Learn data science best practices.


Carol HargreavesFebruary 14, 2019

6 Steps to Building a Powerful Customer Analytics System

Carol Hargreaves, director of the data analytics consulting centre at the University of Singapore, presents six steps to building a powerful customer analytics system.

Amit MishraFebruary 11, 2019

Fusing Design Thinking and System Engineering for Data Science

Innovator and educator Amit Mishra, Ph.D. presents a methodology that fuses Design Thinking and System Engineering that can increase the success of a data science project.

Sebastian NeubauerJanuary 28, 2019

Why Is It so Hard to Put Data Science in Production?

Is data science helping your company build the systems that automate operational decisions? If not, Senior Data Scientist Sebastian Neubauer believes that adopting a DevOps mentality in your data...

Shashank Shekhar RaiJanuary 17, 2019

5 Data Cleaning Tips to Test Assumptions

In this post, machine learning practitioner Shashank Shekhr Rai offers five tips that any data scientist or analyst can use as data checks and a way to second guess any assumptions that may creep...

Gyasi DapaaJanuary 16, 2019

Target Twisted: Avoid Creating Biases in Loss Cost Modeling

Actuarial rate-making thought leader Gyasi Kwabena Dapaa explains why actuarial trend rates are not suitable for trending target loss variables of insurance predictive models.

Vikram ReddyJanuary 14, 2019

A Business Perspective to Designing an Enterprise-Level Data Science Pipeline

Oracle Senior Data Scientist Vikram Reddy walks through a case study and illustrates key things to keep in mind when designing a data science pipeline.

Mischa FisherJanuary 9, 2019

10 Common Data Science Pitfalls to Avoid

Chief Economist Mischa Fisher takes his role in overseeing several Illinois state agencies and shares the ten common data science pitfalls that are universal to every data practice.

DataScience StaffJanuary 9, 2019

Data Science Concepts From A to Z: A Quickstart Guide for Non-Technical Professionals

Beyond technical challenges, misalignment between data science and business teams is one of the most common reasons data projects fail. Learn the lingo of data science — and improve outcomes — with...

Saras YagnavajhalaJanuary 7, 2019

Making the Case for Centralized Data Science

Oracle Hospitality Senior Director of Data Science Strategy Saras Yagnavajhala shares how a centralized approach to data science can help break organizational silos and drive measurable and scalable...

John MillerDecember 20, 2018

How to Make Sure Your Machine Learning Model Holds Up In Court

Consider these tips to help improve the odds that your machine learning model will be used in a business setting.