Would you like to be able to accurately predict business outcomes?
Want to leverage your Alteryx skills to gain value from predictive models?
Interested in improving your understanding of Machine Learning?
Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
The goal is to go beyond knowing what has happened to provide a best assessment of what will happen in the future.
Register for MIP’s training course ‘Deep dive into Data Science with Alteryx’ to learn more.
This hands-on, 1-day course will teach you:
- Predictive modelling in a business context
- The mechanics of building models in Alteryx and understanding the data science process
- The skills required to solve both regression and classification problems
- Both linear and forest modelling techniques, which underpin most supervised machine learning applications
The training material provides tips from experienced instructors, as well as real-world challenges, to allow you to put into practice the concepts presented in each module.
At the end of this course, you will be equipped with a robust framework to build both linear and classification models using Alteryx and raw data to answer advanced business questions.
This course is aimed at anyone who currently works with or analyses data and is proficient using Alteryx.
This includes business analysts, data analysts and developers who are looking to take the next step in both their theoretical and practical understanding of Predictive Analytics.
Predictive Analytics in a Business Context:
How does predictive analytics fit in amongst other common reporting and analytics:
● Key terms and concepts
● What defines a good predictive model
● The impact of a decision on future performance
Build a Predictive Model in Alteryx:
Practical implementation of a basic linear regression model:
● Alteryx overview and starter files
● Structure of variables and target
● Linear Regression explained
● Other model types
● Alteryx predictive and ML tools
Feature Engineering and Dealing with Missing Data:
How to handle missing and inconsistent data, for better model performance:
● Null values
● One hot encoding
● Label encoding
● Ordinal encoding
● Binary data
● ID and free text fields
● Dates and cyclical data
● Normalising data
Adjustments to the Dataset:
Practical implementation of the above data adjustments.
● Improve dataset
● Variable selections
● Train multiple models
● Implement learnings
● How to measure model accuracy
● Precision and Recall
● Other factors to consider
● Practical implementation of the above theory
● Justification of best model choice Classification Problems
Differences between regression and classification problems:
● Decision trees explained
● Capstone project
Course Wrap-up and Where to from here:
● Capstone project review
● Alteryx Community and next steps
● Further modules to continue your Data Science journey