Data science has been democratized for the most part. AI is now mainstream!
Data science is no longer the exclusive province of large companies with deep pockets.
AI has finally made its way into our everyday lives, and it’s only a matter of time before you start seeing it becoming more widely used across all industries, not just finance!
With that came a new breed of data scientists, citizen data scientists.
Who is a citizen data scientist?
A citizen data scientist is a person who works in fields other than statistics and analytics yet creates or generates models that incorporate predictive or prescriptive analytics.
Citizen data scientists are found in virtually every industry, from healthcare to retail.
In the past, many would have been called business analysts or knowledge workers because they rely on their insight and understanding to make decisions.
In today’s world, they use predictive analytics as part of their day job. They may not implement all analytic techniques, but they can tell if the results make sense.
Why is this important?
Organizations are increasingly using analytics to optimize every aspect of their businesses, from pricing decisions to hiring strategies.
However, many organizations find that IT isn’t keeping up with the demand to incorporate analytics into day-to-day operations. Citizen data scientists fill that gap.
Citizen data scientists do not need to be trained in analytics or mathematics, nor do they have to have deep IT knowledge about the used systems. They have to know what results are reasonable and can ask for help when needed.
What are the roles and responsibilities of a citizen data scientist?
The job responsibilities of a citizen data scientist usually don’t include dealing with data. They are mainstream workers who leverage the power of predictive analytics to make better decisions that affect their job performance, such as product placements or customer targeting.
Citizen data scientists are usually not expected to build elaborate models because they work with other people’s data. Instead, they evaluate results and interpret them for managers or other non-technical staff members.
Often citizen data scientists work in isolation with limited opportunities to compare notes or share best practices. Collaborating with peers is an excellent way to learn new techniques and gain further insights.
What is the best approach to becoming a citizen data scientist?
You don’t need to be an expert in statistics or analytics. Still, you should have some exposure to techniques such as regression analysis if you want to become a citizen data scientist.
However, it is vital that you understand the business applications of predictive analytics.
For example, linear regression might not be the best technique for your application, but you should be able to tell when a model is overfitting.
It also helps if you have some programming ability, but it isn’t necessary. You don’t need to know how to program in R or Python, but being familiar with SQL and Excel will help.
For example, writing a joint statement in SQL can be crucial in implementing a successful model.
How to become a citizen data scientist? Here’s a 5 step approach.
Improve your data literacy.
Data literacy is the ability to understand and communicate analytical techniques and their insights. In addition to interpreting data and drawing insights, a good data-literate person asks the right questions to creatively solve the problem.
The best way to improve data literacy is to collaborate with a data scientist or analyst. Platforms such as Kaggle make it possible. On the other hand, you could also try a good data literacy online course.
Here’s a list of programs you could try:
- Data Fluency: Exploring and Describing Data on LinkedIn Learning;
- Data Literacy for All from Tableau eLearning;
- Healthcare Data Literacy on Coursera and;
- Data Literacy Foundations by edX.
Pick the right tools for the job.
To be a successful citizen data scientist, you need to learn the basics of data scientists’ tools.
Basic programming skills are important as they can help transform unstructured or unorganized data into structured and coherent datasets. But having a good understanding of no-code platforms such as Tableau, KNIME, and not to mention, Excel is essential.
Tableau is one of the most used tools in data visualization. You can connect your data sets with Tableau and use its drag-and-drop interface to create interactive visualizations. It’s also possible to start with predefined templates or data sets.
KNIME is another open-source platform that allows users to create processes, explore, and mine information using various tools. It offers numerous modules for data querying, transformation, and analysis of big data sets on distributed architectures.
Excel is best known as the king of spreadsheets. It offers powerful features for data wrangling, and most people find it easy to use. It is an excellent tool for beginners in data science.
A good understanding of SQL is also essential. While various tools offer wizards for beginners, it is necessary to understand its basics to query data sets effectively.
Find data science projects to practice.
It is a good idea to practice your data science skills on actual data. There are various platforms where you can find exciting datasets for research and experimentation.
Try analyzing the same data using different tools and compare the insights provided by each of them.
Most enterprise data science problems are already solved and easy to replicate. You can find real data as well as their solution.
There are also various data science virtual competitions in which you can participate. For instance, Kaggle provides a platform for data scientists to compete in challenges with real-world datasets and prize money.
Ask for data access and replicate your success.
Once you have practiced sufficiently with real-world projects, it’s easy to approach company executives and ask for data.
Applying what you have learned by participating in challenges or practicing on projects will help you communicate the knowledge you have gained.
Before pitching to higher-ups, it’s important to do your research. Get familiar with company data and know how much access the company employees currently have. That way, you can justify why they need your skills!
Master the art of “Data Storytelling.”
To become a successful citizen data scientist, you needn’t have advanced degrees in Math or Computer Science. Instead, pick up an analytical mind and learn to create compelling stories with data.
Being a citizen data scientist requires more than technical skills; it means understanding the business problem and finding insights that others can use in the organization. The best way to do this is to speak to people and find out what issues they need solving and how they want these results presented.
To create a great data story, there are five essential components to focus on:
- Your audience — Who will you be talking to? What do they know, or what don’t they know?
- The happenings — What is the purpose of your communication? What happened in reality?
- Anecdotes — What context do you give the audience to help them better understand what happened?
- Analysis — How can your insights be explained using simple terms?
- Conclusions — How does this impact the audience? What should they take away from this conversation?
As AI becomes more and more mainstream in the enterprise, non-technical people are playing a more prominent role than ever before.
Citizen data scientists have emerged to help bridge the gap between IT professionals who may not be as skilled with algorithms or machine learning techniques vs. business stakeholders that know how to identify insights from large datasets but lack technical skills.
This article has discussed where you can start, what tools and techniques you should learn, and how to pitch your results when presenting them back to executives.
What is your opinion on citizen data scientists? Let me know in the comments.
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