For more information, see the online learning platform

DATAmaestro is a cloud-based application, which makes getting started faster and easier. Its intuitive design provides a comfortable learning environment. The following sections describe how to navigate and use the work area.

Change password
User Settings
DATAmaestro Analytics Menu and Sidebar
DATAmaestro Analytics Display Options
User Guide Conventions

For more information, see FAQ.

Change password

We recommend changing your password at the first connection and then on a regular basis.

Open the user menu in the top right corner
Click Change password.
Enter your existing password, then enter your new password twice. You must use a combination of characters (min 8 characters using upper/lower/numbers).
Click Change password to confirm

For DATAmaestro deployments with a separate server for DATAmaestro Lake from Analytics+Dashboards, you must do this on the DATAmaestro Lake server.

User Settings

Your user privileges in DATAmaestro determine what you can customize in your work environment.

Multilingual DATAmaestro

To help users around the globe, we are progressively rolling out multilingual DATAmaestro.

A first beta-version is ready in French and Japanese.

Under the user menu, select “Language” to set your preference.

DATAmaestro Analytics Menu and Sidebar

In DATAmaestro Analytics, the main features are organized in a project workflow in the top menu. To access your saved project tasks, open the sidebar on the left.

The Analytics Menu

Items in the menu bar help you manage your projects through every stage.

Menu Item	Description
Project	Create, open, delete or copy a project. You can also set preferences to customize the page appearance.
Data	Manage the data sources that support your projects. You can upload CSV or Excel files, or use data connections like AllegroGraph or DATAserver. Excel plugin To upload Excel files you must install a plugin, contact Technical Support.
Select	Create a new Record set or Variable set. For more information, see Data.
Transform	Use operators to create or edit Function Variables expressions, as well as process control features including CUSUM, Statistical Process Control (SPC), and Principal Component Analysis (PCA).
Visualize	Create histograms, scatter plots, box plots, trends and dendrograms based on data you select.
Models	Use learning and test sets to build automatic learning models. Train your data using robust classification, regression and clustering techniques.
Reports	Export subsets of your data to use for reporting purposes outside of DATAmaestro.
Help	Find answers to questions in the collection of user references and tutorials.
User Menu	Switch between Lake, Analytics or Dashboards, Change password, change Language preferences, and more.

The Analytics Sidebar

In DATAmaestro Analytics, use the sidebar to access the elements that have been saved in your project. Elements are saved with their associated feature, which is represented by a grey icon. Hold your mouse pointer over an icon to see the name of the feature and the number of elements that are saved. For example, histograms:

Icon	Active	Description
		The current project has nine saved histograms.

To expand and collapse all tasks using the sidebar, click All at the top of the sidebar. The expanded panel displays all the saved elements for each feature.

Expanded Sidebar Panel

Sidebar Controls

Search: Use the search field at the top to find saved elements.

Pin: To pin the sidebar panel and view all of the current page, click ().

Open: To open an element, click the name in the sidebar.

New: To create a new element, click Add new... in the appropriate section.

Manage: To edit () or delete () elements, click the appropriate icon.

Deleting and data dependency

If the element you want to delete is used by another task, the title of the task is displayed in the dialog box for you to confirm. If you delete the element, the depending tasks are also removed. For example, if you confirm and delete a function variable that is used for a histogram, a scatter plot and a decision tree, they will all be removed.

Sidebar Icons

Icon	Name	Description
	Data sources	Data source(s) uploaded to support the project. The number of files is indicated on the active icon.
	Variable sets	Variable sets that have been created for the project. By default, all the individual variables appear in the Variable List.
	Record sets	Record sets are created to establish specific record groups for a given data source. For example, records that correspond to a production regime, records associated with the normal variation range of a KPI variable.
	Function variables	Function variables are created to transform variables for models and advanced analysis.
	Histograms	Histograms are typically used to illustrate the process distribution and are used to make predictions about a stable process.
	Scatter plots	Scatter plots are typically used to show how much one variable is affected by another. Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X- and Y-axes.
	Box plots	Box plots provide effective views to help identify outliers. They show a data set’s lowest value, highest value, median value, and the size of the first and third quartiles. The box plot is useful in analyzing small data sets that do not lend themselves easily to histograms.
	Trends	Curves are typically used to compare the temporal dynamic behaviour/trend of two or more numerical variables of a database.
	Dendrograms	Dendrograms and Correlation matrices provide a means of assessing dependency levels between variables. A dendrogram is a tree-structured graph used in heat maps to visualize the result of a hierarchical clustering calculation.
	Linear regressions	A simple linear regression is an approach to modeling the relationship between a scalar dependent variable Y and one explanatory variable denoted X. Several input variables may be combined to predict an output variable Y in the shape of a multi-linear regression.
	Trees	Decision trees and Regression trees in are important modeling methods that are used to accomplish a prediction objective (classification or regression).
	Ensemble trees	Ensemble trees are an extension of the regular tree methods; Extra Trees, Adaboost & MART.
	Clustering	K-Means and Subclu models in the project.
	Other tasks	All other items can be found under the Other menu, including models: Artificial Neural Networks, Partial Least Squares, k-Nearest Neighbors, Partial Dependence Plots, Sensitivity Analysis, Dynamic Inputs, PRIM analysis & Optimizer, Statistical tests: T test, Spearman and Pearson, as well as, Data Exports, Summary Charts, Process Flow and more.

DATAmaestro Analytics Display Options

The colour pallet, fonts and other display options can be modified to suit your preference. To change the display settings and work environment in an Analytics project:

Click Project > Preferences in the menu.
Click Add Entry and then enter a Symbol and Color.
Click Save, or on icon
to delete a preference.

User Guide Conventions

The conventions used in this document are described below.

Element	Example	Description
Bold text	Click Compute to see the revised calculation.	A clickable item or button.
Lesson highlight	Lesson To use the data from ...	Lessons and additional information to enhance your learning.
Note highlight	Tip To find an variable, use the search field.	Notes and tips to speed your process provide alternative methods.
Screen captures		Example screen captures used in this document are from the Home Energy tutorial.

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The Work Area

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