Roles, Skills, and Knowledge
Data Science (DS) and Machine Learning (ML) are two of the hottest fields in industry.
Data Science involves using Computer Science and Statistics to generate insights from large amounts of data. Machine Learning is a specific approach to Data Science which involves training algorithms to perform a task.
Engineering and Research roles
Data Science jobs have been ranked #1 in 2019 in terms of median base salary ($108k), job satisfaction, and the number of job openings. Here are five of the most common Data Science roles. Note that each employer defines these roles differently so it's important to understand the specific knowledge and skills required for each.
Data Scientist
Models large amounts of unstructured data using technology to run experiments and then analyzes and extracts insights and trends.
Data Engineer
Transforms unstructured data to enable analysis and experimentation by Data Scientists, using knowledge of system architecture, programming, user interface, database design and configuration.
Data Analyst
Uses Statistics and tools (i.e., SQL, R, Excel) to identify insights, often creating visualizations.
Machine Learning Scientist
Researches, develops experiments, implements new models and architectures, prototypes implementations, and designs new architectures for real-world problems.
Machine Learning Engineer
Integrates research done by Machine Learning Scientists into the product by determining which solution works best for their use case and constraints, often building a system around a model.
Skills and Knowledge
Along with advanced technical knowledge, organizations are looking for candidates with problem-solving and communication skills. Related coursework, projects, and experience are desired.
Data Science
Python, R, D3, Apache Spark, Apache Hadoop, Apache Pig, Apache Hive, MapReduce, NoSQL databases, Github, Tableau. Advanced knowledge of Statistics and Mathematics.
Machine Learning
Python, C/C++, TensorFlow, PyTorch, scikit-learn. Advanced knowledge of Linear Algebra, Statistics, and Calculus.
CICS offers courses in Artificial Intelligence, Data Science, Machine Learning, Natural Language Processing, Computer Vision, Deep Learning and more which are great for prospective Data Scientists and Machine Learning Engineers/Scientists. Some of these courses are graduate-level courses, but undergraduate students may be able to enroll.
For Data Science, talented students from both undergraduate and graduate programs are hired (Kaggle: 44% Master's, 32% PhD, 20% Bachelors). For Machine Learning, Master's and PhD are the primary focus. Undergraduates should consider going to grad school to pursue research and experience in the field. You should also explore research opportunities.
