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10/05/2020
By
MJV Team

Examples of Data Science projects to help you leverage results

More and more organizations – among them, big players, startups, and small companies – are discovering the strategic place of Data Science in business.

Regardless of industry or size, organizations that want to remain competitive in the era of Big Data need to develop and efficiently implement Data Science capabilities – or risk being left behind. Do you know what Data Science is?  In this article, we’ll talk a little bit about the concept and show you some examples of Data Science projects that helped leverage business results. Check it out!

What is Data Science  

Data Science is the field of study that combines domain knowledge, programming skills, mathematics, and statistics to extract significant insights from data. Data science professionals apply machine learning algorithms to numbers, texts, images, video, audio, and more, producing Artificial Intelligence (AI) systems to perform tasks that would typically require human input.  In turn, these systems generate insights that analysts can translate into tangible value.

One way to understand data science is to visualize what a data scientist does. Gartner defines this as follows: this professional has a “crucial role for organizations looking to extract insights from information assets for Big Data initiatives.” 

The company also adds that this specialty requires a wide range of skills; collaboration and teamwork are necessary to understand business problems. Decision modeling and analytical skills are needed to discover relationships within the data and detect patterns. Data management skills are essential for building the right database for analyzing and generating valuable insights to promote efficient decision making.

How the Data Science Process Works 

The Data Science process follows this flow typically: Data > information > knowledge > insight > intelligence

To make this a little more practical, let’s turn to the explanation given by Hugo Bowne-Anderson, Ph.D. in Data Science at Harvard Business Review

“First, data scientists create a solid database to carry out a robust analysis. Then, they use online experiments, among other methods, to achieve sustainable growth. Finally, they build machine learning pipelines and personalized data products to better understand their business and customers and make decisions more efficiently. In short, with technology, data science is about infrastructure, technology, testing, machine learning for decision making, and data products”.

Benefits of Data Science for Businesses

The main advantage of using Data Science in an organization is facilitating decision making and empowerment. Organizations with data scientists can weave quantifiable evidence based on data into their business decisions. These decisions can lead to increased profitability and improved operational efficiency, business performance, and workflows. 

In customer-oriented organizations, Data Science helps to identify and refine target audiences. It is also very useful in recruiting: the internal processing of applications and data-driven fitness games can help an organization’s human resources team make faster and more accurate selections during the hiring process.

The specific benefits of data science vary depending on the company’s purpose and the industry in which it operates. Sales and marketing departments, for example, can extract customer data to improve conversion rates or create individual marketing campaigns. 

Banking institutions are mining data to improve fraud detection. Video and Music Streaming services implement it to define user interest and use it to determine the best TV shows and movies to produce. 

Data-based algorithms are also used to create personalized recommendations based on a user’s history. Shipping companies like DHL and FedEx already use Data Science to find the best routes to lower delivery times, as well as the best modes of transportation for their shipments.

Two examples of Data Science projects used to leverage results.

Check out some Data Science projects that were very effective in their goal of enhancing results.

Yelp: improving the referral system

When it comes time to choose a restaurant, many people turn to Yelp to help make that choice. But what if you are looking for a specific type of cuisine, and there are many restaurants with the same rating within a small radius? Which one would you choose? 

Robert Chen, a data scientist, investigated a way to evaluate Yelp reviewers on Yelp better to determine whether his reviews led to the best Indian restaurants.

Chen found out that there were many recommended Indian restaurants with the same scores when searching on Yelp. Indeed, not all reviewers had the same knowledge of this cuisine, right? With that in mind, he took the following into account:

  • The number of restaurant reviews by a single person from a specific cuisine (in this case, Indian food). He managed to justify this parameter by examining reviewers from other cuisines, like Chinese food, for example;
  • The reviewer’s ethnicity in question, if the reviewer had an Indian name, he inferred that they could be of Indian ethnicity and, therefore, more familiar with what constituted good Indian food.

Its modification in data and variables showed that those with Indian names tended to give good reviews to only one restaurant per city in the 11 cities analyzed, thus providing a clear choice per city for restaurant customers.

Amazon vs. eBay: e-commerce comparison saves money

Have you ever made an online purchase and then found that it was significantly cheaper at another store? To support a Chrome extension he was building, Chase Roberts decided to compare the prices of 3,500 products on eBay and Amazon. 

The results showed substantial savings potential: “Our shopping cart has 3,520 unique items and, if you choose the wrong platform to buy each of these items, that cart will cost the US $ 193,498.45,” says Roberts on his blog.

As you can see, Data Science isn’t some new-age boogie man waiting under the bed to stop your digital transformation in its tracks. It boosts results by helping you find answers to business questions. Go deeper into this theme: download our Design Driven Data Science e-book now.

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