Tableau has become a popular data visualization and dashboarding tool as well. Visualization-wise, it can be immensely helpful to be familiar with data visualization tools like matplotlib, ggplot, or d3.js. When it comes to communicating, this means describing your findings, or the way techniques work to audiences, both technical and non-technical. Visualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions. This will be most important at small companies where you’re an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have. Some examples of data imperfections include missing values, inconsistent string formatting (e.g., ‘New York’ versus ‘new york’ versus ‘ny’), and date formatting (‘’ vs. Because of this, it’s really important to know how to deal with imperfections in data - aka data wrangling. Often, the data you’re analyzing is going to be messy and difficult to work with. The answer is that at a certain point, it can become worth it for a data science team to build out their own implementations in house. You may wonder why a data scientist would need to understand this when there are so many out-of-the-box implementations in Python or R. Or, your interviewer may ask you some basic multivariable calculus or linear algebra questions, since they form the basis of a lot of these techniques. In an interview for a data science role, you may be asked to derive some of the machine learning or statistics results you employ elsewhere. Understanding these concepts is most important at companies where the product is defined by the data, and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company. Your goal is to understand the broad strokes and when it’s appropriate to use different techniques. A lot of these techniques can be implemented using R or Python libraries so it’s not necessary to become an expert on how the algorithms work. This can mean things like k-nearest neighbors, random forests, ensemble methods, and more. Netflix, Google Maps, Uber), it may be the case that you’ll want to be familiar with machine learning methods. If you’re at a large company with huge amounts of data or working at a company where the product itself is especially data-driven (e.g. Statistics is important at all company types, but especially data-driven companies where stakeholders will depend on your help to make decisions and design / evaluate experiments. One of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren’t) a valid approach. You should be familiar with statistical tests, distributions, maximum likelihood estimators, etc. StatisticsĪ good understanding of statistics is vital as a data scientist. You’ll be expected to know a statistical programming language, like R or Python, and a database querying language like SQL. No matter what type of company or role you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade - and that includes several programming languages. Let’s get started! The 8 Data Science Skills That Will Get You Hired Programming Skills I’ve outlined them below, and you can find additional detail and learning resources in the Ultimate Data Skills Checklist at the conclusion of this post. Specifically, my team and I have worked with industry leaders to identify a core set of eight data science competencies you should develop. I’m here to help you know what skills you need to develop, and where you can learn them. Regardless of your previous experience or skills, there exists a path for you to pursue a career in data science. This blog post was last updated on July 27, 2021.
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