Competitive Intelligence

Tactical, Operational & Strategic Analysis of Markets, Competitors & Industries

Edgar De Monte Furtado
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  • Belmont, MA
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About Me:
Conduct data mining, data modeling, statistical analysis, business intelligence gathering, trending and benchmarking. Data analytics supports decisions for high-priority, enterprises initiatives involving IT/Product development, Customer service improvement, organizational realignment and process reengineering.

Key Achievements:
• Data Mining and Modelling: Collected, cleansed and provided modeling and analyses of structured and unstructured data used for major business initiatives. Outcomes:
• A 15% reduction in transportation costs, resulting in $1.2M in annual savings
• Improved demand forecasting that reduced backorders to retail partners by 17%
• Completed focus group and BI research that helped New England region sales by 15%
• Dashboards: Created visually impactful dashboards in Excel and Tableau for data reporting by using pivot tables and VLOOKUP. Extracted, interpreted and analyzed data to identify key metrics and transform raw data into meaningful, actionable information.
• eCommerce: Designed and built statistical analysis models on large data sets (e.g; Aster, Teradata) that helped increase online sales (up to 15% per product) and lowered cart-abandonment rate by 23%
My Interest in Competitive Intelligence is:
12 Important Algorithms

1. Regression Algorithms

 Ordinary Least Squares Regression (OLSR)
 Linear Regression
 Logistic Regression
 Stepwise Regression
 Multivariate Adaptive Regression Splines (MARS)
 Locally Estimated Scatterplot Smoothing (LOESS)

2. Instance-based Algorithms

 k-Nearest Neighbour (kNN)
 Learning Vector Quantization (LVQ)
 Self-Organizing Map (SOM)
 Locally Weighted Learning (LWL)

3. Regularization Algorithms

 Ridge Regression
 Least Absolute Shrinkage and Selection Operator (LASSO)
 Elastic Net
 Least-Angle Regression (LARS)

4. Decision Tree Algorithms

 Classification and Regression Tree (CART)
 Iterative Dichotomiser 3 (ID3)
 C4.5 and C5.0 (different versions of a powerful approach)
 Chi-squared Automatic Interaction Detection (CHAID)
 Decision Stump
 M5
 Conditional Decision Trees

5. Bayesian Algorithms

 Naive Bayes
 Gaussian Naive Bayes
 Multinomial Naive Bayes
 Averaged One-Dependence Estimators (AODE)
 Bayesian Belief Network (BBN)
 Bayesian Network (BN)

6. Clustering Algorithms

 k-Means
 k-Medians
 Expectation Maximisation (EM)
 Hierarchical Clustering

7. Association Rule Learning Algorithms

 Apriori algorithm
 Eclat algorithm

8. Artificial Neural Network Algorithms

 Perceptron
 Back-Propagation
 Hopfield Network
 Radial Basis Function Network (RBFN)

9. Deep Learning Algorithms

 Deep Boltzmann Machine (DBM)
 Deep Belief Networks (DBN)
 Convolutional Neural Network (CNN)
 Stacked Auto-Encoders

10. Dimensionality Reduction Algorithms

 Principal Component Analysis (PCA)
 Principal Component Regression (PCR)
 Partial Least Squares Regression (PLSR)
 Sammon Mapping
 Multidimensional Scaling (MDS)
 Projection Pursuit
 Linear Discriminant Analysis (LDA)
 Mixture Discriminant Analysis (MDA)
 Quadratic Discriminant Analysis (QDA)
 Flexible Discriminant Analysis (FDA)

11. Ensemble Algorithms

 Boosting
 Bootstrapped Aggregation (Bagging)
 AdaBoost
 Stacked Generalization (blending)
 Gradient Boosting Machines (GBM)
 Gradient Boosted Regression Trees (GBRT)
 Random Forest

12. Other Algorithms

 Computational intelligence (evolutionary algorithms, etc.)
 Computer Vision (CV)
 Natural Language Processing (NLP)
 Recommender Systems
 Reinforcement Learning
 Graphical Models
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