Machine Learning Product Strategy: Defining a New Category
Machine learning research creates solutions to long standing business problems, driving the need to create new categories. Product strategy needs to be built for the new category and adapted for a model-based product.
February 9th, 2021
Why Do Models Drift?
Data drifts. Models lose accuracy. They require monitoring to detect drift and retrain to prevent failures. Why? Let me explain in non-math terms.
February 5th, 2021
Framing a Business Problem as a Data Science Problem
Is the business problem a data science problem? Reframing the business problem answers this question and allows you to assess the feasibility of your independent project. This is a key step to move past toy projects and build one that gets you hired.
January 21st, 2021
Monetizing Your Data Science Content: Creating a Unique Position in the Space
What makes some people stand out to gain thousands of followers? There is something special about successful content creators. Capture that so you can start making money from your content.
January 20th, 2021
How to Earn Money from Your Free Data Science and Machine Learning Content
You are building useful content and giving it to the community for free. You deserve to get paid for your efforts. It is difficult so I am launching a series to teach you what has worked for me and other content creators.
January 19th, 2021
Data Science Independent Project 1: Building a Machine Learning Business Case
Building a business case for a machine learning project sets candidates apart from the crowd. This project proves your business acumen and ability to build solutions based on a business problem.
January 12th, 2021
18 Independent Projects That Get Aspiring Data Scientists Hired
An independent portfolio of projects helps aspiring Data Scientists break into the field. I outline 18 project categories that will showcase your ability to contribute from day 1.
January 7th, 2021
How to Write A Data Science Resume
The best data science resumes have a lot in common. Here is a detailed guide to building an effective resume.
December 22nd, 2020
Machine Learning Basics. What to Learn, Before You Learn About Data Science.
Here are some specific sources to get started learning the field. I will keep these updates going forward.
December 21st, 2020
The Interview Bank: Kruthika Simha, ML Engineer at Apple
In today’s interview, we talk with Kruthika Simha about her journey into ML, life at Apple, and navigating the job market as a student. The interview has been paraphrased for brevity.
November 13th, 2020
The Essentials of Data Science Leadership – What Makes a Good Leader?
Data Science Leadership is rarely covered and needs more attention. A capable leader is the difference between teams who produce value and those that cannot. Here are the traits of a great leader and how they build a better team.
October 28th, 2020
Introduction to Adversarial Machine Learning in Practice
It is difficult to find an entry point into Adversarial Machine Learning. This tutorial takes you from AML’s foundations to the current day.
October 23rd, 2020
Introduction to Unit Testing for Machine Learning
Unit testing for machine learning has different objectives than software developers are used to. Unit tests add stability to the data pipeline and extra granularity to model validation.
October 23rd, 2020
Adversarial Machine Learning: A Horror Story
Our field has discussed security threats to machine learning for six years. We have built solutions rapidly over the last year. However, we still have a long way to go.
October 20th, 2020
Learning to Think Like a Data Scientist Part 2
Many data science projects fail in the execution phase, never making it into production. In Part 2, Esther Richler explains how to implement her initial design and build a working product.
October 19th, 2020
How to Answer a Software Development Best Practices Question in a Data Science Interview
Best practices of software development are a sign of maturity. You must be able to answer these questions to show your capability as a Data Scientist to build for production.
October 18th, 2020
How to Answer Questions About GPUs, Optimization, and Scaling in a Data Science Interview
Optimization and scaling are core competencies of Applied Machine Learning. If you can cover these topics well, you will stand out from the crowd.
October 18th, 2020
How to Answer Questions about Statistical Significance and Model Selection in a Data Science Interview
Model selection questions require advanced answers that focus on your process for Applied Machine Learning. A theoretical answer will not get you hired.
October 13th, 2020
How to Answer a Data Science Interview Question About Sampling
Answering an interview question is different from answer a question in class or most other settings. The point of your answer is to get you hired. A textbook answer will not get you hired. Getting hired requires you to stand out.
October 9th, 2020
How to Answer a Statistics Questions in a Data Science Interview
Your answer to an interview question aims at getting you hired. Everyone will tell you what to memorize. Everyone knows memorization does not get you hired. You need to know how to answer a statistics question.
October 8th, 2020
Can Machine Learning Promote and Improve Diversity in Hiring?
Businesses are setting challenging diversity targets. HR and Recruiting teams are asking their software vendors, “Can Machine Learning build a more diverse talent pool?”
October 7th, 2020
Learning to Think Like a Data Scientist Part 1
Esther Richler made the transition from academia to data science. She realized her mindset needed to change. She shares her journey into applied data science.
October 2nd, 2020
The 5 Key Elements of Data Quality
Data quality is talked about but poorly defined. In this post, Avanti Chande Sr. Data Scientist at Walmart, presents a simple framework for evaluating data quality.
October 1st, 2020
AI for HR and Recruiting: A Machine Learning Skeptic’s POV.
What works and what is overhyped? I build machine learning based products. At the same time, I advise most companies to steer clear of buying machine learning based products. Here is why.
September 2nd, 2020
Building a Better Machine Learning Team
Machine learning has become a larger piece of business models. Revenue is increasingly dependent on more complex projects. The new Machine Learning Team is built to execute.
August 15th, 2020
Machine Learning for the Return to Work – Reskilling Using ELV Models
Decisions about employees and reskilling are obviously strategic but difficult to quantify and support. Employee Lifetime Value models make raw data actionable to align reskilling with the business strategy.
June 9th, 2020
Privacy Concerns in The COVID Business Climate
COVID created new data markets that are poorly understood. There is opportunity for businesses and threats to individual privacy.
June 2nd, 2020
What’s Happening With Data Science Hiring?
We have all seen the immediate impacts of COVID. What has been the impact on data science hiring?
May 12th, 2020
Data Wrangling for Machine Learning Professionals Part II – Data Privacy and Security
Security and privacy are completely overlooked during the data wrangling process. Expert machine learning teams need to incorporate these into the earliest phases of the model development lifecycle.
April 15th, 2020
Data Wrangling for Machine Learning Professionals Part I – Sourcing and Validation
Enterprise machine learning leans heavily on quality data. The earliest stages of data gathering and wrangling have a huge impact on model performance. Here is how to do sourcing and validation right.
April 11th, 2020
How to Break into Data Science – Analyzing Career Paths, Qualifications, and Success Factors
What career path is most likely to help you break into the field? I analyzed over 24,000 active data scientists’ way in.
February 28th, 2020
What to Do When You are Laid Off
Layoffs are happening due to COVID. Even back in December and January, the first signs of workforce reductions were here. There are some simple steps that will help you land on your feet.
January 21st, 2020
Busting the Garbage Metrics That Drive Our Hiring Hot Mess
A lot of what is broken about hiring is driven by poor metrics. Here is a data driven approach to hiring metrics.
January 12th, 2020
There is No Leadership in Technology
I have analyzed data on thousands of leadership positions in and out of technology. We have a drought of leaders in tech which is a core reason so many companies are not seeing the ROI they should from technical teams.
December 23rd, 2019
Five Questions to Build Your AI Product Strategy Around
Product strategy for machine learning is a critical success factor and an area that is poorly understood. Here is my guide to get started.
October 14th, 2018
What Does Usability Mean for Machine Learning Based Products?
Usability. Done well, it differentiates a machine learning based product and drives adoption. With so many poorly designed products in this space, it is the biggest opportunity for new products in the market.
October 4th, 2018
What Soft Skills Does A Data Scientist Need to Succeed?
Soft skills are just as important as technical capabilities. At the entry level, the basics are enough. However, taking the next steps in your career require soft skills.
September 19th, 2018
Hiring Is Broken Pt. 1: The Candidate Experience Is Inexcusable
Hiring is a broken process that misses the best candidates. There are fixes that can be put in place, but only after we have confronted the problem.
September 5th, 2018
How I Figured Out What I Wanted to Do in Data Science and You Can Too
I did not have a straight-line path in the data science field. Here is how I got started and how I decided what focus my career on.
July 23rd, 2018
The Data Science Lifecycle
For companies moving forward into the next phase of data science maturity, a consistent lifecycle is needed. This is the blueprint for Enterprise Data Science.
May 3rd, 2018
What You Need to Learn, Before You Learn Simple Linear Regression for Data Science
Data Science is a big field and diving straight into regression is not the right way to go. There are areas to study first and here is a better starting point into the field.
April 3rd, 2018
4 Critical Success Factors for Data Science in The Enterprise
I have taught companies about Enterprise Data Science for almost five years. This concept explains how to transition from early stages in the Machine Learning Maturity Model to advanced applications.
January 30th, 2018
Communications for Data Scientists
Soft skills are overlooked in data science, but they are just as critical for a successful data scientist.
December 3rd, 2017
How to Get Past the HR Filter For Data Scientists
The HR filter is the first layer for an aspiring data scientist to break through. Here is how to start getting more call backs and interviews.
October 27th, 2017
Solving the Data Science Skills Gap
The concept of a farm system to feed the business’s need for emerging talent is something I have been teaching clients for three years. It reduces both time and cost to hire.
October 23rd, 2017
New Roles for The New Business Reality - Data Science Product Manager
Three years ago, this was an emerging role. As companies move forward in the Machine Learning Maturity Model, the Data Science Product Manager role is a key part of getting ROI out of Machine Learning.
September 23rd, 2017
Is There A Data Science Skills Gap or A Hiring Hot Mess?
I wrote this for Fast Company three years ago. This is what I saw, and still see, is holding back the hiring process across tech.
June 17th, 2017
Eight Habits of Effective Data Scientists
The best data scientists I have hired and worked with share 8 common traits.
June 5th, 2017
How To Become A Data Scientist, No Matter Where Your Career Is At Now
Do not let what you are doing now, prevent you from breaking into data science. There is a clear path from almost any other job. This is about three years old, but things have not changed very much.
April 11th, 2017
Deep Learning Use Cases: What can deep learning do for businesses?
Three years ago, these were emerging use cases for and emerging technology. Many businesses are now starting to implement deep learning. Here is where to look first to get the most value out of your initiatives.
April 4th, 2017
How to Ace the Data Science Interview
Here is a preparation guide that will help you spend the least time getting ready for a data science interview.
March 26th, 2017
How to Enable, Not Replace, Employees with Artificial Intelligence
The drive towards automation will hit a brick wall if no one adopts it. Human in the loop machine learning works with people in the way they are used to working.
March 9th, 2017
What The C-Suite Needs to Know About Data Science – From a CEO And Data Scientist
The C-Suite is deeply invested in Machine Learning to drive revenue and maintain the business’s ability to compete effectively. That means involvement in the process at the right time and asking the right questions.
October 10th, 2015
How To Compete With Data Driven Companies
Five years after I wrote this, many companies are still working on strategies to compete with data driven companies. As the competitive landscape is increasingly dominated by data, this is more important than ever to understand.
August 5th, 2015
The Most Powerful Realization in Predictive Analytics
It was true five years ago when I wrote this and much more applicable today. Most data science can be improved with a few simple insights.
June 16th, 2015
Why Data Science Needs Predictive Analytics but Predictive Analytics Does Not Need Data Science
I wrote this five years ago and it has become more relevant today. Predictive analytics and now prescriptive analytics have moved past data science methodologies. To provide business value, companies need to take the next step.
June 7th, 2015
Why Content Marketing Success Depends on Diverse Data
Without diverse data sources, content marketing only sees part of the target audience. Knowing what data to use how to apply it to content marketing is a critical success factor.
May 20th, 2015
Data Science for Marketers: What Customer Analytics Do You Need for Digital Marketing?
Data driven digital marketing. Great buzzword but what data should a business gather to get a view of their customers?
March 14th, 2015
Best Practices In Data Science
Applied data science and machine learning require best practices. Here are the basics.
January 19th, 2015
How to Hire Your First Data Scientist
The first hire is as important now as it was five years ago. While most businesses have gone well past their first hire, for startups, this is still worth reading.
January 17th, 2015
Is Your Career About to Be Automated?
Machine Learning, as a field, has a responsibility to talk about what fields we are working to automate. As we move towards more accountability and impact analysis, this becomes an important area of focus.
January 1st, 2015
Big Data Buzzwords for 2015 And What They Mean
This was applicable five years ago and we are still introducing people to these terms. We are knee deep into implementations, but the buzz words still hide the real meaning and value.
December 22nd, 2014
What Are Businesses REALLY Doing with Machine Learning?
I wrote this almost six years ago and it is just as relevant today. Our implementations are more complex, but the use cases remain the same.
December 2nd, 2014
Machine Learning’s Big Pitfalls
There are many mistakes a business can make on their path to implementing data science and machine learning. Here are the most common.
September 27th, 2014
Data Science and Talent Management, A Match That Drives Margins
Six years ago, data science was making its first impacts on HR and hiring. Today, there is a diverse marketplace of machine learning based products. They still do not address linking ROI to individual hires or the hiring process.
September 21st, 2014
What Can Big Data Do for Your Pricing Strategy?
Over 5 years ago I was building my first pricing models and wrote a brief overview of the business cases for those models. Many businesses have yet to adopt machine learning driven pricing.
September 16th, 2014