Expert Talk: Data Science vs. Data Analytics vs. Machine Learning

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Data Science vs Data Analytics vs Machine Learning

Today, there is no question that Data and Analytics are essential for businesses to reach new potential. However, companies that want to thrive in the age of “Big Data” also need to understand the language of Data. Unless you are a data analyst with 20 years’ experience, becoming a data scientist, to learn algorithms and analytics programs is a huge challenge and there will be many unanswered questions.

What does Machine Learning mean? What is Unstructured Data? What are the core differences between Data Science and Data Analytics?

If becoming a data-driven organization is in your company’s future, it’s important to have a working knowledge of the tools you need to turn your data into actionable insights.

Data Science vs. Data Analytics

In the past, you may have heard the terms “Data Science” and “Big Data Analytics” used interchangeably. In truth, these two terms have distinct meanings, even though they are undeniably interconnected.

Although both fields can produce valuable results for data-focused businesses, it is important to understand that Data Science and Data Analytics are both very different in terms of their approaches to data and their goals.

Data Science

Data Science refers to a broad and multidisciplinary field, which is primarily focused on finding previously unseen connections and questions. Or more simply put, Data Science is a field that looks for answers to questions that we did not know we had. Data scientists use a wide range of techniques to gain insights from both unstructured and structured data.

Data scientists are often faced with massive amounts of data to sift through. They employ several mathematical and scientific techniques to obtain answers and incorporate statistics, machine learning, and predictive analytics into their research.

Data Analytics

Data Analytics is a concentrated subset of data science, one that is generally more focused. Data Analytics is often conducted with a specific goal in mind. With Data Analytics, information is often split into two groups: what companies know and what they are aware that they do not know. Employing Data Analytics, a company can sort through data to find specific insights targeted to its needs and goals.

The Core Differences

For those with less experience working with data, the differences between Data Analytics and Data Science may seem nuanced and minor. However, the core differences can have a huge impact on any company looking to use data to improve their business. Here are some of the major differences that define the two practices:

  Data ScienceData Analytics
Aim Using data to shape new questions. Using data to find specific answers (often related to a company’s goals).
Commonly used techniques Cluster analysis, anomaly detection, classification analysis.Data mining, network analysis, hypothesis testing, regression analysis.
Popular applications Exploratory data analysis, identifying and predicting trends, “cleaning” dirty data. Predictive analytics, comparing trends, clustering and classifying information.

Data scientists work with “what people don’t know they don’t know” and the results are used to shape new questions that were previously not being asked. Data analytics is often conducted with a goal in mind. To reach this goal, it looks at information companies already know and information they know they want to learn more about.

Machine Learning

Along with Data Analytics and Data Science, Machine Learning is another term you might have heard. But what exactly is it and how can it impact your business?

In simple terms, Machine Learning is the scientific goal of getting computers to learn and improve independently, as humans do. Through Machine Learning, computers can act in ways outside of what they were explicitly programmed to do and automatically learn through experience.

Machine Learning is a branch of Artificial Intelligence and another tool employed by data analysts to identify patterns, predict future behavior and gain further insights from data. It is used in a wide range of settings from optimizing health care and government services to improving corporate marketing and sales.

There are multiple machine learning methods, and the four most popular methods are:

  • Supervised learning: Uses historical data to predict future events, such as predicting credit card fraud or insurance claims.
  • Unsupervised learning: Where the algorithm is not given any “correct” answers to learn from and must figure out the information it is being given on its own. Popular uses include identifying outliers and creating self-organizing maps.
  • Semi-Supervised learning: Uses both labeled and unlabeled data for training and can be useful in cases where the cost of strictly labeled data is too high.
  • Reinforcement learning: Often used for gaming and robotics, this algorithm will learn through trial and error.

Taking Your Data to the Next Level

Ultimately, there are many factors that affect the choice of the right data services for your business, including your industry, company size, and corporate goals. Now that you have some familiarity with three important data concepts, you might be interested in discovering how data science, machine learning, and data analytics can help you turn data into one of your company’s most valuable assets.

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