Data Science

Machine learning & data science for beginners and experts alike.

Best Practices

Encoding Techniques for Machine Learning in Alteryx: A Conci...

In this blog, we will explore a number of encoding techniques and understand the best scenarios for effective application.

Best Practices

2 min to read

Data Science Portal Discussion Highlights, November 2021

November was a busy month as the winter holidays ramped up, and the Data Science Portal was no exception.

Best Practices

11 min to read

Balancing Act: Classification with Imbalanced Data

Feeling a little off balance? Dealing with imbalanced data can be challenging, but we've got a variety of approaches to help.

Best Practices

2 min to read

Data Science Portal Discussion Highlights, October 2021

Don't endure a data science existential crisis alone. Check out October's Data Science Portal discussions for problem solving and advice.

Best Practices

2 min to read

Get Moving With Machine Learning in One Hour

In just an hour, this trio of videos can help anyone become more comfortable and familiar with the tools of machine learning.

Best Practices

2 min to read

Data Science Portal Discussion Highlights, September 2021

The leaves may be changing colors, but the enthusiasm for data conversation on our Data Science Portal is evergreen! Check out last month's highlights.

Best Practices

9 min to read

Dealing with Data Leakage

A plumber can't help you with data leakage. Be prepared to DIY by learning how to recognize and avoid this potentially messy problem.

Best Practices

2 min to read

Data Science Portal Discussion Highlights, August 2021

Beta, Pearson, Spearman, macros and more — last month’s Data Science Portal discussions covered a lot of territory! Here are three interesting conversations that happened in August...

Best Practices

7 min to read

How to Make Word Clouds People Won’t Hate

Haters gonna hate on word clouds. But they're still useful visualizations, and you can make them better.

Best Practices

1 min to read

Feature Engineering 101: Introduction to Featuretools

Construct new features automatically to build better models, save time and avoid the mistakes that can occur in a manual process.

Alteryx Designer

9 min to read

Picture Perfect: Inside Image Processing

Images and scanned documents can be a bit hard to decipher. Let’s see how the Image Processing tool works so you can make the most of your image-based data.

Best Practices

3 min to read

Experts Debunk Data Science Myths

There are plenty of myths to debunk. Let's hear some Alternative Hypotheses!

Alteryx Designer

7 min to read

Notable Nodes: Identifying Influencers with Network Analysis

Identifying social media influencers is both an art and a science. Let’s try out the Network Analysis Tool to find them on Twitter.

Best Practices

10 min to read

The Data Science Celebrity Q&A: Meet P. Value (aka p-value)

Our interviewer sat down with P. Value, the popular but sometimes mystifying statistical calculation, for an exclusive Q&A. Hear its origin story, and learn why its success isn’t j...

Best Practices

8 min to read

Vital Signs: Assessing Data Health and Dealing with Outliers

Learn about the diagnostic powers of the Data Health Tool and find out how to take action on your results, especially outliers you find.

Best Practices

7 min to read

Normalization, Standardization, and Regularization in Altery...

Ever get a little confused by these three terms that sound so similar? Let's get some clarity on these important concepts.

Best Practices

8 min to read

Metric Matters, Part 2: Evaluating Regression Models

Feeling determined, absolute or maximum? Find the right metrics for evaluating your regression models.

Best Practices

15 min to read

Metric Matters, Part 1: Evaluating Classification Models

The metrics you choose to evaluate your models matter, but with so many choices, which one should you select?

Best Practices

7 min to read

Data Normalization: What You Need to Know Before Feature Eng...

Especially ahead of feature engineering, it is important to have an understanding of how and why our data is structured - something called data normalization.

Best Practices

4 min to read

Who’s Curious about ML and AI?

Who's looking into machine learning and artificial intelligence? See where you fit among those exploring these topics.

Best Practices

4 min to read

Looking Forward to 2021 in Data Science and Analytics

Take a look at some of the trends and developments of 2020 that will shape and inspire our work in the year to come.

Best Practices

8 min to read

Market Basket Analysis 102: Alteryx Designer + Python

Ready for your next steps with market basket analysis? Learn how to do it in Designer and visualize your results with Python.

Best Practices

9 min to read

Market Basket Analysis 101: An Introduction

Ever been curious about those "MB" tools? Learn the essentials of market basket analysis in part 1 of our exploration.

Best Practices

7 min to read

Expand Your Predictive Palette V: No More Black Box with LIM...

Stuck in the middle between model accuracy and interpretability? Trying to break down the black box? One famous package is called LIME.

Alteryx for Good

5 min to read

Data Science Volunteering: Ways to Help

Contribute your data expertise to a good cause through one of these opportunities.

Alteryx Designer

3 min to read

De-Confusing the Matrix

“Confusion Matrix” is not just a term for how we all felt after watching the Wachowski’s masterpiece for the first time; it's also a way of looking at the effectiveness of a model’...

Best Practices

5 min to read

Predicting Success for College Classrooms and Football Field...

Each student’s journey through a higher education institution creates lots of data. Use it to build models that support institutional and student success.

Best Practices

9 min to read

What Are Humans Good For? Automation and Ingenuity

Insights from our Twitter chat show how automated processes free up human time and creativity. Maybe they'll also make us better people.

Best Practices

8 min to read

Interpretability, Explainability, and Machine Learning

How do we prioritize accuracy, interpretability and explainability in the development of models? Is there always a tradeoff?

Best Practices

7 min to read

Visualizing Automated Feature Engineering

To make it easier to understand how a feature was generated automatically, Featuretools now has the ability to graph the lineage of a feature.