Tag: Statistical Modeling

Introduction

Overview of TIBCO Statistica Features

• Interactive UI and workflow UI
• Analytic facilities and unique features
• Statistica Help

Getting Started with TIBCO Statistica

• Installation
• MS Office integration
• Selecting a data file
• Templates

Exploring Data Analysis Capabilities

• Commonly used statistical tests
• Elementary concepts (variables, measurement scales, relations, etc.)
• Statistical Advisor feature

Exploring Statistics Basics

• Correlations and ANOVA
• Variable bundles
• By-group analyses
• Summary results panels

Managing Data Using TIBCO Statistica

• Spreadsheet formulas
• Workspace nodes
• Managing output
• Printing, importing, and exporting

Managing Statistica Settings and Projects

• Project actions
• Customizing settings (toolbar, menus, commands)

Working with Spreadsheets, Reports, and Macros

• Using spreadsheets and workbooks
• Creating graphs and reports
• Working with macros

Exploring Advanced Analytics Topics

• Statistica Data Miner (SDM)
• Machine learning
• Engineering applications
• Design of Experiments (DOE)

Summary and Conclusion

Introduction

Stata and Big Data

• What is Stata?
• Stata syntax and commands

Preparing the Development Environment

• Installing and configuring Stata

Datasets and Data

• Opening and cleaning datasets
• Compressing datasets
• Importing and exporting datasets
• Viewing, describing, and summarizing raw data
• Using tabulations and tables
• Working with distributional analysis
• Implementing variables for data manipulation
• Saving data
• Working with commands

Graphing in Stata

• Using plots, charts, and graphs
• Working with distributional analysis in graphing
• Styling and combining graphs

Statistics and Regression

• Using bivariate correlation and regression
• Working with OLS regression, logits, and probits
• Using interactive effects in regression models

Summary and Conclusion

Day One: Language Basics

• Course Introduction
• About Data Science
  • Data Science Definition
  • Process of Doing Data Science.
• Introducing R Language
• Variables and Types
• Control Structures (Loops / Conditionals)
• R Scalars, Vectors, and Matrices
  • Defining R Vectors
  • Matricies
• String and Text Manipulation
  • Character data type
  • File IO
• Lists
• Functions
  • Introducing Functions
  • Closures
  • lapply/sapply functions
• DataFrames
• Labs for all sections

Day Two: Intermediate R Programming

• DataFrames and File I/O
• Reading data from files
• Data Preparation
• Built-in Datasets
• Visualization
  • Graphics Package
  • plot() / barplot() / hist() / boxplot() / scatter plot
  • Heat Map
  • ggplot2 package (qplot(), ggplot())
• Exploration With Dplyr
• Labs for all sections

Day Three: Advanced Programming With R

• Statistical Modeling With R
  • Statistical Functions
  • Dealing With NA
  • Distributions (Binomial, Poisson, Normal)
• Regression
  • Introducing Linear Regressions
• Recommendations
• Text Processing (tm package / Wordclouds)
• Clustering
  • Introduction to Clustering
  • KMeans
• Classification
  • Introduction to Classification
  • Naive Bayes
  • Decision Trees
  • Training using caret package
  • Evaluating Algorithms
• R and Big Data
  • Connecting R to databases
  • Big Data Ecosystem
• Labs for all sections