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It’s becoming increasingly important that data scientists not only know how to build models but how to deploy them as well. Here are some of my favorite resources to learn Git:ĭocker is a containerization platform that allows you to deploy and run applications like machine learning models. It allows you to use the same codebase as others even if you’re working on an entirely different project.It allows you to work in parallel with several other data scientists and programmers.

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It allows you to revert to older versions of code.

Git is extremely important for several reasons, with a few being that: This means that files (or repositories) are stored both locally and in a central server. For example:Īll jokes aside, Git is a tool that serves the same purpose, except that it’s a distributed system. In high school or university, if you ever had to write an essay, you might have saved different versions of your essay as you progressed through it. If that doesn’t make sense, consider this example.

Git is the main version control system used in the tech community. Here are some of my favorite resources to learn Pandas: Pandas has become such a prevalent package, not only because of its functionality but also because DataFrames have become a standard data structure for machine learning models. As a data scientist, you’ll be using this package all the time, whether you’re cleaning data, exploring data, or manipulating the data.

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FreeCodeCamp - Full Python Course for BeginnersĪrguably the most important library to know in Python is Pandas, a package for data manipulation and analysis.

Here are some of my favorite resources to learn Python: Python programming is a building block for applications like manipulating data, building machine learning models, writing DAG files, and more. Learning Python syntax is easy, but you should be able to write efficient scripts and leverage the wide-range of libraries and packages that Python has to offer. That doesn’t mean that you can’t be a data scientist if you use R, but it just means that you’ll be working in a language that is different from what the majority of people use.

Here are some of my favorite resources to learn data visualizations & storytelling:įrom my interactions, Python seems to be the go-to programming language to learn over R. And it’s especially important when communicating with others who are not as technologically savvy. A good picture book has good visuals, but it also has an engaging and powerful narrative that connects the visuals.ĭeveloping your data visualization and storytelling skills are essential because you’re always selling your ideas and your models as a data scientist. If you think creating data visualizations and storytelling are specific to the role of a data analyst, think again.ĭata visualizations simply refer to data that is presented visually - it can be in the form of graphs, but it can also be presented in unconventional ways.ĭata storytelling takes data visualizations to the next level - data storytelling refers to “how” you communicate your insights.

FreeCodeCamp - Full Database Course for Beginners.

Here are some of my favorite resources to learn SQL: Also, writing efficient and scalable queries is becoming more and more important for companies that work with petabytes of data. SQL is used to extract data from a database, manipulate data, and create data pipelines - essentially, it’s important for almost every pre-analysis/pre-modeling stage in the data lifecycle.ĭeveloping strong SQL skills will allow you to take your analyses, visualizations, and modeling to the next level because you will be able to extract and manipulate the data in advanced ways. Whether you’re a data scientist, a data engineer, or a data analyst, you’ll need to know SQL. SQL is the universal language in the world of data. With that said, let’s dive into the seven most recommended data science skills to learn in 2021:

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And so, the seven most recommended skills to learn actually overlap with the skills of a data analyst, a software engineer, and a data engineer. Currently, there is a much higher demand for skills that are used in the pre-modeling phases and post-modeling phases. You might notice that none of the seven skills have anything to do with machine learning or deep learning, and this is not a mistake. I’m specifically not referring to data from scraped job postings because, from my experiences, there seems to be quite a disconnect between job descriptions and what’s actually done on the job. While this article may be more anecdotal, I feel like this shares a valuable perspective. I want to share the seven most recommended data science skills from dozens of interactions and discussions with some of the largest data leaders in the world, including the Head of Data & Analytics Google, the Senior Director of Engineering NVIDIA, and the VP of Data Science and Engineering Wealthsimple.