Data Analytics Starter Guide III

Learning while a working professional isn’t about having an immediate recall and doing everything at once. There isn’t a test we need to cram for. We aren’t (typically) working directly with data daily, but we tend to be managing others. So knowing what to do is more our speed. Guiding others in diagnosing problems, inconsistencies or other forms of troubleshooting requires we have some relevant experience.  

Pandas Profiling Tutorial

The word is getting out — data is subjective. So as we’re taken off our rose-colored glasses, we’re picking up the magnifying glass to screen data and datasets, data processing, and data insights more carefully.

The next hurdle becomes how to assess the quality of your data and datasets. Short answer: it’s a multi-step, human-informed, and algorithmic-based set of processes that are achievable in a reasonable timeframe but may not lead you to the conclusions you expect. Long-ish answer: you’ll need to be comfortable with the uncomfortable by leaning on others’ perspectives of the data and datasets. Then, you’ll likely have to forgo executing some of your “standard” data processing approaches as you’ll learn that not all approaches lead to valuable insights. And lastly, you’ll likely recognize how elusive quality data truly is so you become more agile and forgiving of your data. When you know better about your data and datasets, you do better.

Analytics Design Guide by BI Brainz

Stop struggling to standardize your dashboards and reports with our Analytics Design Guide online course and downloadable template. No design or marketing skills are needed to use our guide. Learn how to quickly customize it so you can share it with your team and users in minutes.

After downloading the guide, feel free to use it in your current/next project.

Exploratory Data Analysis (EDA) Using Python

This 30-minute tutorial walks the viewer through fundamental data analyses in Jupyter Notebook. It’s golden, especially for those who don’t know where to start. 

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