It contains:

• automation for data cleaning; dirty data is the most common analytics problem
• exploratory analysis & visualizations; learn about the problem space
• descriptive statistics, nonparmetrics; learn and share factoids about the problem to build situational awareness
• linear regression models, nonlinear regression models; estimate the relationships among your variables and create predictive models (machine learning); also use simulated data to create linear regression models and learn something new
• multivariate exploratory techniques; organize data into meaningful clusters, classify variables (reduce/relate variables), principal components & classification analysis
• tabulation options; everyone needs a summary table for their presentation to management

There are two modes of interaction with the analytics; spreadsheet and workspace. The workspace is a visual analytic workflow management tool and is recommended. This allows work to be saved and reused. No coding is needed to complete a workspace. And for the users who need to manage their code, the workspace has a "code node" which can execute C#, Python, or R code. For ad-hoc analysis that does not need to be duplicated, users can import data into a spreadsheet and interact with menus, variables, and rows of data. 

A workspace can:

• import excel, csv, fixed width (mainframe) data
• embed data within workspace as a lookup table; transform "m" to Monday for readability
• import Spotfire SBDF data file and configure analytics (see options below)
• retrieve data from database with ODBC driver and configure analytics (see options below)
• data mashup 
• create visualizations
• format output for reporting
• export results to excel, csv, Spotfire SBDF, etc..
• write results into a database; SQL Server, Oracle, Teradata, SQL Server PDW, PostgreSQL, DB2

2D and 3D visualizations are available with the product; histogram, line, scatterplot, means with error, bag plots, quantile-quantile (beta, exponential, extreme, gamma, lognormal, normal, Rayleigh, Weibull), variability, contour, wafer, normal probability, etc..

Data Profiling, Cleaning, Transformation

The Data Health Check node (data profiling) explores values, value ranges, discrete text lables, missing data, outliers, etc.. on every variable. The results of this analyses is a diagnostic report. This node can also be configured to automate fix the data problems uncovered by the analyses. 

Additional options to transform and clean are available; remove duplicates, recode, rank, merge, process invariant variables, recode outliers, missing data inputation, recode missing data, subset, sample, etc..

Box-Cox is available to transform variables so that they have a distribution as close to normality as possible (Box and Cox, 1964). This allows the use of algorithms, like regression analysis, that only work with a normal distribution. 

Analytics 

• ANOVA/MANOVA
• Calculators; Distributions, Pearson Product Moment Correlation Coefficient, Six Sigma
• Canonical Analysis
• Classification Trees
• Cluster Analysis
• Correlation
• Correspondence Analysis
• Cox Proportional Hazards Models
• Descriptive Statistics
• Discriminant Function Analysis
• Distribution Fitting
• Distributions & Simulation
• Factor Analysis
• Fixed Nonlinear Regression
• General Discriminant Analysis (GDA)
• General Linear Models (GLM)
• General Partial Least Squares Models (PLS)
• General Regression Models (GRM)
• Generalized Linear/Nonlinear Models (GLZ)
• Log-Linear Analysis of Frequency Tables
• Multiple Regression
• Multidimensional Scaling (MDS) 
• Nonlinear Estimation
• Nonparametric Statistics
• Power Analysis and Interval Estimation
• Reliability and Item Analysis
• Stepwise Model Builder (what-if)
• Structural Equation Modeling and Path Analysis (SEPATH)
• Survival & Failure Time Analysis
• Time series / forecasting
• t-tests and other tests of group differences
• Tabulate
• Variance Components & Mixed Model ANOVA/ANCOVA