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
This course will cover Alteryx Designer for intermediate and experienced Alteryx users who require more advanced features.
The student is provided with data sources and hands-on exercises to cover the course content. The material provides tips throughout as well as challenges to allow the students to put into practice the concepts taught in each module.
Anyone who currently works with or analyses data, including business analysts, data analysts and developers.
Tell me, show me, and let me do it.
- I have an Excel spreadsheet which has multiple tabs and would like to combine the data from a selection of these tabs and create a new spreadsheet or combine multiple Excel spreadsheets but exclude a specific sheet?
- Do you have to match Customer data using fuzzy match logic?
- Are there times when you have wide data such as survey data, but you need to rearrange it so that you can use it in a BI tool?
- Do you need to download and parse data from the internet?
- Do you have large volumes of database data to process?
- Do you need to use the PSMA Geocoded National Address Files for location and address now that the data has been made public and would you like to store the data in the Calgary database?
- Do you need to parse Java Script Notation text?
- Do you have the need to create and use Macros!
- Have you the need for Time Series Forecasting? Or Predictive Analytics?
Excel Multiple Sheets
Dynamically input Excel data, both with multiple sheets and multiple workbooks with the option to exclude nominated sheets using the following tools:
- Text Input
- Dynamic Input
- Output Data
- Dynamic XSLX
Explore the Fuzzy Match options, Purge and Merge, along with tutorial and challenge, introducing:
- Input Data
- Fuzzy Match
Find out how to prepare survey data for your BI tool, including a challenge, while introducing:
- Text To Columns
- Tableau Workbook Macro
- Tableau TWBX
- Running Total
Download Data and XML Parse
Download data from websites and parse for output. Follow me and challenges introducing:
- Auto Field
- Tool Container
- Multi-Field Formula
- XML Parse
- Cross Tab
- Dynamic Rename
- Dynamic Select
- Data Cleansing
Process data in your database to enhance performance introducing:
- Connect In-DB
- Formula In-DB
- Summarize In-DB
- Data Stream Out
GNAF and Calgary Database
Explore Geocoded National Address file data, both at location and address level and placing that data into a Calgary Database. Retrieving and joining the data to business data, including challenges, introducing:
- Calgary Loader
- Calgary Input
- Calgary Join
How to parse Java Script Notation (JSON) data using:
- JSON Parse
- Create Points
Create and use a macro with challenge, introducing:
- Macro Input
- Detour End
- Macro Output
- Drop Down
- Numeric Up Down
- Check Box
- List Box
Time Series Forecasting
Explore Time Series data and create Time Series Forecast models to compare the results and create the forecast, introducing:
- TS Plot
- TS Compare
- TS Forecast
Investigate new donor data to determine who is most likely to donate, predict and score the results, introducing:
- Basic Data Profile
- Association Analysis
- Contingency Table
- Distribution Analysis
- Field Summary
- Violin Plot
- Create Samples
- Decision Tree
- Lift Chart