Data science is, and has been, in vogue. Every forward-thinking company wants to have a data science program because by now it is conventionally understood that it will improve profitability and efficiency across the business. To some companies with troves of data waiting to be ‘data scienced,’ this means hiring data scientists willy-nilly even if […]
Step 1: Collect data. Step 3: Profit. Step 2: Hire data scientist? Read More »
Many companies focus on hiring permanent employees for their data science roles when, in some cases, data science contractors might provide a convenient, more readily available solution while also offering other advantages. If you’re like many of the analytics leaders and HR professionals we talk to, you know how hard it is to build a
Need a data scientist? Why you should consider data science contractors. Read More »
Trends seen throughout the majority of 2017 persist in the current job market—low unemployment rates and rapid job growth across multiple sectors. These factors have combined to create a highly competitive hiring environment and a shortage of highly skilled professionals—especially in fields that have seen a higher than average job growth rate such as data
Should we be more open to sponsoring analytics talent? Read More »
What is Hashing? Hashing is simply passing some data through a formula that produces a result, called a hash. That hash is usually a string of characters and the hashes generated by a formula are always the same length, regardless of how much data you feed into it. For example, the MD5 formula always produces
What does it mean to hash data and do I really care? Read More »
Predictive analytics is this super-complex field that only statisticians and data scientists can understand, right? Well, perhaps it takes some training to do it well but it only takes two sentences to understand what it’s all about: In predictive analytics we determine the likelihood of something by looking at data about it. We do this
Data Science and Predictive Analytics Explained in Two Sentences Read More »
If nothing else, the data science industry is good at coming up with new, unique, confusing names and terms. ZooKeeper, MapReduce, Hadoop, Pig, Storm, Mahout MongoDB…the list keeps growing and it’s totally understandable if you can’t always identify or explain the different technologies and tools of the industry. In each article in this series we
Kafka: What Is it? Read More »
Any serious conversation about the future of data and analytics invariably turns to the topic of artificial intelligence. The past year has seen AI surge in popularity, with high profile corporations and personalities getting behind what many people believe could be the biggest technological watershed moment since the advent of the internet. Indeed, many companies
Artificial intelligence & insurance: how AI is changing the industry Read More »
Last week we offered some suggestions on how to attack your data management initiatives in 2018 according to your organization’s level of data expertise. This week we follow up with some tips on how to pitch those new data science technologies and initiatives to increase your chances of getting resources allocated to your projects. As
Pitching Your 2018 Data Science Budget Read More »
It’s that time of year again! With budgeting for 2018 at the forefront of your agenda, you may be wondering where to head with your data science efforts. Investments in big data are often expensive, but when planned correctly you can manage costs and also ensure that your new systems and employees generate data-driven profits.
Planning Your 2018 Data Science Budget? Here Are Some Logical Projects Read More »
Cyberattacks have become increasingly damaging and visible in recent years, in part because of numerous, high-profile instances of hacking affecting everything from your personal files to global election outcomes. And, in fact, hacking is no longer just about unauthorized access to, or loss of data. As the internet of things continues to expand, so has
How Big Data Helps Detect Hacking Read More »
