Datasets help you recognize patterns faster
What is a dataset?
A dataset is a collection of data focused on a specific subject. For instance, predicting traffic congestion relies on historical traffic data. Let’s say you are going to researching traffic flow on European roads, one of your first steps would be finding a dataset containing traffic jam information.
Is it a “data set” or a “dataset”?
There’s some debate over whether the term should be written as one word or two. According to sources like www.woorden.org, “dataset” is a single word, while others, like Dictionary.com, use “data set.” At EasyData, we prefer “dataset,” and that’s the spelling we use throughout this article. This guide explains the concept of a dataset and how to utilize it effectively in your organization.
What is in a dataset?
A dataset can consist of a collection of coherent data that can occur in all kinds of different formats.
A dataset contains information relevant to the subject you are investigating. For example, traffic jam datasets might consist of database table data collected by the departments or agencies in charge of exploitation and maintenance of the roads, where columns represent variables (e.g., time, location) and rows represent specific records.
Naturally, datasets aren’t limited to table structures. For example, if you’re training a Machine Learning model to recognize chairs, your dataset might consist of images of chairs.
Datasets come in various formats, such as text files, numerical data, images, sound recordings, videos, or other digital formats. The format depends on the use case and the specific requirements of your project.
What do you do with a dataset?
Datasets are a foundation for setting up Machine Learning algorithms. For instance, sticking to traffic jams example, you’re in logistics, and your planning system integrates traffic congestion data with real-time weather forecasts. This integration enables you to make informed decisions about travel routes and times.
Predicting the Future
With sufficient relevant data in your dataset, you can predict future events. For example, using historical traffic data and weather forecasts, you can estimate travel times between Amsterdam and Rotterdam during specific conditions. These predictions, powered by applied Data Science, are not guesswork—they’re grounded in scientific analysis.
With the power of this data, you can make decisions that shape the future, such as avoiding routes prone to traffic jamming under certain weather conditions. As data availability grows, these predictive capabilities become even more powerful.
Data Science in Practice
Data Science has the power of predicting the future through data analysis. At its core lies the dataset. Without a well-structured dataset, Machine Learning algorithms cannot function effectively. Finding a suitable dataset for your unique needs might be challenging, but it’s not always the case. There are resources to simplify the process. Tools like Google’s Dataset Search Portal or Kaggle provide access to diverse public datasets. Additionally, organizations often offer their own datasets—an overview of Dutch datasets, for example, is readily available here for instance.
From Dataset to Data Analysis
Transforming a dataset into actionable insights through data analysis involves several key steps. Here’s a breakdown of the process.
Document and Deliver
Provide thorough documentation of the dataset creation and analysis process. If needed, train your team to use and maintain the completed Data Science project.
What do you want to achieve with the Dataset?
Define Your Objectives
Clearly outline the question you aim to answer. This step ensures your analysis aligns with your organization’s goals. It might require a series of clarification sessions with EasyData asking you questions.
Dataset collection
Data can come from various sources, including your internal systems in the first place, web scraping, or publicly available datasets on open-source resources. Creativity and expertise play a significant role in assembling a dataset tailored to your needs and EasyData specialists can help you with this task.
Clean and Enrich the Data
Raw data often contains errors, missing values, or outliers. Cleaning the dataset ensures accuracy and consistency, laying a strong foundation for analysis. That is what makes Data Science projects so fascinating, Data Science is more than an algorithm in combination with a dataset.
Analyze and Visualize
Present the data in an easily understandable format to gain actionable insights. EasyData often uses Grafana for this. If you have another existing data visualization system in place, of course, we can help you unlock the value of your data with it as well.
What can we improve?
Here we enter the domain of what is called Feature engineering. In many projects, Feature engineering is an important part of producing correct predictive analysis. With Feature engineering, we transform values from raw data into a data format that can be used for accurate predictions.
Feature engineering comes into play as soon as the dataset with the algorithms trained for this purpose starts to yield data. We will evaluate the yielded data, i.e. the result of your data analysis. We then construct the specific features in an iterative and continuously evaluate the improved model performance. Furthermore, we continue to compare these improvements with our basic data. This iterative process improves model performance by identifying and constructing features that enhance accuracy.
Process completion
Depending on the individual agreements with our client, we provide our Data Science project with the desired documentation. This also includes a chapter on how the dataset was created. On request, we train employees of our client to use and maintain the completed Data Science project.
