![]() With that in mind, data analysis typically involves searching for and identifying trends, patterns, or outliers in data. Analytical SkillsĪnalytical skills are ultimately what allow you to dig into a dataset and come away with insights that inform business strategy and other decisions. 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. ![]()
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