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Introduction
Did you know that despite teaming up to solve major challenges, the datasets used in machine learning and data science differ?
For instance, in an E-commerce recommendation system, you may use data science tools to analyze customer purchase history, behavior and preferences. Then, you use the findings or insights to train a machine model to recommend products or services.
However, if you were to observe the underlying datasets, you’d realize the structure, patterns, or characteristics are distinct. To avoid using the wrong datasets for machine learning or data science, here’s how to tell them apart:
Distinguishing Datasets for Machine Learning and Data Science
1. Purpose and focus
Datasets for machine learning are primarily used to train models to classify, predict, or make decisions based on specific patterns in data. Essentially, the dataset is structured to facilitate model learning, testing, and validation.Conversely, data science has a wide-ranging focus. Beyond making predictions, the dataset is also used for visualization, analysis, and extracting actionable insights from just about any data pool. You can use machine learning models as part of data science tools.
2. Types of data
Since machine learning models learn differently, their datasets must be structured to fit the learning approach. For instance, supervised learning requires that datasets have both input and output labels while unsupervised learning eliminates the need for output labels.In data science, the datasets are more varied. They range from structured to semi-structured to unstructured. Unlike machine learning, data science can make use of large and messier datasets like sensor readings and logs.
3. Workflow and application
Before using machine learning datasets, there are preprocessing stages it must undergo. From data cleaning to encoding categorical variables, there is a need to prepare and balance the dataset to help the model fulfill its purpose. Then, there are evaluation metrics to test the
Sourcing Datasets for Machine Learning and Data Science
1. Data Scraping
Web data scraping gives you access to historic and real-time data from various websites. It involves the use of automated scripts which fetch web pages and extract the needed data.
Even though web scraping is highly reliable for obtaining datasets for creating datasets for machine learning and data science, proceed ethically. Always check the site’s robots.txt file to determine what part of the website to avoid scraping.
Also, review the site’s Terms of Service (ToS) to find out if the site permits scraping. Keep in mind that some websites do not allow scraping activities. If you disrespect this simple rule, you might have legal trouble to deal with. Moreover, whenever you are allowed to scrape a website, never overload a site’s server with requests. Doing so may land you into legal trouble, too!
2. Open Datasets
These are ready-made and publicly available datasets. They cover a wide range of purposes and domains such as social science, healthcare, and finance. So, you are to filter through the datasets to find those suitable for machine learning and data science.
Ensure to source suitable datasets from reputable and trusted sources. Some trusted ones include Kaggle and UCI Machine Learning Repository. The former hosts generic and domain-specific datasets across different industries while the latter has high-quality datasets targeted at machine learning projects.
Other than the platform’s reputation, consider dataset user ratings and discussions. For example, Kaggle does allow users to rate, comment, and discuss a dataset, providing other platform users with insights into the quality and usability of that dataset.
3. Platform APIs
APIs come in handy whenever you want access to real-time structured data from specific websites. Basically, an API is a set of protocols or rules that facilitate and control the communication between software. Therefore, you can configure a machine learning model or data science tool to retrieve data automatically with the help of an API.
Most APIs deliver data in a semi-structured format such as XML or JSON, making it machine-readable and easier to manipulate for further use. Moreover, you can configure and automate a site’s API to deliver only the desired data to a model or data science tool.
4. Organizational Databases
To have a competitive edge in business, use internal datasets. They include the data the company has collected over a specific period of time. For instance, if you are within a retail company, look into product sales and customer transactions data. You can use the data to train machine learning models for prediction purposes.
If you do not work within an organization but desire to have some of their internal datasets, come up with a collaboration agreement.
Typically, a company is less likely to grant you their datasets due to legal regulations, privacy concerns, and business confidentiality. However, some may agree to share their data under specific terms and conditions.
5. Synthetic data
Sometimes, it is difficult to find data to build a specific dataset for reasons including scarcity, privacy concerns, or cost. In such acase, synthetic data is an optimal option as you get to simulate data with the help of statistical models or algorithms that can mimic real-word instances.
A good example of a situation you may need to simulate data is when you need patient records to train a model. The healthcare sector has lots of regulations like HIPAA, which restricts access to patient’s data. Also, you can generate data to expand a thin dataset or anonymous one.
Closing Words
Machine learning and data science make automation, personalization, and data-driven decision making possible. However, all these positives are impossible without data. Yes, data is the backbone of what machine learning models and data science tools do.
Now, before you go sourcing a dataset for either, understand the distinctive nature of each with the help of this comprehensive guide. Remember, using a dataset for the incorrect purpose or focus leads to errors, inconsistencies, and inaccuracy in results.
