Alteryx Masterclass: Advanced Data Analytics Training

Explore in-database processing in Alteryx, blending and analyzing large data sets inside the source database without moving data, using 64-bit drivers and visual SQL queries.

Master data blending with join and union tools to combine multiple sources, including in-database and external data, using inner, left, right, and full outer joins.

Utilize in DB versions of select, summarize, and formula tools to group, aggregate, and compute fields within the database, then stream results to designer tools via a data stream out.

Learn to level up your apps and macros by building an iterative macro in Alteryx, using interface tools like a dropdown, and looping to compute total payout.

Learn to build an iterative Alteryx macro that uses a filter on the iteration number to stop at the max year, calculating payout with interest and mapping iteration data.

Explore batch macros in Alteryx, including how batch macros run for each supplier, configure inputs with a control parameter, and generate pdf reports per supplier and category.

Learn to build robust alteryx apps with error message and condition tools, use constants (engine, question, user), mask password input, validate credentials, and log outcomes.

Learn to design and customize Alteryx apps and macros with the interface designer, including grouping questions, container toggles, test and debug workflows, and organized, wireless connections.

Explore Alteryx Intelligence Suite's assisted modeling to build a machine learning pipeline for classifying milk grade, compare algorithms, manage data types and missing values, and deploy with the Predict tool.

Explore data health and feature types in the Alteryx intelligence suite to prepare data for modeling by assessing missing values and outliers and applying semantic data typing.

Combine customer and transaction data with build features and feature types to create predictive features for a credit score classification model, evaluated by AutoML with random forest and Xgboost.

Explore the transformation, fit, and predict tools in an assisted modeling workflow to prepare data, set data types, handle missing values, and encode features for classification and regression models.

Build a machine learning pipeline from raw data to a model using regression and classification tools, one-hot encoding, data prep, training and testing, and evaluation with linear regression and XGBoost.

Auto field data, tag nationality with regex, save to Calgary database, divide 480 students into four tiles, compute running total of raised hand, and create tile_num, tile_sequence_num, and run_total_raised_hand indexes.

Import the sql database and join three tables in database tools, remove duplicates, and compute the average users per year; also export 2019 questions to excel.

Master batch macro design in Alteryx to summarize online retailer sales by country, filter by country, and output country-specific CSV files using macro inputs and control parameters.

Build an XGBoost classification model to predict airline passenger satisfaction, applying data type corrections, missing value imputation, and one-hot encoding on training and testing data.

Train a house price regression model using an assisted modeling tool, from data type preparation and 60/40 sampling to feature selection and comparing models, with random forest delivering best accuracy.

Explore the fundamentals of Alteryx for data integration and analytics, and gain practical insights into using this powerful tool for advanced data analytics.