Data Science, a multidisciplinary domain that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data, has grown exponentially in popularity in the past decade. As a result, a host of exciting career opportunities have become available. This article will demystify four such careers in the realm of Data Science.
A Data Scientist is often referred to as the 'sexiest job of the 21st century'. This role involves analyzing and interpreting complex digital data, such as the usage statistics of a website, to assist a business in its decision-making.
- Collecting, analyzing, and interpreting raw, complex datasets.
- Using statistical tools to interpret data sets and prepare reports.
- Using machine learning tools and statistical techniques to produce solutions to problems.
- Liaising with stakeholders to understand their business objectives and develop data-driven solutions.
- Strong knowledge in statistics, machine learning, and programming.
- Proficiency in using advanced statistical tools and languages like R, Python, SAS.
- Good understanding of data and database management.
A Data Analyst collects, processes, and performs statistical analyses on large datasets. They discover how data can be used to answer questions and solve problems.
- Interpreting data and analyzing results using statistical techniques.
- Developing and implementing data collection systems and other strategies that optimize statistical efficiency and data quality.
- Identifying, analyzing, and interpreting trends or patterns in complex data sets.
- Knowledge of statistics and experience using statistical packages for analyzing large datasets.
- Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
A Data Engineer is responsible for constructing data pipelines and often have to use complex tools and techniques to handle data at scale.
- Develop, construct, test and maintain data architectures.
- Align architecture with business requirements.
- Identify ways to improve data reliability, efficiency, and quality.
- Exceptional coding and programming skills.
- Deep understanding of databases and best engineering practices, including handling and logging errors, monitoring the system, building human-fault-tolerant pipelines, understanding how to scale up, addressing continuous integration, knowledge of batch vs stream processing.
Machine Learning Engineer
Machine Learning Engineers create data funnels and deliver software solutions. They are computer programmers, but their focus goes beyond specifically programming machines to perform specific tasks. They create programs that will enable the machine to take actions without being specifically directed to perform those tasks.
- Study and transform data science prototypes.
- Design machine learning systems.
- Research and implement appropriate machine learning algorithms and tools.
- Develop machine learning applications according to requirements.
- Profound knowledge in machine learning.
- Ability to write robust code in Python, Java and R.
- Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).
Knowing the responsibilities and required skills for each of these roles will help you make an informed decision about which Data Science career path is right for you. It's important to remember that continuing education and professional development are crucial in this rapidly evolving field.