What is data exploration? Data exploration is the first step of machine learning, where you use data visualization and other tools to get a good idea of various characteristics of your data. You can think of data exploration as that inventory stage where you look at the data and focus on its various qualities. For example, you can look at whether the data is accurate, and you can even look at quantity and size. These characteristics help you gauge how useful and valuable the data will be.
There are many data exploration tools to be utilized to give you the most accurate information possible about that data. Data visualization is also crucial because it helps you see what you should focus on without worrying about other problems. Data exploration is crucial because it is the foundation of machine learning initiatives. Machine learning does not work without good data, and it is crucial for you to be able to get data that will be relevant enough to make your machine learning initiative successful.
The Importance of Data Exploration
Humans are naturally visual creatures, meaning it is much better to show someone the results of data visualization than the raw information you got from data exploration. Data visualization is the core component that allows you to get insights from your data exploration results. It will let you know whether the data you are working with is useful or not.
Data exploration using python is also quite easy for most people, meaning there are plenty of libraries you can use. Data exploration in Python continues to grow because many people who aren’t software engineers prefer to use Python over other programming languages. You can visually look for cues and anomalies as well when you use data exploration in a visual way. Data visualization makes it possible to do lots of problem-solving without diving deep into the data yourself.
Data Exploration in Machine Learning
Machine learning is becoming a crucial component in the data exploration process. Machine learning allows you to analyze data in a more thoughtful way. On top of that, data exploration is often the foundation of a good machine learning model. That is why data exploration is now such a crucial component of the process. If you don’t do it well, your model accuracy will suffer. That is one of the many reasons why model accuracy suffers in production when the data starts to shift.
Data exploration and data visualization are also two ways of doing better feature engineering in the machine learning process. It allows you to get the information needed to create and select the right machine learning algorithms for the project.
Languages for Data Exploration
Data exploration using python is impressive. Python is the main language for data exploration because it is simple to use and open to people who aren’t software engineers. Data exploration tools are often written in Python or R. However, regardless of the programming language you choose, data exploration is only as good as your ability to understand what you are doing.
Data exploration in Python might also make you focus on problem-solving instead of understanding the process. It leaves you in a bad place because you might end up in situations where you need to solve a problem that cannot be found in another library. That is why it is crucial to understand Python and R, but you also need to know the algorithms that go into these libraries. Either way, data exploration is becoming more crucial as machine learning gets more important.