Data Science Terminology: 26 Key Concepts Everyone Should Understand (Part I)

Data science is the theory and practice powering the data-driven transformations we are seeing across industry and society today. Artificial intelligence (AI), self-driving cars, and predictive analytics are just a few of the breakthroughs that have been made thanks to our ever-growing ability to collect and analyze data. Just as with big data (see my terminology pieces here: Part I,

Big Data Terminology: 16 Key Concepts Everyone Should Understand (Part I)

These definitions are for anyone who wants to know more about Big Data and of which they should have a general understanding. As-a-Service Infrastructure Data-as-a-service, software-as-a-service, platform-as-a-service, these all refer to the idea that rather than selling data, licences to use data, or platforms for running Big Data technology, it can be provided “as-a-service,” rather than as a distinct product.

Making the Leap from Data Science Hopeful to Practitioner

It’s a familiar dilemma. You’ve done your research, read some books, taken some online classes – and at long last, you’re finally ready to get real-life work experience as a Data Scientist. But as you browse job postings, you become discouraged: “They want me to be a d3 Expert and a Deep Learning ‘Ninja’? A ‘Wizard’ of ETL and a

The Skills You Need to Become a Data Scientist

As a data scientist in Silicon Valley, I am humbled by the amount of attention our field has received in the past few years. Harvard Business Review has called data science the sexiest job of the 21st century and Forbes released a report explaining why data scientist is the best job to pursue in 2016. These headlines pique the interest

Data Opportunities Slipping Through the Cracks

If you’re struggling to make the most of your data, take heart in the fact that you’re not alone. A large percentage of companies are not fully executing on their big data, data science, and IoT strategies, several recent studies say. That good news is there’s lots of room for improvement. The gap between what companies can do with data,

Data Science Meets Behavioral Science

In the United States alone, 38 million people start their day by eagerly fastening a device to their wrist that is not worn for the purpose of fashion or keeping time. It is a fitness tracker and these little gadgets have swept the nation. Why? Because people love having instant access to their performance, activities and goals. They enjoy tracking

4 Reasons to Build Predictive Customer Analytics into Your Retail Business

Data science is playing an increasingly bigger role in how businesses utilize technology in strategy, planning, and operations. Everyone is trying to collect data, analyze it and apply the intelligence learned into optimizing business activities. One area where the value of data – particularly in predictive analytics – has been making its mark is in retail. This is a tough

When Big Data Isn’t Enough

The big data paradigm has changed how we make decisions. Armed with sophisticated machine learning and deep learning algorithms that can identify correlations hidden within huge data sets, big data has given us a powerful new tool to predict the future with uncanny accuracy and disrupt entire industries. But what if data alone isn’t enough? What if some decisions can’t