Suman is a Data Scientist working for a Fortune Top 5 company. His expertise lies in the field of Machine Learning, Time Series & NLP. He has built scalable solutions for retail & manufacturing organisations.
Read it in 12 Mins
Is by Doing
Data Science is a domain-agonistic field, i.e., its application is not dependent on a particular industry. Rather it could be applied across any problems spanning various industry types considering the right set of data and tools are provided. As more organizations are becoming data-centric, the need to have the right set of people operating on the data is now a necessity. However, due to the lack of seasoned data professionals, companies often resort to training individuals with the relevant set of tools and techniques to get the job done. One such group of individuals could be referred to as Citizen Data Scientists. To know more about it, refer to this article.
Citizen Data Scientists are professionals who want to leverage state-of-the-art technologies to solve business problems. Though the term is deemed as a label, often they refer to themselves as only “Data Scientist”. These are the group of professionals who possess strong domain knowledge of the industry they are working in, which includes retail, banking, e-commerce, healthcare and many more. They work in tandem with the other Data Scientists in areas that require deep business understanding enabling the team of Data Scientists to focus on other complex issues. A more complete definition of a Citizen Data Scientist can be found in this Data Science course.
Often, the role of an analytics professional is to analyze the events which have occurred in the past and present the findings and insights in the form of dashboards, graphs, and reports. To improve on this work setting, a Citizen Data Scientist has the advanced set of tools to solve business problems with greater depth and precision compared to a Data Analyst. Some of their work could include predicting the next event, recommending a certain product, predicting customers most likely to buy, and so. It won’t come as a surprise if the analysis and business value generated by a citizen data scientist surpasses that of a data scientist. data science with python course demonstrates the importance of a Citizen Data Scientist.
The role of a Citizen Data Scientist is growing rapidly, and more companies are now including it in their workstream. One of the major factors which have caused this is the absence of quality Data Scientists. As data is growing exponentially, the demand to mine is. It also rises, which means employees should be trained with the necessary skill sets. On top of that, the advent of Data Science, Machine Learning, and AutoML have prompted professionals to understand these tools better for their business problems. The growing synergy between AutoML and Citizen Data Science has helped enterprises to upscale efficiently. Another reason for the rise of Citizen Data Scientists is the capability of individuals from various backgrounds to wear this hat and bridge the gap between business and technology. AutoML and Citizen Data Science has helped enterprises to upscale efficiently. Another reason for the rise of Citizen Data Scientist is the capability of individuals from various backgrounds to wear this hat and bridge the gap between business and technology.
An ideal role of a Citizen Data Scientist is a mixture between Data Scientist and Business Analyst. Many organizations would not need an expert Data Scientist to solve their problems. In those scenarios, professionals from different roles, such as software engineers, BI Analysts, and others, could be upskilled with the usage of the right tools to solve those problems.
As stated previously, a Citizen Data Scientist would have just the right mix of technical and domain expertise to tackle any business problem pertaining to the industry. They should be familiar with the use of AutoML tools, which can help in decision-making. They would work in sync with the Data Scientist to get more accustomed to using those tools. A complete definition of a Citizen Data Scientist can be found in this Data Science Bootcamp.
A Citizen Data Scientist often needs to wear multiple hats to develop the best solution for a business problem.
Working in the Data Science domain requires performing multiple experiments on the given data and comparing the various edge cases pertaining to it.
A Citizen Data Scientist cannot solve a business problem on their own. They need to collaborate and work together with other professionals, which includes Data Scientists, Product Managers, Stakeholders, and so on.
Having domain knowledge goes a long way in solving a use case that otherwise gets difficult to solve with only technical expertise. People with strong domain knowledge coupled with the right technical expertise could help in identifying the right set of data and features to build a solution.
A Citizen Data Scientist cannot always go by the book when approaching any problem. Multiple times you need to think outside the box and come up with a unique solution.
Building an analytical mindset requires critical thinking. Citizen Data Scientist uses their critical thinking mindset while solving a problem.
Flexibility and adaptability are key to success in Data Science. Many times the requirement and the business expectation changes. A Citizen Data Scientist would always advocate for such changes for the betterment of the business.
A Citizen data scientist is a superuser who bridges the gap between self-service data discovery and the analytical techniques of data scientists.
Preserving the broader objective in mind while approaching a business problem is an ideal recipe for success. A Citizen Data Scientist always focuses on the goals and direction of a company before proceeding to work on a use case.
A citizen data scientist has to leverage a set of tools to mine data and solve various business problems pertaining to multiple domains. Some of the citizen data scientist tools are.
Knime is an open-source analytics platform used by citizen data scientists. They could be easily integrated into data science workflows and are reusable in nature. Knime provides a drag-and-drop graphical interface that creates intuitive workflows. It could easily blend with R, Python, and Machine Learning. You can also check the Knime hub for publicly available workflows. hub for publicly available workflows.
Dataiku provides a one-solution-fits-all platform for citizen data scientists to integrate into their workflows. It supports an end-to-end data science pipeline which includes data preparation, visualization, machine learning, data operation, maps, and so on. Dataiku provides extensive functionalities for model monitoring, governance, and interpretability. Many small and large-scale enterprises in banking, retail, and other domains use Dataiku for their projects' interpretability. Many small and large-scale enterprises in banking, retail, and other domains uses Dataiku for their projects.
One of the most popular open-source platforms in Data Science, H2O.ai is used heavily by citizen data scientists and analytics professionals. Its AI cloud solves complex business problems and helps in garnering new ideas based on the results. The platform allows building ML models, monitoring them, and sharing them using AppStore.One of the most popular open-source platforms in Data Science, H2O.ai is used heavily by citizen data scientists and analytics professionals. Its AI cloud solves complex business problems and helps in garnering new ideas based on the results. The platform allows building ML models, monitoring them, and sharing using AppStore.
The DataRobot cloud platform could be seamlessly leveraged and used by analytics experts, DevOps teams, and stakeholders alike. Its augmented intelligence platform could be integrated with a diverse source of data and produce best-in-class solutions. Like other platforms, it also provides end-to-end data science solutions ranging from data preparation to model monitoring. DataRobot could be hosted in the private cloud, public cloud or even in edge devices. platforms, it also provides end-to-end data science solutions ranging from data preparation to model monitoring. DataRobot could be hosted in a private cloud, public cloud, or even in edge devices.
The Alteryx platform allows intuitive data storytelling, which is paramount to a business. Data could be accessed from various sources like spreadsheets, salesforce, UiPath, and so on. The data would then be passed through an AI-enabled platform, and the results could be integrated with SAP, AWS (Amazon Web Services), and other platforms. It has one of the largest data science communities.
The salary for any role varies according to the company and seniority. The salary of a Citizen data scientist could range from $88,000 to $107,000. On average, they earn around $100,040. With regards to the country, a citizen data scientist could earn around between €48,000 to €80,000 in Germany, whereas in the UK, the range is between £34,000 to £78,000.80,000 in Germany, whereas in the UK the range is between £34,000 to £78,000.
To become an industry-ready citizen data scientist, there are certain steps that need to be followed.
A citizen data scientist needs to master a set of skills to have a successful career.
A citizen data scientist needs to work collaboratively with data scientists and analytics professionals. They are not trained with the foundations of data science, and hence collaboration becomes important.en data scientist needs to work collaboratively with data scientist and analytics professionals. They are not trained with the foundations of data science and hence collaboration becomes important.
Citizen data scientists need to be trained with the AutoML tools and understand data security, fairness, and so on.AutoML tools, understand data security, fairness and so on.
Data compliance policies should be respected, and data access needs to be restricted.
Citizen Data Scientists are a mix of Data Scientists and businesses. They are not highly technical as Data Scientists but understand the usage of various AutoML tools to solve problems. However, a citizen data scientist possesses strong domain experience, unlike a Data Scientist. AutoML tools to solve problems. However, a citizen data scientist possesses strong domain experience, unlike a Data Scientist.
Citizen data scientists are crucial for a business as they possess the strong domain knowledge to solve any analytical problem. They understand the tools and can collaborate with the Data Scientist. Citizen data scientists are trained to share crucial insights with the business from the data. The Data Science with Python course demonstrates the importance of a Citizen Data Scientist.
Citizen Data Scientist is one of the rapidly growing roles in the industry. Many companies are now inclined towards AutoML tools, which makes the position even more relevant. The number of citizen data scientist jobs is on the rise. This article covered the importance of a citizen data scientist and how you could be one.
Citizen Data Scientists leverage open-source tools and platforms to solve business problems. They have the right set of domain understanding required to generate insights.
Citizens use data scientists to generate insights and maximize business outcomes. They have the right set of skills required to be successful in this field.
Data Scientists are more technical than citizen data scientists. On the other hand, a citizen data scientist possesses a better domain understanding.
Train them with the right set of tools. Also, create a more collaborative work environment.