Read about the different roles that might be a good fit for your course.

Amy Peterson avatar
Written by Amy Peterson
Updated over a week ago

Terms like "beginner" and "expert" mean different things to different people, so we use roles to help clarify a course's audience. When choosing specific roles, you should explain how the course would help someone in that role, and what extra skills or prerequisite knowledge you are assuming learners that take the course will have. 

See below for possible Roles to choose from. 


Data Consumer

Data Consumers work in a non-technical role, but consume analytics outputs as part of their daily processes and regularly interact with colleagues who analyze data. They need to be able to interpret statistics, results, and visualizations that their colleagues generate. They use their domain expertise to apply the output of analyses to their business. 

Example job titles: Sales Executives, Marketing Administrator



Leaders use analytics for monitoring performance and making strategic decisions. They need to be able to converse with data science team members and make high-level decisions about how a project is implemented based on discussions with data scientists. They also identify opportunities to use data to improve their business performance and ask for relevant data when presented with recommendations. Similar to the Data Consumer, someone in this role should be able to interpret statistical results and visualizations that their colleagues generate. In addition to the interpretation skills, a Leader also needs to be able to direct data-led initiatives effectively.

Example job titles: Senior Executive, Senior Manager


Data Analyst

Data Analysts define and develop domain-specific analytics to support decision making, including calculating descriptive statistics, visualizations, and reporting. They support others in asking appropriate business questions to derive value from data and clearly communicate insights gained from their analyses. A large part of the job involves manipulating data, whether in SQL, Python, R, or spreadsheets; advanced programming skills are not expected. 

Example job titles: Data Analyst, Marketing Analyst, Business Analyst, Operations Analyst


Citizen Data Scientist

Citizen Data Scientists use advanced domain-specific techniques to perform data science tasks; they understand basic statistical modeling techniques (linear and logistic regression), and have the data manipulation skills of the Data Analyst. They perform advanced analytics to gain insights and communicate those insights to others to answer business questions.

Example job titles: Marketing Analyst, Business Analyst, Quantitative Analyst 


Data Scientist

Data Scientists have a strong background in a range of statistical techniques and an ability to apply those techniques to specific business problems. Data Scientists use traditional machine learning for prediction and forecasting. They have strong SQL skills and are proficient in either R or Python. In addition to the skills required of a Citizen Data Scientist, the Data Scientist has more advanced knowledge on the topics of statistics and machine learning and has several skills that overlap with the Statistician, Machine Learning Scientist, and Programmer roles. 


Data Engineer

Data Engineers control the flow of data within an organization and build custom data pipelines and storage systems. They design infrastructure so that data is not only collected but easy to obtain and process. They are proficient in SQL and use languages like Scala and Python to process data. They have strong database and command line skills. 

Example job titles: Data Engineer, Quantitative Data Engineer, Data Warehouse Engineer


Database Administrator

Database Administrators create databases and control database access for others within the organization while optimizing database performance. They have strong SQL skills. 

Example job titles: Database Administrator, Data Administrator, Database Architect



Statisticians have strong statistical modeling skills with a broad knowledge of different modeling techniques and when each should be used. They also have a strong knowledge of inferential statistics, hypothesis testing, and experimental design. 

Example job titles: Statistician, Economist, Mathematician, Risk Analyst


Machine Learning Scientist

Machine Learning Scientists are similar in their experience to Data Scientists, but they have added machine learning specialization skills. They have a broad knowledge of statistical models with a focus on being able to make powerful predictions. They use either Python or R to create their predictive models and are experienced in using popular machine learning libraries like TensorFlow to run powerful deep learning algorithms. 

Example job titles: Machine Learning Scientist, Machine Learning Researcher, Machine Learning Engineer



Programmers can develop packages, write high-performance code, and perform unit testing. They have a general knowledge of coding best practices and can conduct code reviews. 

Example job titles: Programmer, Software Developer, Software Engineer, Software Architect 

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