Alteryx Training

In this section, you will be introduced to the fundamental concepts of AlterYX training. 

Topics covered: 

• Understanding Alteryx Designer
• User and Workflow settings
• Alteryx designer Interface Learning Outcome: By the end of this section you will gain complete knowledge of the applications of AlterYX, its usage, etc. 

Alteryx Designer is a unique platform that allows you to process large sets of data at a faster rate without using any code. It eliminates the long process associated with data processing and gets the results in hours rather than weeks. In this section, you will master all the concepts of AlterYX designer and come to know how it works in real-time. 

Topics covered: 

• Tool Palettes
• Favourite Palettes
• Configuration
• Workflow Canvas

Learning Outcome: Upon the completion of this section you will become familiar with overall concepts of AlterYX designer. 

We need to join data from various sources to construct a valid report or new data set. This section is designed to teach you how to join data from different sources.

Topics covered: 

• Filter
• Browse
• Summarize
• Join Multiple
• Union
• Join 
• Formula
• Input Data
• Output Data
• Generate Row
• Data Filter

Learning Outcome: By the end of this section you will be able to use all the above elements to join the data from various sources. 

This section is designed to provide you with complete knowledge of data restructuring concepts. Data restructuring is a process of modifying the structure of source data to align with the target data. 

Topics Covered: 

• Split data, skip records,  and pivot and unpivot data 
• Trim and split fields, input data, and text to columns
• DynamicRename, Transpose
• Cross Tab, Directory, Comment and Sample
• Select, Tool Container, Output Data,  Filter
• Record Id, Output Data, Unique 

Learning Outcome: Upon the completion of this section, you will gain complete knowledge on data extraction, filtering,  cleansing, and join the data across various sources in AlterYX. 

Data handling is a process in which you need to identify the missing, appended, and skipped data with the help of AlterYX tool.  This section makes you expert in handling the data. 

Topics covered: 

• Impute Values
• Formula
• Data Cleansing
• Append Field
• Unique
• Random Sample
• Running tool

Learning outcome: By the end of this session, you will be able to handle all data-related activities and be able to make data ready to use.  

This section deals with the process to prepare statistical reports in AlterYX. 

Topics covered: 

• Table
• Layout
• Charts
• Datetime Now
• SortEmail
• Resort map
• Image

Learning outcome:  After completion of this section, you will be able to create static reports, as well as charts, maps, and texts.   

Spatial analysis is a technique applied to structures, notably for the analysis of geographical data. This is highly useful to optimize sales, logistics and services in a specific geography.

Topics covered: 

• CreatePoints
• FindNearest
• SpatialMatch
• Trade area
• Special info
• Distance

Learning outcome: This section will give you the practical experience on how to make business decisions using geospatial data.  And also you will learn to optimize the logistics, sales, or cover areas to deliver better services.

This section deals with the Regex tools in AlterYX, such as parsing, matching, replacing, and tokening. 

Topics covered: 

• List Box
• CheckBox
• Numeric UpDown
• Action
• Control Parameter
• File Browse
• Image
• Radio Button

Learning outcome: After completion of this module, you will get familiarity with Regex tools in AlterYX.  You will also gain hands-on expertise in creating analytic applications.  

This section is designed to make you knowledgeable in developing Analytical applications using the AlterYX tool. 

Topics covered:

• List Box
• CheckBox
• Numeric UpDown
• Control Parameter
• Image
• Action
• File Browse
• Radio Button

Learning Outcome: You will gain practical knowledge and process to be followed to build an Analytical app. 

A Macro is a workflow built into a single tool that can be injected into other workflows. Macros save you from repeated analytical processes. 

Topics covered: 

• Create standard macro
• Macro Input
• Batch macro
• Macro Output
• Dynamic Input
• Dynamic Rename
• Action
• Control Parameter

Learning Outcome: After completing this session you will be able to create a Macro that goes to every record and then loops the record back with workflow. 

This chapter is designed to teach you the process to Join and blend data. 

Topics covered: 

• Inputting data
• Select
• Unique
• Union
• Browse
• Formula 
• FuzzyMatch
• Filter
• Auto Field

Learning Outcome: you will gain hands-on experience in using AlterYX tools such as filter, sample, sort, select, and more. 

In this section, you will learn how to use Database connections from a central location. Data connection widow allows you to see the database connections you have created, shared connections with you, and also new connections added. 

Topics covered: 

• Join In-DB
• Data Stream Out
• Data Stream In
• Connect In-DB
• Select In-DB
• Formula In-DB
• Filter In-DB
• Summarize In-DB

Learning outcome: You will come to know the process of how to blend massive amounts of data without moving data out of the database. Besides this, you will also learn traditional procedures to move data out of the database for processing.

This module will teach you how regression techniques will be useful in handling analytics and data visualization processes. 

Topics covered: 

• Score
• Lift Charts
• Visualization
• Linear regressions
• Nested test
• Stepwise Regressions
• Logistic Regressions
• Decision tree

Learning Outcome:  you will get an overview of How regression techniques are used in data analysis and visualization investigation. 

This section deals with the clustering concept in AlterYX.  Clustering helps in dividing the data points into groups based on certain variables. 

Topics:

• K-Centroids Analysis
• Append Cluster
• Visualization

Learning outcome: upon the completion of this chapter, you will come to know how to partition records into “K groups”  around centroids with the help of KMedians, K-Means, or Neural Gas clusterings.