Data science projects are typically quite different from your normal software development projects because they rely heavily upon the quantity, quality, and type of data that is available that is relevant to the business problem at hand. The motivation for a data science project is usually based on the idea that the organization already has access to most, if not all, of the data necessary in order to be able to produce some kind of useful results for the business. Now, as a data science practitioner, of course, you would need to have a deep understanding of the data environment and be able to identify exactly how that data might be used to benefit the business. Of course, you'll also need several other key skills. There's a number of different roles and responsibilities that are involved in a data science project. But, in general, there are some skills that are typically going to be held in common. Now, each data science practitioner, of course, needs a foundational understanding of statistics and probability. Because these sciences give us a underlying basis for understanding what's taking place. Some programming is going to be necessary, and the you don't need to be a genius programmer as it were, but you do need to be proficient. Because many of our data science tools are available through programming languages like python and R. Understanding how data can be adjusted to put it into a more usable form is also going to be very useful. And transforming your data is one of the areas where probably, as a practitioner, you're going to spend a lot of your day-to-day activities. Because often massaging and adjusting the information into a form that is most usable often takes perhaps the longest amount of time. And some of these projects having a skill set that will allow you to issue queries to database servers. Usually using structured query language or SQL in order to retrieve and update information in those databases may be essential as well. Now, not all data science projects will involve machine learning, but machine learning is often going to be a major goal. And so having a basic understanding of the theory of machine learning and the overall process related to training model, tuning it and evaluating different models and adjustments to those models to determine which are going to produce the best results for your particular goal. Having critical thinking skills is important because this will help you to perhaps spot patterns in the data while avoiding making assumptions that could mislead you. By applying your own intuition and your knowledge of the actual domain will help you to be able to make those assessments using your critical thinking, and of course, you're not going to be working in a vacuum. But normally on these projects, you'll work along with other team members. And so, the ability to work well with others and share information openly so that everyone can stay on the same page is going to be important to the success of your data science projects. Today, the lines between some of our traditional job rules have been blurred. An example of this is DevOps where software development and operations are integrated together to be able to yield better benefits to the business. And so, we understand that having individuals with varying skill sets is becoming more common integration of those skills. And, so we want to make sure that as we're going through a project we have, people that have the right skills involved at the right time. And perhaps even more important having individuals that are willing to learn new skills and adapt to new circumstances willing to expand their abilities. Because, of course, we're in a field where really the goalposts keep moving [LAUGH] to a large degree. And so, if you have someone willing to adapt and learn that's going to be very valuable to the team. Some of the problems you might encounter will perhaps be caused by limitations on the resources you have. Maybe [COUGH] a project will require hardware that you don't have in order to be able to complete its tasks within a reasonable frame of time. For example, maybe graphics processing units or computer clusters would accelerate the project. Now, the thing is setting these up goes typically above and beyond what a traditional IT skill set might require. And so there may be some more advanced learning and required there. We may also decide to forgo building our own hardware, and instead use cloud services which can provide us with an elastic scalable solution that perhaps will cost us for the initial usage. But, then of course we can scale back down so that it doesn't carry that ongoing cost. And there isn't a huge capital cost investment up front, like there would be in buying and building your own computer clusters. So, one way or the other, we're going to need to have a skill set that will allow us to support whatever decision has been made. It's cloud services are separate skill set as well that would need to be considered. And, of course, we have to think about really what are the requirements of the project and that is going to really drive what skills will be necessary to accomplish the objectives for that particular data science project.