Blog categorized as Intermediate

Data Quality is a challenge that needs to be owned by Data Professionals. Given the volume of growing data we deal with and complexity of data transformations we implement in our jobs, we always need options to reduce tedious effort in testing and ensuring data quality. 

• Data Validation needs to...

Recommended Article for data analysts learning Python and SQL

Full article written by Sanjana is posted here 

A data analyst or data scientist get lots of opportunity to work on Python and SQL. Python with its enormous library support provides data analyst with a magic wand to mani...

Converting an organization that is not used to having a ‘data driven’ culture to become one takes considerable effort. Following are steps we recommend

1. Analyze data that is already captured: Start with what you have before starting new  data initiatives. Use the data to tell a story of what is h...

As a growing SaaS company Acme was focused a lot in growing customer base. Sales and Marketing took lot of time and assuming existing customers are happy with product was a reasonable thing to do.

However, Acme had usage analytics in place to monitor login activity every week. During one of these rev...

Key Point in referenced Article

"To that third point, data analysts teach technical courses on writing SQL and visualizing data in Tableau. Kalb teaches one about data literacy, including pitfalls to avoid in data interpretation, and on how to be a “data translator” and "data storyteller....