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Data Science vs Machine Learning

Machine Learning vs Data Science

Data Science and Machine Learning (ML) are not just theoretical concepts but practical tools that can revolutionize businesses. By leveraging these technologies, companies can enhance their product performance and make data-driven decisions. In fact, by 2024, almost half of all businesses (and over 90% of leading businesses) are projected to have adopted Machine Learning solutions. While these fields are interconnected, they have distinct characteristics that determine their unique contributions to the tech industry. Let's explore these differences in more detail.

What is Data Science? 

Data Science is a broad term within the Computer Science field of study that makes meaning and finds hidden insights out of raw data. Its goal is to get a deep understanding of answers, validate assumptions, and solve problems to support business operations and strategies through valuable insights. This way, Data Science plays a crucial role in streamlining a wide range of business processes, such as marketing campaigns, anomaly detection, and data entry. This field has brought us some of the most useful tools we take for granted, like Google Maps and Google Analytics.

Data Science has gained significant popularity in recent years, largely due to its close association with Artificial Intelligence (AI). By harnessing the power of AI and combining it with technical skills like statistical analysis, math, and programming languages, Data Science professionals can develop applications that can tackle complex tasks, often without the need for human intervention. This is where the lines between Data Science, Machine Learning, and AI start to blur as they all work together to create intelligent systems. 

AI can help you analyze large volumes of data and provide personalized content recommendations. That's very common in most popular social media and eCommerce platforms, such as Instagram and TikTok. But how can Software Engineers use Data Science to build apps that rival (at least in some areas) human intelligence? That leads us to our next topic.

What is Machine Learning?

Machine Learning (ML) is a powerful subset of Data Science with a critical role in building, through Data Science techniques, algorithms that can learn from data insights. Used with predictive analysis and statistical models, it can help collect more valuable insights from data. Machine Learning algorithms perform pattern recognition to make accurate predictions from fields like disease diagnosis to market trends, stock prices, and weather. 

Common Machine Learning techniques used to train predictive models include Supervised Learning, Unsupervised Learning, and Reinforcement Learning. These techniques dictate how algorithms learn to understand data and make predictions.

For example, Supervised Learning is commonly used for speech recognition (e.g., Siri and Alexa), image classification, and future trend prediction based on past events. Unsupervised Learning is normally used for tasks like customer segmentation and recommendation systems (e.g., Netflix, YouTube, Spotify), being also very common in anomaly and fraud detection. On the other hand, Reinforcement Learning is especially popular in the gaming industry, as well as in robotics and self-driving cars. 

How Does Machine Learning Work with Data Science?

Let's say you're building a social media app, and you want it to provide personalized recommendations. You would start by defining the problem and collecting relevant data, including browsing behavior and other interactions with content and accounts. After that, you'll have to preprocess and clean the data you've collected. This step is  very important because raw data often comes with inconsistencies, missing values, and wrong formats. Making predictions with data that hasn't been preprocessed or cleaned can lead to very inaccurate predictions and poor performance.

After cleaning the data, Software Engineers can implement Exploratory Data Analysis (EDA) to understand it better and gain insights into complex patterns. This analysis will determine the best tools to process the collected data and help Software Engineers create the features to be evaluated in the learning process. Think of these features as the particular variables the algorithm will analyze in further steps, like genre preferences, ratings, and viewing time. After defining the features, it's time to select a model or algorithm and train it with the data and features from the previous steps.

That's when Machine Learning comes into play and joins the Data Science process. In fact, the algorithm trained with the data is referred to as the Machine or Deep Learning algorithm. After the model is trained, Software Engineers must evaluate its performance and accuracy by adjusting values in the algorithm, known as parameters, to improve results. Further steps, such as deployment, monitoring, and maintenance, involve both Data Science and Machine Learning. Lastly, Software Engineers can integrate the algorithm into the app, ensuring it can handle new data and still make accurate predictions.

Why are Data Science And Machine Learning Relevant?

Data Science and Machine Learning have a strong focus on helping us get the most out of data to solve real-world problems. They can help overcome business challenges with data-driven decisions. With actionable insights from data, businesses can understand their users better and refine their strategy. Garner's studies have shown how "Data Science holds the key to unveiling solutions to old problems." These studies also show how Data Science can change lives for the better and even save them in some cases. 

For instance, UPS implemented an On-Road Integrated Optimization and Navigation (ORION) system, which relies on Data Science. The ORION system has helped UPS save millions of US dollars a year by optimizing delivery routes for trucks and providing customers with better service.

Lastly, Data Science and Machine Learning are also key to the amount of data we currently have access to, and data will do nothing but increase in the following years. Data creation is projected to reach around 180 zettabytes by 2025! Just imagine one zettabyte equals one million petabytes, and one petabyte equals one million gigabytes. If a gigabyte were the equivalent of a book (1.1811 inches thick) in the data projected for 2025, there would be enough books to stack from the Earth to the moon and back over 200 times. The goal of Data Science and Machine Learning solutions is to help us take advantage of all those massive amounts of complex data sets to build world-class solutions. 


Nine of every ten leading businesses have acquired Data Science and Machine Learning models due to the great value they have for the business decision-making process! We can expect them to continue to shape most industries with amazing products and data-driven insights. As a Full-Cycle Product Development agency, we have a strong understanding of how to carry on projects involving Machine Learning and Data Science, with examples of reputable businesses like Sylvester/Tably.