Data science vs Data Analytics vs Machine Learning
Which are the main differences?
An enormous amount of data enters our lives every day. From the links we click to the boxes we check, data is everywhere. Organizations use data to refine their products, improve their services, and deliver highly personalized user experiences. However, it is necessary to make sense of all the data that is stored.
In fact, companies are looking for professionals in Data science, Data Analytics and Machine Learning who can sift through the data goldmine and help them make quick business decisions efficiently.
What are the main differences between these three sciences?
Let’s go see them one by one.
What is Data Science?
In order to answer this question we have to start from the Venn diagram created by Drew Conway in 2010.
This diagram is made up of three circles: math and statistics, subject matter proficiency, and hacking skills.
In a nutshell, there are three fundamental steps to take to become a Data Scientist.
- Being able to handle text files from the command line, learning vectorized operations, and thinking algorithmically is key to being a successful data hacker
- It is also essential to obtain information from the data obtained. As? Using appropriate mathematical and statistical methods.
- Finally, we need to ask ourselves some motivating questions about the world and make hypotheses that can be brought back to the data and tested with statistical methods.
Essentially, if you can do all three steps, you are already highly skilled in data science.
What is Data Analytics?
Data Analytics is, on the other hand, a science that is based on performing basic descriptive statistics, visualizing data and drawing conclusions.
A Data Analyst must have a basic knowledge of statistics, a perfect knowledge of databases, the ability to create new views and must have the right perception of the data they view. Data analytics can be called the foundation of Data Science.
What is Machine Learning?
It is a practice that uses algorithms to mine data, learn from it, and predict future trends for that topic. Machine learning is therefore used to identify patterns and acquire hidden information based on the data perceived.
A good example of automatic machine learning is Facebook. Facebook’s machine learning algorithms collect behavioral information for every user on the social platform. Based on your past behavior, the algorithm predicts interests and recommends articles and newsfeed notifications.
Data Science VS Data Analytics
Data Science is an umbrella term that encompasses data analysis, data mining, machine learning, and many other related disciplines. While a data scientist is expected to predict the future based on past patterns, data analysts extract meaningful insights from various data sources. A data scientist creates questions, while a data analyst finds answers to the existing set of questions.
Data Science VS Machine Learning
The main difference between the two is that Data Science as a broader term not only focuses on algorithms and statistics but also deals with the entire data processing methodology.
Data Science, Data Analytics and Machine learning are three complex and related topics. All involve the manipulation and interpretation of data. While each overlaps, they can be broadly defined as follows:
- Data science is a scientific discipline that explores the facets of all types of unstructured data and how that data relates to the world.
- Data analytics is a key process in data science, used to create meaningful insights based on structured data sets.
- Machine learning is a practical tool that can be used to simplify the analysis of highly complex datasets.