Archie  Clayton

Archie Clayton

1679043321

Google Sheets with Data Visualization

In this tutorial on data visualization in Google Sheets you will learn how to create bar and pie charts in Google Sheets, how to filter data for use in the charts and towards the end show you how to publish the charts to the web for free.

Data Visualization in Google Sheets for Beginners

Spreadsheets are the OG resource for visualizing data with charts and graphs...unless you count chalkboards, I suppose.

Spreadsheets are built to churn through tons of data. And by using a few simple built-in tools, you can glean valuable insights from large chunks of data.

og

gif of "OG" graphic

When dealing with small sets of data, you can often find answers and insights at a glance. But when your spreadsheets begin to reach into the hundreds and thousands of rows, charts can help condense all those numbers down to useable pieces of information...especially if you're presenting to people who aren't good with numbers!

Video Overview

Speaking of visuals:

  • Here's a link to the demo spreadsheet with all our data and charts.
  • And here's the video walkthrough of everything covered below:

How to Get the Data

Kaggle is a wonderful resource to find interesting data sets. We're using this video game sales dataset. To import it into a Google Sheet, all that we need to do is create a new Google Sheet by typing sheets.new in the address bar of our browser.

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screenshot of web address bar

Then, select File, Import from the menu.

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screenshot of file menu in google sheets

You can now upload the .csv file you downloaded from Kaggle.

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Screenshot of importing options in google sheets

This will give you several import options. If you're following along and using a completely blank, new spreadsheet, simply select Replace spreadsheet and it will pull everything in automatically.

If the data is cleaned well, and Kaggle datasets typically are, you can leave the separator to Detect automatically.

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screenshot of import file options

This will give us a lovely 16,000+ row spreadsheet full of video game data. 😁

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screenshot of spreadsheet dataset

How to Insert Charts

From here, we need to select Insert - Chart from the toolbar.

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screenshot of insert chart in google sheets

We'll be confronted with a blank chart in the middle of the screen and a Chart editor in the right sidebar.

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screenshot of chart editor

Now let's make sure we're referencing the correct data range. Google Sheets is pretty smart, and if you click the little graph icon to the right of the data range form, it will suggest some ranges to use. In our case, the range we need is suggested: A1:K16600.

selectrange

We're going to find the sales by genre, so next let's select Genre for our x-axis:

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screenshot of chart options 

Sometimes Google Sheets will be not-so-smart. If there are a ton of series listed and a funky graph, you can simply remove all the series and manually add what you need:

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screenshot of chart series

Now click the Aggregate button to group all the sales data for each genre, and select NA-Sales as the Series to display the sales in millions of dollars on the y-axis.

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screenshot of chart series options

And voilà! We've got a standard issue column bar chart. But we can do better. At the top right of our chart editor, we can Customize the chart further by changing the appearance, font, gridlines and titles.

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chart editor screenshot

How to Customize the Chart

From the customization tab, we have a lot of options. We can style our chart by changing the background color and font. We can make it 3D, and we can choose whether or not to maximize the chart in the chart window.

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Chart style screenshot

We can then add chart titles, subtitles and axis titles and also modify the color and fonts.

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Chart titles screenshot

Then, we can individually edit each Series. In our example we're only using one series, but if there were more, you could modify each of their styles independently.

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screenshot of series customization options

If you have a legend, you can modify those options in the next dropdown window:

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Then there are customization options for both the horizontal and vertical axes.

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screenshot of axes options

And the last block of customization options is for gridlines and tick marks. These can be toggled on and off, and we can change the color and frequency of the grid and tick lines.

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Once we're done, we now have a more stylized chart:

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Screenshot of column chart in Google Sheets

If we'd like to move this chart around, we can drag it throughout the current spreadsheet. Or, we can put it on its own dedicated sheet by clicking the three dots in the top right and selecting Move to own sheet.

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How to Publish the Chart

Here's an added bonus: you can actually publish the chart (or the whole worksheet) to the web. Select the Publish Chart option from the dropdown on the chart shown above, or select File, Share, Publish to web:

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screenshot of publish to web options

From here, you'll get to select what you wish to publish and how you want it displayed. For this example, we'll select the Sales by Platform chart to be shared as an interactive chart.

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Screenshot of publishing options in google sheets

This will generate a shareable link to the chart. It may take a few seconds to load, but once it does, you'll have a nice chart to easily share that is interactive. When you hover over the slices, it will display the percent of sales of the pie slice.

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Screenshot of a published chart

Here's the link to the chart that we just made.

Conclusion

Thanks for reading! I hope that you learned something in this beginners tutorial on data visualization in Google Sheets.

You can really do a whole lot using the basic built-in charts available in Google Sheets as well as Microsoft Excel. Charts remain an extremely helpful way to interpret large data sets.

Have a great one!

Source: https://www.freecodecamp.org

#datavisualization #googlesheets

What is GEEK

Buddha Community

Google Sheets with Data Visualization
Sid  Schuppe

Sid Schuppe

1617988080

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Tia  Gottlieb

Tia Gottlieb

1594600740

Google Data Studio in 6 steps — Beginners Guide

“Whatever your message is, data visualization can increase its reach a thousand-fold” — Randy Olson

Google Data Studio is a data visualization, or a reporting tool aimed at converting data into stories, generate insights and curate reports

Out of the many data visualization or reporting tools, Google Data Studio stands out for its FREE and astute service offerings, and while that might be enough to attract storytelling enthusiasts, it has a multitude of other characteristics to offer –

  • Ease of Use — since most users are well versed with google suite’s user interface, deciphering and navigating data studio becomes natural and intuitive
  • 200+ data connectors
  • Drag and Drop functionality
  • Customizable Charts and Visuals
  • Effortless sharing and collaboration options

Step 1 — Getting Started

To use Google data studio, you can use your existing Google account or create a new one, there is no installation of any kind required.

Next up, once you open Google Data Studio, your window would look as below.

This window provides you with some predesigned reports and templates as your guide or reference.

The Blank Report tile with a ‘+’ sign or the ‘+ Create’ option on the left pane, would direct us to a blank canvas to create our reports.

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The Data Sources tab provides a view on all the data sources used in creating the reports.

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You can click on the ‘+ Create’ option to add a data source

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Step 2: Data Preparation

You have an option to pull data from multiple platforms. Scroll down or use the search option in Google Data Studio to find the platform you wish to connect to.

To know more about the data connectors.

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Select on the platform or connector of your choice, browse the data set you wish to connect to, and hit the ‘Connect’ button.

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Once you connect to the data set, your window would be divided into

  • Field Names
  • Field Types
  • Field Default Aggregation type
  • Description (if any)

The Blue colored fields are metric based — fields that can be aggregated (sum, average, count, etc.) or indicate quantitative values.

The Green colored fields are dimensional fields — these fields are categorical in nature (Name, Country, etc.)

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#data-science #google-data-studio #data #data-visualization #data analysis

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt