In Data Science

For some, it may be difficult to grasp how prevalent data science is in our world! I’ve written a post here that brings to life some of the ways in which you may see data science in your everyday routine. However, you may find yourself wondering exactly how far-reaching data science actually is, or how it actually impacts certain industries or companies. In this post, I’ll go a little deeper into these topics.

For one, data science has been a major player in industries like manufacturing and retail. For example, in manufacturing, data science is utilized from an insight-driven perspective. Simply put, it’s how businesses use data to figure out how to best utilize their resources when making and producing a large-scale product they’re trying to sell to customers. For instance, a business may collect data on how the customer interacts with the product in order to figure out what features are most profitable to produce. They may also collect data related to how well the machinery is operating on the production line so they can analyze weak points and optimize efficiency. For example, Ford utilizes wireless connections and in-vehicle sensors to capture insights on driver behavior as well as ways to increase production speed.

In retail, we also see a similar move towards an insight-driven perspective, one that allows businesses to use predictive analytics by utilizing data from product reviews, call center records, and social media streams. On a merchandising front, businesses like Target and Walmart may even utilize data collected from video footage or track heat signatures in order to improve layout and product placements. If you have ever wondered why you can’t walk out of Target without spending at least a hundred bucks, now you know why! Basic industrial-organizational psychology usually places the most utilized items, like milk and eggs, in a place further into the store so that customers must walk past a plethora of products that they don’t need but may end up buying due to product placement.

What has become even more significant than brick-and-mortar stores, however, are online retailers, such as Amazon. Because of the huge variety of items to choose from, many customers may feel overwhelmed by how many options there are when they visit the website. However, because Amazon is so great at collecting data on customers and then turning that raw data into valuable predictors, they make it much easier for the average consumer to buy products suited for them. For example, to figure out what items to recommend users, they collect data on what items that user browses through and what they end up buying. Amazon also takes into account the user’s shipping address, which lets them reliably predict what income a user may have.

All these little tidbits of information allow them to build a 360-degree model of who this individual so that they can associate them with a group of people that also have the same identifiers. Grouping users can help Amazon make assumptions about what you will buy based on what others buy in your grouping. If you buy Burt’s Bees Lip Balm with a predicted income of 53k, they may come to the conclusion that people in this category also have a high rate of buying Bounty Quick-Size Paper Towels, which they can then recommend you buy next. In many ways, it’s exactly like what Target does with product placement when someone walks into the store. However, because Amazon lacks a physical storefront they must instead strategically place products around the web page in order to create the same passerby effect.  

On a larger scale, this is essentially how advertising works on all the social media platforms you may find yourself scrolling through. One one hand, they may collect data on you in order to predict which advertisements your group of users may relate with. However, they may also track your individual data and essentially stalk you with little bits of code called cookies. For example, one time while scrolling through Instagram, I saw an ad for my absolute favorite chai brand that’s manufactured in the town of my alma mater, the University of Colorado at Boulder. I caught myself thinking, “ How in the world did they know?” The ad was so specific to my preferences that it caught me off guard. I’m assuming at some point I had Googled this brand of chai and the browser cookies were used by Instagram to target users with that specific interest, which is in general how most targeted advertising works. The weird part is, I couldn’t recall ever Googling it…

The fact of the matter is, many businesses are collecting data on behaviors you may not even realize. Some say that Facebook records users through their phones or that Alexa is listening to you without your knowledge. The truth is, we have reached an age in which data is a highly valued commodity. As demonstrated by the near failure of footwear company Timberland, how a company uses data can either make or break them. As data becomes increasingly important in our world, the demand for data scientists will be even higher.  

If you’re interested in becoming a data scientist, check out Codeup’s program here. It’s a 20-week hands-on accelerator that will teach you all the technical skills you need to know in order to jumpstart a career in Data Science.

 

Joyce Ling is a software developer at a cloud security company based in Richardson, TX. In her free time, she sings in a women’s chorus, rock climbs, plays guitar, and currently runs an organization to bring queer women together in the Dallas/Ft. Worth metroplex. 

Follow her on Instagram @ironicsushi or read more of her work at The Luscious Word.

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