![]() Start with my free Python tutorial or the premium Python for Beginners course.If you’re completely new to Python, start learning the language itself first: The first stop when you want to use Python for Data Science: learning Python. The differences are what make this field exciting and full of learning opportunities! Learn Python It doesn’t matter too much: ultimately, we all need to learn and fill in the gaps. On the other hand, some data scientists come from the AI/machine learning world and will tend toward that part of the job and less toward other parts. That’s OK.įor example, if you come from a math background, you’ll be great at the math part, but perhaps you’ll have a hard time wrestling with the data initially. You can be strong in one, and weak in another. Every data scientist will have different levels of these skills. So what is a data scientist?Ĭoming back to our original question: what is data science? Or: what makes someone a data scientist? You need at least basic skills in all the subject areas named above. You can’t analyze data from a specific field of expertise without understanding the basic terminology and rules. Generally, you’ll need to dive into what makes a field what it is. You need to know about creating basic graphs, pie charts, histograms, and potting data on a map. Math and statisticsĪs if the above skills aren’t hard enough on their own, you also need a fairly good knowledge of math, statistics, and working scientifically.Įventually, you want to present your results to your team, manager, or world! For that, you’ll need to visualize your results. During the past years, however, many new super-useful Python libraries came out specifically for data science. There’s a reason the data science community has embraced Python initially. There are (literally) tons of useful libraries out there.You can use it both interactively and in the form of scripts.Python has proven itself to be an ideal language for data science for several reasons: However, you need to know one or more programming languages to do so. For many data science jobs, all you need to do is combine the right tools and libraries. There are fantastic libraries and tools out there for data scientists. You don’t need to be a software engineer, and you probably don’t need to know about software design, but you do need a certain level of scripting skills. In order to read, process, and store data, you need to have basic programming skills. ![]() Value: at the end of all your processing, what value does the data bring to the table? Think of useful insights for management.Īlthough you’ll hear about these five V’s more often in the world of data engineering and big data, I strongly believe that they apply to all of the areas of expertise and are a nice way of looking at data.Veracity: the data quality is it complete, is it easy to parse, is it a steady stream?.Variety: are there different types and data sources, or just one type?.Velocity: how quickly is the data flowing? What is its timeliness (e.g., is it real-time data?).These are also called the five V’s of data: How you manage and process your data depends on a number of properties or qualities that allow us to describe it more accurately. What kinds of data are there, how can it be stored, and how can it be retrieved? Is it real-time data or historical data? Can it be queried with SQL? Is it text, images, video, or a combination of these? But first and foremost, you have to get comfortable with data. To be a data scientist means knowing a lot about several areas. If we ask ten people, I’m sure it will result in at least eleven definitions of data science. It’ll also help us define a clear learning path.Īs you may know, giving a single, all-encompassing definition of a data scientist is hard. While I assume you have a general idea of what data science is, it’s still a good idea to define it more specifically. Before we start, though, I’d like to describe what I see as data science more formally.
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