Data Visualization Essentials for Data Analysis and Machine Learning
Data analysis and machine learning visualization techniques are effective tools to achieve your data science initiatives. With data analysis, it’s possible to show patterns and relationships for historical events within huge sets of data in a visually compelling way. Machine learning, on the other hand, is usually associated with analyzing data in the present and predicting future events, based on input variables received. Here are a few essentials about these two concepts and what to consider when presenting your data to a target audience.
In order to make historical data relevant and make sense of it, it’s almost imperative to visualize it. So the question should never be whether if the data needs to be presented graphically or not, but instead, what visualization options suit the persona viewing the material. Sometimes a huge lump of numbers isn’t everybody’s cup of tea.
There are many ways to visualize data: tables, charts, graphs, maps, infographics, etc. Over the years companies spent lots of money on business intelligence solutions, precisely for this purpose. Sometimes it requires some experimentation in order to figure out which format suits your purpose best. However, once you have come up with a viable option, it’s best to go through the following steps:
- Analyze what you see and cater to your personas. Once you have chosen a basic format to represent your data, you will want to learn from it to be able to present it to your target audience. Do these visualizations make sense for the target audience? What alternatives can I offer so that power users can drill down to answer their own questions? It’s important to consider as many scenarios as possible to be sure the visualization is not only good looking, but meaningful to the people it’s intended for.
- Highlight unexpected results. With the information you gathered in the previous step, you might need to tweak a few things here and there. You might want to inspect certain data elements or findings in detail in another visualization or highlight them in the current one. This may kick off other data analysis initiatives.
Through these two basic steps you can figure out if your visualization works and if the data is actually telling the right story. Remember that people understand data better through images and stories than by reading numbers in rows or columns. By visualizing data, you are able ask and answer important questions in a more effective way.
It goes without saying that big data is useful for machine learning. In many cases, large numbers of input variables are needed for machine learning to be effective. (You need to be careful, however, just throwing more input variables at the problem is sometimes counter productive).
But how does the machine learning model building process work? Basically there are two machine learning types: supervised and unsupervised learning. Supervised machine learning is when a model attempts to determine a specific outcome, such as whether an email is ‘spam’ or ‘ham’. In order to determine whether an email is ‘spam or ‘ham’ a data scientist should offer input variables and training data until the machine learning model offers satisfactory results.
Unsupervised machine learning, on the other hand, has no right or wrong answer. For example, segmenting a particular retail market into clusters may offer information about which persona should belong in which cluster. Data scientists intervene by experimenting with iterations such as ‘let’s segment the market with three clusters instead of two clusters’.
Once you’ve found an ideal machine learning model to use, you can deploy said model to obtain specific outcomes. But many users will want to understand how you determined an optimal model in the first place. Under these scenarios, web notebooks come in handy since you can mix mark down text, code and graphs to explain the methodology used during model training. Nevertheless, once the model is deployed, users may want to see results in real time in order to find patterns. Just as with data analysis, determining how to present your information is based in large part on the target audience. And remember, tell a story!
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