Skills Development & Core Competences
Data scientists and research software engineers share core competencies such as programming, data analysis, machine learning, communication, and problem-solving skills. They use these skills to develop and deploy data science and machine learning models, analyze data, and build and maintain software applications.
Data scientists and research software engineers share the following similarities:
- Both need strong programming skills in Python and R.
- Both utilize data analysis and machine learning to uncover insights.
- Both must convey their findings to technical and non-technical audiences.
- Both require strong problem-solving abilities.
However, their roles differ in the following ways:
Data scientists leverage data science and machine learning techniques to tackle real-world problems. Research software engineers design and maintain software applications that support data science and machine learning. Data scientists emphasize statistics and machine learning, while research software engineers focus on computer science and software engineering.
To become a data scientist, a strong foundation in statistics and mathematics is needed, as well as learning programming languages such as Python, R, and SQL, build a portfolio of data science projects, be involved in the data science community, and pursue a relevant degree or certification.
Tips for scientists interested in becoming data scientists:
- Use your existing skills and experience. Research, problem-solving, and critical thinking skills are valuable in data science.
- Be patient and persistent. Learning data science takes time.
- Keep up-to-date with the latest trends and technologies in data science.
Key skills for a data scientist:
- Programming. Data scientists should be able to code to manipulate data, create data visualizations, and build machine learning models. Popular programming languages are Python, R, and SQL.
- Statistics and probability. Data scientists should have a strong foundation in statistics and probability to analyze data, discover patterns, and trends.
- Data wrangling and mining. Data wrangling is the process of organizing data for analysis, while data mining extracts insights from data. Data scientists analyze data by wrangling and mining it.
- Machine learning. Data scientists use machine learning, a type of artificial intelligence, to solve problems by building and applying models.
- Data visualization. Data scientists communicate their findings through data visualization, which presents information in a way that is easy to understand.
- Communication and collaboration. Data scientists must communicate their findings and collaborate with other data scientists, engineers, and business stakeholders.
In addition to these core skills, data scientists may also need knowledge of specific domains, such as finance, healthcare, or marketing. They may also require experience with specific tools and technologies, such as Version Control, Docker/Singularity, PyTorch, TensorFlow/Keras, MLFlow.
To become a research software engineer, a bachelor's degree in computer science or a related field is needed, gain experience as a software engineer, develop research skills, get a master's degree in computer science or a related field, and network with other research software engineers.
Tips for becoming a research software engineer:
- Be passionate and curious about research.
- Communicate ideas effectively to both technical and non-technical audiences.
- Collaborate well with other engineers, scientists, and mathematicians.
- Adapt to new technologies and methodologies as the field evolves.
Core skills for a research software engineer:
- Programming: proficient in at least one language (e.g. Python, Java, C++) with knowledge of data structures, algorithms, and software engineering principles.
- Problem solving: identify and solve complex problems through creative and innovative thinking.
- Communication: clear and concise communication with technical and non-technical audiences.
- Collaboration: effective teamwork with engineers, scientists, and researchers towards common goals.
- Self-learning: fast adaptation to new technologies and reading technical documentation or research papers.
- Adaptability: willingness to take on new challenges and flexibility to changing requirements.
Additional skills helpful for research software engineers:
- Domain knowledge: Having some knowledge of the specific domain they work in, such as healthcare, finance, or energy, helps to better understand the problems they are trying to solve.
- Machine learning skills: Skilled in machine learning, research software engineers will be in high demand.
- Cloud computing skills: Skilled in cloud computing, research software engineers will be able to build and deploy software more efficiently.
Professional development is essential for Data Scientists (DS) and Research Software Engineers (RSE) because the nature of the fields. Both DS and RSE are extremely fast moving fields. The state of the art is continuously changing.
- Formal Continuous Education (technical and soft skills)
- Technical Skills Training (projects, collaborations, co-learning)
- Professional Skills(leadership, management, interpersonal communications)
- Teaching and Mentoring Skills (teaching small workshops)
- Mentorship or Apprenticeship (collaborative, evidence based)
- Traditional mentorship
- Peer and Near-peer mentorship
- Group mentoring
- Project based
- Thematic based
- Expertise based
Created: 08/28/2023; Updated: 08/28/2023
Carlos Lizárraga
Carlos Lizárraga, Data Lab, Data Science Institute, University of Arizona, 2023.
