365 Data Use Cases: Product Development with Tina Huang

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Hello! My title is Tina Huang and I’m an information scientist at a FAANG firm. I maintain a Bachelor’s diploma on the College of Toronto the place I studied pharmacology. Subsequently, I labored in bioinformatics for a 12 months after which did my Grasp’s in laptop science (MCIT) on the College of Pennsylvania. My expertise consists of an internship at Goldman Sachs the place I did some machine studying work earlier than I took up my present information science job in tech.

I’m tremendous excited to hitch the 365 Information Use Circumstances collection, and on this submit, I’ll share insights about my favourite information use case: product growth.

What Is Product Growth?

Product growth is a big and sophisticated set of processes that rests on many transferring elements, a lot of hypotheses testing, and finally many choices. The top purpose is to create and develop a product that customers love.

It’s also possible to take a look at our video on the subject under or scroll all the way down to carry on studying.

Take into consideration a few of your favourite apps in the present day: Instagram, Uber, Fb, and YouTube. Instagram has an infinite scroll, and Uber has totally different choices corresponding to UberXL and Uber Pool.

These days, we take it without any consideration that these options exist, however the integrity of these merchandise is, in actual fact, a labor of affection. It requires numerous selections to get them proper, lots of which haven’t been as simple at first. It’s the complexity of product growth, with all of its transferring elements, that makes information immensely highly effective.

What Is the Product Growth Course of?

Information Wants in Product Growth

In product growth, we do a lot of alternative sizing to determine which options to construct and what alternatives to spend money on. This usually entails analyzing similar products to compare how they performed. We will have both structured information (e.g. 1-5-star rankings) or unstructured information (corresponding to social media evaluations).

We additionally do experiments to check out the options we construct and see how customers reply to them. The info right here may also be structured or unstructured. Ideally, we additionally need a lot of information as a result of this can give us extra confidence in our outcomes.

As soon as we’ve the information, we start to tease out the high-impact insights. Keep in mind, it’s the issue that defines the instruments we use! As a basic rule of thumb, we begin from the only difficulty and steadily enhance in complexity.

Forms of Evaluation: Conventional, Machine Studying, NLP, and Deep Studying

Within the first place, we’ve traditional analysis. It entails a lot of speculation testing and statistics which 365 Information Science additionally has a wonderful course on. Usually, it requires quite a lot of information heavy-lifting and data visualizations with SQL and Python or R.

Plenty of issues could be executed with conventional evaluation, however it’s machine learning that has taken an especially stable place in product growth. In observe, we use machine studying in several conjunctures. Nonetheless, one in all my favorites is utilizing supervised studying strategies, corresponding to random forest, SVM, and XGBoost, to find which options contribute essentially the most to the success of a product. These fashions are straightforward to implement in addition to actually useful in deciding what to construct and the way a lot to spend money on it.

Pure Language Processing (NLP) is one other method that has a well-deserved place in product growth.

Lastly, unsupervised clustering and extra advanced strategies, corresponding to deep studying, even have their place. All within the title of growing a tremendous product!

Why Is Product Growth Essential?

Product growth is, by far, my favourite information science use case for 2 most important causes.

First, I prefer it due to how highly effective (nearly magical) it’s in driving selections. Right here, information actually will get to shine, in that it’s each the supply of reality and the motive force of insights. You’ll be stunned how a lot worth there’s in a fast XGBoost mannequin, for instance.

Second, data-driven product growth meshes very nicely with my very own philosophy, generally often known as the 80/20 rule, the place 80% of the outcomes come from nearly 20% of inputs. In product growth, 80% of the success of a product is set by 20% of its options. Principally, you reduce effort and maximize outcomes. That’s precisely why choosing the proper function to construct is so essential!

I hope you’ve loved studying this text. If you happen to’d prefer to be taught extra about information science, transitioning into laptop science, and software program engineering, you may as well subscribe to my YouTube channel. And for those who’re new to information science, take a look at the 365 Intro to Information and Information Science course.

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