![]() While you don’t need to know how to perform all of these activities, understanding what goes into each can give you the vocabulary to speak about them and understand how they impact your project. Data compression makes it easier to store and query a set of data. Data encryption makes a dataset more secure. Data wrangling includes cleaning a dataset by removing errors and filling gaps. For data to be analyzed, it must first be manipulated and transformed into something that can be more readily used.ĭata can be manipulated in several ways. This is because nearly every dataset includes errors, gaps, or information that’s unrelated to the business question at hand. Data Manipulationĭata is rarely useful in its raw state. Once you know what’s possible, you can more easily communicate with those who are responsible for data generation and collection. Still, it’s important to understand the different ways it can be generated and collected, such as surveys or questionnaires. As such, data generation and data collection are the earliest-and arguably most important-steps in the data life cycle.ĭepending on your role, you may not be in a position to generate or collect data. Data Generation and Collectionīefore data is manipulated and analyzed so you can glean insights from it, it must first be generated and collected. Domain fluency enables you to cut through the noise and identify the metrics and data points that are most useful to you. This, in turn, can make it challenging to generate, collect, evaluate, and analyze data. While domain expertise isn’t a data science skill in and of itself, it can be difficult to know which data points are relevant to your work and industry without it. ![]() To effectively leverage data, you must first have a solid understanding of your domain: the trends, developments, challenges, opportunities, and other factors that not only affect your industry and organization, but also the work you perform. Without basic data literacy, you’ll likely find it difficult to talk about or use data, making it one of the most important data science skills to develop as a beginner. You can also leverage the steps in the data life cycle-which underlies most data projects-and elements of the data ecosystem. By developing your data literacy, you can effectively discuss different types of data, data sources, analysis, data hygiene, along with key tools, techniques, and frameworks. This understanding is commonly known as data literacy. To interact with data and those who work with it, you need to understand its key terms, concepts, and language. Regardless of how often you interact with data, a firm understanding of data science can be an asset to your career, especially as small- and mid-sized businesses embark on the data-driven path blazed by larger companies. Change your career to a more data-focused role.Tie your work back to its business case by understanding the key metrics executives care about, along with your contributions to those metrics.Better communicate with others in your organization (especially those on the data team), as well as executives and members of the C-suite.Become more data-driven in your decision-making.Find and evaluate data that may be relevant to your job, even if you don’t typically use data.Whether you’re an individual contributor, manager, or business leader, building your data science skills can empower you to: Other professionals, however, can benefit from developing data science skills. DOWNLOAD NOWĭata science skills are most important for professionals who directly work with data and need to strongly understand it to do their jobs (for example, data scientists, data engineers, and analysts). This has made data science skills extremely valuable for professionals looking to advance in their careers.īelow is a look at why data science is important to modern business, who should prioritize developing data science skills, and a list of skills that those new to data science should gain.įree E-Book: A Guide to Advancing Your Career with Essential Business SkillsĪccess your free e-book today. Businesses that invest in data generation, collection, and analysis are often able to leverage it to inform decision-making and strategic initiatives. To the untrained eye, much of this data may appear as white noise, but in truth, it can be a valuable source of insight. This trend has been driven by several developments, including the emergence of social media, e-commerce, smartphones, wearable technology, and the internet of things (IoT). The past two decades have seen a proliferation of data generation and collection.
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