Sample highlights from LinkedIn Learning courses covering a wide range of data science and AI/machine learning topics, from data ethics to working with Python, R, SQL, and more.
(upbeat music) - Try not to think of data-driven decision making as a drop-in replacement for your own intuition. A data-driven culture uses data to enhance their teams' instinct. Your data science team will be the starting point in creating a larger data-driven organization. It's within this team that you'll start to develop a deeper relationship with your data. Try to think of data-driven organizations as companies with many data science teams. These teams create a mindset of questions and insight. They should help the organization not just collect data, but think about the data in new ways. - [Interviewer] What are you looking for in your team members? - So first off is that they want to be part of a team and you know, many times, especially some of the training we receive in academia, for those that come out of that realm, is that we're used to locking ourselves behind a door and working on the problem in a deep way and there's some problems that are very well suited for that, but majority of the problems work best when you're willing to collaborate, you're willing to trade notes, and you're very open about what's working but I don't think it's a good idea to try to have a one-size fits all type of team. You should try to have a team that has both a diversity, deep curiosity, but a passion for focusing on the problems at hand and pushing the ball forward for the mission of the organization. - There are a number of opportunities you can take advantage of to play an active role and contribute to data science and analytics fields. To name just a few, there are job titles such as data scientist, data engineer, business intelligence architect, machine learning specialist, data analytics specialist, and data visualization developer. Each of these roles are critical in effectively leveraging data and its potential despite numerous challenges. - [Barton] Data science is fundamentally a team sport, that you're going to need people from all sorts of different backgrounds and with different techniques to contribute to the overall success of the project. You can get the people who have all the necessary skills from the foundational data engineer to the machine learning specialist to the analyst to the managers. All working together to get the insight from your data and help your project reach its greatest potential in moving your organization towards its own goals.