As a business analyst, you are probably noticing the need for data more and more in your company’s operations. You are using it with things like Excel or Power BI but your company’s leadership is asking for answers from data, and you have more questions you need to be answered. If you’ve been feeling this way, you’re not alone! In fact, we’re seeing a huge trend in business analysts looking for machine learning tools to leverage data for modeling and business projections. You may not have realized it yet, but if you resonate with any of what we’ve said so far, you fit the loose term of Citizen Data Scientist.
What is a Citizen Data Scientist?
Citizen Data Scientists are a relatively new concept in the business world, but they are quickly becoming a valuable asset for many organizations. A Citizen Data Scientist is someone who doesn’t come from a traditional data science background, but has a strong understanding of data science concepts and is able to use data analysis tools to make informed decisions. This allows organizations to leverage the skills of their employees to gain insights from their data, without relying solely on data scientists - a highly scarce and expensive resource.
The Evolution of the Citizen Data Scientist
The role of a data scientist is highly-technical and data scientists often command a huge premium, costing organizations a lot. They do amazing work and are in hot demand, but some teams simply don’t have the need for a full data team, or even just a data team of one, at this complex capacity. Your team may not have the project load to justify staffing an expensive team of data scientists. From project load to budgets, to staffing shortages, there are so many reasons why a company may NOT have a data scientist on the team.
However, with the abundance of data, and a greater understanding of what it can bring to the table for a business, leadership is asking more from the data at hand. They’re asking departments and analysts to provide answers and projections from the company’s data. But, what happens when the demand is so high, but the resources for a trained data scientist are low?
The reality is that business analysts have suddenly found themselves in this role. You may know enough to know what you need the data model to do, but you lack the technical know-how to execute the machine learning model. That’s where low-code and no-code solutions (like Mia) come into play.
If you’ve found yourself in this role, you have probably started to seek out resources to help you get started. Below are a few resources to help you learn the basics and the latest trends:
Data Science Weekly: This is a great resource put out weekly that covers topics from all angles within data science. Lots of new resources shared on a regular basis within this one!
Data is Plural: Dive into data-driven projects and data sets with this newsletter.
Towards Data Science: This Medium publication is a wealth of trending topics, as well as resources from experts exploring concepts, ideas, and codes.
EdX/HarvardX Professional Certificate in Data Science: A series of nine courses covering topics like data visualization, inference and modeling, data wrangling, data organization, machine learning, and more.
Coursera/IBM Data Analyst Professional Certificate: Learn core principles of data analysis and gain hands-on practice.
StatQuest: Josh Starmer dives into complex topics like stats, machine learning, and data science and helps simplify them.
David Langer: David covers a wide variety of topics like data analysis, data wrangling, data cleaning, data visualization, data exploration, machine learning, and more.
Mia: We may be a little biased on this one, but Mia is a great no-code tool to start building machine learning models. It’s designed to be simple to use, but powerful in its output!
Citizen data scientists are becoming a powerful resource within companies, allowing companies to be versatile and flexible, while leveraging data to grow and become a stronger force in their industry. At Mia, we’re proud to support data scientists, and citizen data scientists alike as they put their data to work!
To find out how you can start using machine learning to get more out of your data give us a shout at email@example.com