How to Break into Data Science
By: Nicole Janeway Bills
Tags: Machine Learning, Data Science, Python, Artificial Intelligence, Programming

When your data needs to get dressed up, Tableau is a fool-proof style service. It offers a sleek, drag-and-drop interface for data analytics with native integration to pull data from CSVs, JSON files, Google Sheets, SQL databases, and that back corner of the dryer where you’ve inevitably forgotten a sock.

Data is automatically separated into dimensions (qualitative) and measures (quantitative) — and presumed to be ready for chart-making. Of course, if there are still a few data cleaning steps to be undertaken, Tableau can handle the dirty laundry as well. For example, it supports re-formatting data types and pivoting data from wide to tall format.

When ready to make a chart, simply ctrl+click features of interest and an option from the “Show me” box of defaults. This simplicity of interaction enables even the most design-impaired data scientist to easily marshal data into a presentable format. Tableau will put your data into a suit and tie and send it to the boardroom.

Follow these tips to go from “good” to “great” in your data visualization abilities.

Gain inspiration from master chart-makers

Throughout my time as a business analyst at a Big Four firm, these three blogs were my go-tos for how to create a great looking, functional Tableau dashboard.

Keep these 4 guidelines in mind

#1 — Sheets are the artist’s canvas and dashboards are the gallery wall. Sheets are for creating the artwork (ahem, charts), which you will then position onto a dashboard (using a tiled layout with containers — more on this in a second) along with any formatting elements.

#2 — To save yourself time, set Default Properties for dimensions and measures. This will provide a unified approach to color, number of decimal points, sort order, etc. and prevent you from having to fiddle with these settings each time you go to use a given field.

#3 — Along those lines, make use of the overarching Format Workbook and Format Dashboard options instead of one-off formatting tweaks.

#4 — Avoid putting floating objects into your dashboards. Dragging charts around becomes a headache once you have more than two or three to work with. You can make your legends floating objects, but otherwise stay away from this “long-cut.”

Instead, use the tiled layout, which forces objects to snap into place and automatically resizes if you change the size dimensions of your dashboard. Much faster and simpler in the long run.

Get started with your first dashboard

In summary, the Tableau platform is easier than finger paints to use, so if you’re ready to get started, Tableau Public is the free version that will allow you to create publicly accessible visualizations— like this one I put together after webscraping some info on questionable exempted developments from the Washing DC Office of Zoning — and share them to the cloud.

Getting ready to present financials to the C-suite. Photo by Lisa Fotios on Pexels.

After investigating data from your local community, another good sample project is pulling your checking account data and pretending you’re presenting it to a CEO for analysis.

Read more about the difference between a data scientist and a data analyst:

Now if you not-so-secretly love data viz and need to find more time to devote to putting your models into production (🙋‍♀️), let’s move on to…

🦁 Learning DevOps

Your machine learning model is only as good as its predictions and classifications on data in the real world setting. Give your model a fighting chance by gaining at least a basic understanding of DevOps — the field responsible for integrating development and IT.

Reframe your thinking about what data science is or isn’t

In this brilliant article, hero of deep learning Andrej Karpathy argues that machine learning models are the new hotness in software — instead of following if-then rules, data is their codebase.

Get a sense for how this works in enterprise

This clever novel fictionalizes The DevOps Handbook and is surprisingly readable. (Not free — but if you buy a copy, give it to your coworker and hope they become super passionate about productionizing your models).

Introduce your machine learning model to the wild

Check out this article about how to use Streamlit for both deployment and data exploration. I’d be remiss if I didn’t also mention Docker and Kubernetes as enterprise-level tools for productionization.