{"id":6131,"date":"2019-09-01T13:41:42","date_gmt":"2019-09-01T13:41:42","guid":{"rendered":"https:\/\/careerkarma.com\/blog\/?p=6131"},"modified":"2020-05-23T01:18:25","modified_gmt":"2020-05-23T08:18:25","slug":"top-data-visualization-projects-for-beginners","status":"publish","type":"post","link":"https:\/\/careerkarma.com\/blog\/top-data-visualization-projects-for-beginners\/","title":{"rendered":"Top Data Visualization Projects for Beginners"},"content":{"rendered":"\n

The reason everyone is so excited about big data and data science<\/a> is because of the enormous potential data has to transform the way business, education, healthcare, and many other fields operate, for the better. Coaxing insights out of huge volumes of information, however, is a well-paying<\/a> skill that only a small number of people will be able to devote themselves to acquiring. 
<\/p>\n\n\n\n

In my experience, data visualization is a component of the entire data science workflow, from start to finish. Data scientists therefore need to master the art of data visualization. With the appropriate charts and graphs a data scientist can understand the structure of the data they have, identify potential issues, craft analytical strategies, and summarize their results for others. 
<\/p>\n\n\n\n

These projects will set you on the path to learning this subtle and important craft. 
<\/p>\n\n\n\n

The Importance of Data Visualization<\/strong><\/h2>\n\n\n\n
\"\"
What\u2019s important in life? Caffeine and data visualization. <\/figcaption><\/figure>\n\n\n\n

As alluded to in the introduction, data visualization is baked into data science at a really deep level. 
<\/p>\n\n\n\n

Building a good visual representation of data is important not just for the non-technical consumers of a data scientist\u2019s work, but for the data scientist herself. 
<\/p>\n\n\n\n

One of the first things a data scientist does with new data is perform \u2018exploratory data analysis\u2019, or EDA. This involves familiarizing yourself with the overall patterns and characteristics of a dataset. Among the best ways to approach this is to throw up some quick charts in matplotlib or Altair and just see what\u2019s there. 
<\/p>\n\n\n\n

As with statistics, however, it\u2019s very easy to make a visualization that creates more confusion than it removes. Mislabeled plots, axes on the wrong scales, variables that aren\u2019t properly formatted, and other little problems can work silently to undermine your attempts at clarity. 
<\/p>\n\n\n\n

 When doing these projects, try to cultivate the habit of being extremely careful in assessing your visualizations. Where I work, we have extensive sets of checklists to make sure we\u2019ve done things like give our charts accurate titles (harder than you think) and we\u2019ve included units of measurement in our axis labels. 
<\/p>\n\n\n\n

Whether or not you go through this much effort, you should do whatever you need to be similarly careful. 
<\/p>\n\n\n\n

Projects for Data Visualization Beginners <\/strong><\/h2>\n\n\n\n
\"\"
The most important step is just to begin.  <\/figcaption><\/figure>\n\n\n\n

Since most data scientists are going to be using either Python or R, I\u2019d recommend using a mix of the standard visualization tools for completing these projects. These include matplotlib, plotly, Altair, and the built-in functions provided by Pandas and R, like .plot(). <\/p>\n\n\n\n