with various distance measures and for continuous/ categorical variables
  • 3.  11. Overview of machine learning/supervised learning  12. Data exploration methods: Understanding data(distributions, visualizations), Data nuances, data transformations  13. Basic classification algorithms  13.1. Version spaces and decision trees classifier  13.2. K-Nearest Neighbours and parzwen window  13.3. Bayesian classifiers: naïve Bayes and other discriminant classifiers  13.4. Perceptron and Logistic regression
  • 4.  13.5. Neur  14. Advanced classification algorithms al networks  14.1. Bayesian Networks  14.2. Support Vector machines  14.2. Support Vector machines  16. Multi class classification problem  17. Bagging(random forest) and Boosting( Gradient Boosted Decision Trees)
  • 5.  18. Regression Analysis  19. Recommendation engines  20. Information retrieval  21. Practical tips in modelling: Bias Vs trade off, Feature engineering and incorporating domain knowledge.
  • 6. Call us USA : +1 646 586 9220 CANADA : +1 613 800 7530 UK : +44 203 5145850 NETHERLANDS : +31 85 888 7820 INDIA : +91 740 660 9000 Email : info@skillogic.com For more details: http://in.skillogic.com/business-analytics- training/business-analytics-certification-hyderabad