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    How I’d Learn Data Science If I Could Start Over (4 Years In) | by Terence Shin | Nov, 2022 | Towards Data Science

    Fundamental Skills

    Regardless of what area of expertise you want to specialize in, it’s inevitable that you’ll have to know how to code in SQL and Python. And so, I recommend that you learn how to code as a starting point.

    SQL is the universal language of data. Whether you’re a data scientist, a data analyst, a machine learning engineer, a data engineer, or a blend of any of these roles, you’re going to need to know SQL.

    How I’d learn SQL is through a couple of resources in this order:

    Python/Pandas

    Python is important for data scientists especially because there are so many packages and extension of Python that are useful. R is an equally as good of an alternative, but doesn’t seem to be the main language that’s adopted in the data science world.

    Learning Python is a little less straightforward than SQL because I’ve found that Python is better learnt by “doing”, as in trying to build projects. That being said, here are a few resources that I found helpful in my career:

    Specialized Skills

    Once you learn the fundamentals, there are several subjects that you can specialize in. How I would determine what to focus on next first depends on whether I see myself as a Business-facing Data Scientist or a Research-facing Data Scientist.

    A business-facing data scientist is focused on initiatives that directly impact the business and tends to work with business stakeholders directly, almost like a consultant. Projects and required skills revolve more around solving business problems directly, the lifecycle of projects are relatively shorter and the impact of one’s work is consistently seen.

    A research-facing data scientist acts more like a researcher or a phD student. He or she will work on longer term projects, like building intricate models or conducting complex research questions. The lifecycle of projects are relatively much longer and the work may or may not be used by the business depending on the cost-benefit tradeoff.

    If you choose to pursue a role that has more of a direct impact to the business, then there are three sub-categories that I would dive deeper into: experimentation & inference, analytics & insights, and visualizations.

    Experimentation & Inference

    Experimentation and Inference refers to a set of techniques that are used to determine the cause-and-effect relationship between two variables. This is extremely important for a business to understand the drivers of success and ultimately what allows businesses to learn, iterate, and improve.

    Analytics & Insights

    Analytics refers to organizing and examining data, while insights refers to discovering information, like patterns and anomalies, in data. Data Scientists focused on analytics and insights are required to answer vague and generally tough questions using a set of analytical and statistical tools.

    Initial resources to learn the fundamentals are provided below:

    Data visualization is the graphical representation of information. Data scientists focused on visualizations are mainly focused on dashboarding, automated reporting, and developing visual insights.

    On the other hand, if you’re more interested in diving into the intricacies of models, reading research papers to keep up with cutting-edge methods, and are more interested in the productionization of models, then I recommend that you narrow in on a particular subject related to modelling. Some subjects include machine learning, deep learning, NLP, computer vision, network science, etc.

    Saturn Cloud is a platform that allowed me to build computationally expensive models that I wouldn’t have been able to build locally. It’s a great solution, if your specs are a bottleneck to your modelling.

    What’s next?

    Once you make it this far, it’s time to work on some data science projects and build your portfolio! Here’s a list of a couple of projects for inspiration if you don’t know where to start:

    Some platforms that you can use to start building your own projects are below:

    And with that, I wish you the best of luck in your endeavours!

    This content was originally published here.

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