Computer science fundamentals.Learning computer science fundamentals and programming is not just for computer science engineering grads anymore. All engineers should devote some time to learning computer technology, even those studying fluid power or mechanical engineering. An engineer should be familiar with data structures, algorithms, and computer memory manipulation to be ready for any engineering career. Engineers should consider participating in programming hackathons and practice problems to hone their IT skill.
Data modeling. This skill helps engineers estimate datasets’ underlying structures to identify useful patterns. It also helps in identifying correlations between data, data clusters, and so on, which can be used to detect anomalies and regressions. One key aspect of data modeling is the continual evaluation of a given model’s integrity using classification or regression measures. This is often combined with strategies such as split testing and randomized cross-validation to identify errors, tweak the model, and apply algorithms.
Probability and statistics. The field of probability and statistics is closely entwined with machine-learning algorithms. These algorithms, when applied with various statistical parameters such as variance, median, and mean, can remove data uncertainties. Using a blend of these techniques, engineers can build and validate viable models from observed data. In essence, machine learning algorithms are extensions of statistical data modeling procedures.
System design. This skill lets engineers understand how small components fit into a larger system of products and services. Creating a system of component interfaces—where each piece connects and communicates with the other via database queries, library calls, and REST APIs—requires an intelligent system design. A carefully-articulated design prevents bottlenecks and lets algorithms handle varying volumes of data. System design is backed by software engineering best practices that include requirements gathering, version control, testing, modularity, and documentation.