Warning: Invalid argument supplied for foreach() in /home/customer/www/opendatascience.com/public_html/wp-includes/nav-menu.php on line 95
Warning: array_merge(): Expected parameter 2 to be an array, null given in /home/customer/www/opendatascience.com/public_html/wp-includes/nav-menu.php on line 102
You’ve been looking to make a data science team, but you can’t get the right person. Too many terrible applications. Not enough talent. As data science becomes an integral part of business, business leaders are looking to add this crucial position to their teams. Unfortunately, confusion... Read more
Your company is making the move to big data initiatives, but with so many organizations launching half-baked data initiatives, how do you know your organization is going to succeed? Dr. Michael Stonebraker, a co-founder of Tamr and co-director of the Intel Science and Technology Center for... Read more
If you’re thinking of hiring a computer programmer as your data scientist, you may want to back up. While the two careers have a lot in common – writing programs, working with sophisticated machines, creating products that provide business value – putting “programmer” on your job... Read more
AI is coming for your business, and at the most recent ODSC East keynote, Michael Stonebraker of MIT and Tamr laid it all out for us. If you aren’t ready to hire the experts, you won’t be ready. AI Engineers are going to be the next... Read more
There are many opportunities in applying machine learning, whether as an individual developer or in a business. But how do you get started? This talk provides an overview that separates fact from fiction and proposes processes to find opportunities for applying ML. This includes understanding where... Read more
The eCommerce sector is one of the most profitable ones, with millions of online stores and digital portals showcasing products meant to cater to you from head to toe. According to Statista, the eCommerce revenues worldwide is anticipated to grow to 4.88 trillion US dollars in... Read more
Over the next 18 months, companies will be completing the R&D phase of their AI/ML investments and will be deploying their models and algorithms to production. The proper execution of deploying your AI/ML models will separate the organizations who see an ROI on AI from those... Read more
Lots of businesses want to use machine learning, but few are ready to integrate machine learning into a real-life context of operations. Dr. Mufajjul Ali, Data Solutions Architect for Microsoft, outlines how Microsoft is addressing these needs and offers some advice for businesses looking to operationalize... Read more
During the industrial revolution, the rise of physical machines required organizations to systematize, forming factories, assembly lines, and everything we know about automated manufacturing. During the first tech boom, Agile systems helped organizations operationalize the product lifecycle, paving the way for continuous innovation by clearing waste... Read more
Data scientists spend a lot of time with data, which by itself is neutral. It only follows that answers gleaned from the data would be neutral too. Even though data is neutral, our responses to data are sometimes filled with bias that can skew our outcomes.... Read more