Data Science

Of the many skills that a seasoned data practitioner should possess, the ability to Google search for answers accurately and efficiently is right there amongst the top.

In this article, discover the top Google search tips that will help you search for data science solutions and ideas better and faster.

Contents

(1) Exact (2) Exclude (3) Site (4) Filetype (5) Date Range (6) Wildcard (7) Logic Operators

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(1) Exact

While simple search terms generally work well, longer search terms with multiple keywords may return us a mix of results that do not match what we want.

A good example is searching for solutions to error messages we encounter while coding our data science projects. The following error is one I got when running a linear regression model with the statsmodel package recently.

The tip to finding the exact solution is to use quotes (" ") to wrap our lengthy search term.

With that, we would see that our search results have been narrowed down significantly to those that directly answer our question.

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(2) Exclude

There are times when we do not want specific terms to be returned as part of our search, especially if the results tend to be dominated by a particular topic.

For example, we may be want to find out how we can implement the XGBoost algorithm in other programming languages other than Python. This exclusion can be done by including a hyphen (–) followed by the term to be excluded.

The results will be information about XGBoost in other popular software such as R and KNIME.

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(3) Site

If we only want results from a specific website, we can include SITE: within our search term.

For example, we want to search the trusty StackOverflow to find out how to make our matplotlib plots appear as full-screen images.

The search results will then display links to the StackOverflow website related to our specific query.

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(4) Filetype

In the course of our data science work, we may want to find items of specific file types (e.g., images, documents).

For example, we might be looking for the PDF version of the latest AI report by Stanford University. To do that, we can include FILETYPE: in our search.

We will then see a list of pdf file results, and the top result will be our ‘Artificial Intelligence Index Report 2021‘ by Stanford University.

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(5) Date Range

Data science is a rapidly evolving field with many advances happening every year. If we want information before or after a specific year, we can include BEFORE: or AFTER: in our search.

For example, we want to learn more about the publications relating to the earliest and most recent versions of the YOLO algorithm in computer vision.

In addition, if we wish to find results within a certain period, we can use double dots (..) to specify a date range.

For example, let’s say we want to learn more about the numerous iterations of the YOLO algorithm between 2016 and 2020.

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(6) Wildcard

If we have only a vague idea of what we want to search for, we can use the wildcard asterisk () as a placeholder in our search term. The Google search engine will automatically fill the as part of the search.

For example, we may want to discover the various cloud platforms to build and host an SQL database.

The search results will show the popular cloud platforms (e.g., GCP, AWS, etc.) that offer relational database services.

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(7) Logic Operators

Should the search entail multiple criteria, we can use the logic operators available within Google search to retrieve more accurate results.

For example, suppose we want to find resources on building data visualization dashboards in either Python or R. In that case, we can use | (‘or’ operator) and wrap them in brackets () to link it with the following search term.

The search results will display resources associated with dashboard building in Python and R.

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Before You Go

I welcome you to join me on a data science learning journey! Follow my Medium page and check out my GitHub to stay in the loop of more exciting educational data science content. Meanwhile, have fun Googling!

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