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Foundations of Data Science
Module 2
Data-driven careers drive modern business
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Data-driven careers drive modern business
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Data professionals are so valuable to their companies. They determine which data streams are most important to specific business projects, challenges, and initiatives. They identified key goals for the future. They give their organizations the ability to take meaningful action by re-imagining processes and improving operations. To do all of this, data teams require individuals with diverse skills, knowledge, and interests. Therefore, there are countless different data focused roles, responsibilities, and project types, which are further differentiated by the industries and businesses they support. Among all of these possibilities, data careers can be loosely categorized into two complimentary types of work, technical and strategic. In this video, we'll investigate both, will also consider some examples of how data professionals use their expertise to transform industries. First, let's find out about the folks whose work requires a heavy emphasis on technical skills. Some examples of these professionals are machine learning engineers and statisticians. Through their expertise in mathematics, statistics, and computing, they build models and make predictions. Using tools such as R and Python, they help their teams extract value from business data sets. The result is a solution that has a direct positive impact. Another highly technical role is the expert data analyst, whose work involves exploring vast and complex data sets to identify directions worth pursuing in the first place. They ensure that an organization's data science efforts are directed as efficiently as possible. Bridging the gap between other technical data professionals and the strategic work we'll cover shortly. Now let's consider data professionals on the more strategic side. These people include business intelligence professionals and technical project managers, to name a couple. Strategic data professionals use their skills to interpret information that affects an organization's operations, finance, research and development, and so much more. Their work aligns closely to the overall business strategy, involves seeking solutions to problems through data analytics. In short, strategic data professionals maximize information to guide how a business works. Sometimes you'll find a company has roles that blend specialized technical knowledge with strategic data expertise, often an unusual and very creative ways. Soon we'll learn more about some of these opportunities, as well as the more specialized technical and strategic roles. Now, let's consider some examples of how data professionals use their expertise to transform industries such as finance, health care, manufacturing, and agriculture. First, the world of big finance was an early adopter of the power of data science. With the way information drives this industry, it's easy to understand why. Data professionals help financial institutions assess investment risks, monitor market trends, detect anomalies to reduce fraud, and create a more stable financial system overall. Data analytics is also key and health care. Here, the data benefits can actually be life saving. Data analytics is helping health care organizations process large amounts of clinical data, which supports the early detection of a health condition and leads to more precise diagnosis. Thirdly, data has a big impact in manufacturing. Data professionals predict when to perform preventative maintenance to avoid production line breakdowns, use data to maximize quality assurance and defect tracking, and artificial intelligence models help respond to logistical issues and reduce delivery truck miles on the road, advancing key sustainability goals. In a time when supply chains, every corner of the world, data enables clear, a near real time communication with suppliers, retailers, and other network partners. Data professionals are also advancing approaches to agriculture. With data insights, farmers develop new ways to approach crop production, livestock care, forestry, and agriculture. The inclusion of autonomous machinery, tractors, and irrigation systems is improving harvesting technologies as well. If you'd like to keep learning about how various industries use data analytics, refer to the course resources on this topic. Here's a little piece of advice for me. Don't miss an opportunity to learn from someone in real life. I love asking business owners, store managers, and client support professionals about how they use data each and every day. Who knows, one of these conversations could open a door to a future opportunity for you.
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