We are well versed across a broad range of topics in Data Science. Reach out today to learn more about the following:

File Organization and Database Management

Data Models, Relational Algebra, SQL, Normal Form, B Trees, Python, XML, NoSQL, MongoDB

Experimental Statistics

Inference Using t-Distributions, Data Screening, Assumptions and Transformations, Linear Combinations and Multiple Comparisons, Correlation and Scatterplots, Simple Linear Regression, Regression Diagnostics, Multiple Linear Regression, Model Selection and Validation

Base SAS Programming for Database Marketing

SAS Syntax, SAS Data Sets, Validating and Cleaning Data, Manipulating Data, Enhancing Reports, Intro to Graphics, Controlling Input/Output, Summarizing Data, Data Transformations, Debugging Techniques, Iterative Processing

Business Intelligence Tools and Technologies

Data Mining, Data Warehousing, Visualization and Dashboarding, Text Mining and Web Analytics, Data Derivation, Reduction & Selection using R, Multiple and Linear Regression, Unstructured Mining, Clustering, Neural Networks, Tableau, IBM SPSS Modeler

Descriptive Business Analytics

Analytics Process, Types of Data, Types of Variables, Sampling Methods, Confidence Intervals, Basics of Marketing, SAS JMP, Discrete Random Variables, Normal Distribution, Quantiles, Box-plots, Histograms, Stem and Leaf, Central Limit Theorem, Hypothesis testing, Two-sample t-tests, Correlations, Simple and Multiple Regression

Big Data Advanced Analytics Technologies

Apache Hadoop Ecosystem, Teradata Aster, Stream Mining, IBM Watson, social media analytics, link analysis

Advanced Marketing Research Analytics

Multiple Regression advanced topics, MANOVA topics (basics), MANOVA topics (advanced), Survival Data Mining: A Programming Approach, PCA and EFA topics, Network Analysis Using the NETWORK Procedure in SAS Viya, Multiple Discriminant Analysis, Bayesian Analyses Using SAS, Perceptual mapping techniques, Conjoint analysis techniques

Advanced Business Analytics

Data Assay, Tactical vs. strategic models, use case of customer revenue risk, Survival data analysis, Loyalty analytics, Econometric business models, An overview of matrix algebra, Eigen values and Eigen vectors, matrix decomposition, Deep neural networks, Convolutional neural networks (CNN), Recurrent neural networks (RNN), Lecture on deep learning using structured data, RNN using Google Tensor Flow, Special Training Sunday on SAS Viya, Survival Data mining, Variable selection and variable reduction advanced techniques (PCA, PLS, LARS, Lasso etc.), Advanced Predictive Modeling (Support Vector Machines (SVM), Gradient Boosting, Random Forest, Nearest neighbor etc.), Advanced Predictive Modeling (Incremental Response or Net Lift Model and Two Stage Models)

Marketing Optimization

Mathematical programming/optimization/OR, Ad spend optimization, Duality & sensitivity analysis, Service workforce Planning, ILP/MILP, Branch & Bound Concepts, Cutting Plane method, The Diet Problem, GYM Equipment Manufacturing Problem, Modeling Logical Constraints, Debugging infeasibility, Network Flow Models and Integrality, Non-linear programming (NLP), GYM: Price optimization