As a data science lead, I see it as my responsibility to guide junior data scientists on training and professional development. The field is broad, and I often see two common profiles: those with strong mathematical and statistical foundations but limited software engineering experience (e.g., object-oriented programming, unit testing, CI/CD), and those with solid computer science backgrounds but less exposure to the mathematical side. To make informed recommendations, I regularly complete courses and exercises myself and make a habit of daily practice. In this post, I review DataCamp’s Machine Learning Engineering course, which I recently completed.
Course
The course is organised into theoretical and practical modules, plus independent projects. It starts with core ML in scikit-learn—classification, regression, model fine-tuning, and preprocessing with pipelines—then moves into MLOps: taking models from notebooks to production (introduction, design and development, deployment, and maintenance) and a modern lifecycle view (develop for deployment, deploy and run, monitor and maintain). The practical modules focus on specific tools and workflows—for example, experiment tracking and governance with MLflow (tracking, models, registry, projects), data versioning and pipeline reproducibility with DVC, data quality with Great Expectations, and ETL-focused data pipelines (from basics through advanced techniques and operations). Platform skills are covered through the Unix command line (files and data manipulation, combining tools, batch processing, creating simple utilities), Docker (running containers, writing images, securing images), and CI/CD for ML with GitHub Actions and DVC (YAML basics, actions, CI for training runs, and comparing runs/hyperparameter tuning). Two short projects tie the content together: a supervised learning project in agriculture and a time-series regression project forecasting London temperatures.
Things I Liked
The theoretical content is, in my view, the track’s strongest asset. Modules on model management and the ML lifecycle are grounded in established engineering practice. The Monitoring Machine Learning Concepts module explains the challenges of monitoring models in production, including data and concept drift, and outlines methods to address model degradation. I also appreciated how the lifecycle-focused MLOps modules (from “MLOps in a Nutshell” through “Monitor and Maintain”) map cleanly to real-world phases. The MLflow and DVC coverage reinforces reproducibility and governance, which are recurring pain points on production teams.
Mixed Views
I have mixed views on the practical components. Some modules—such as those on using MLflow for model deployment and DVC for data version control—are well placed and genuinely useful. Others are more debatable. For example, ETL/ELT is demonstrated using Pandas. While Pandas is widely used and helpful for many data engineering tasks, relying on it alone is often insufficient for production-grade pipelines; the ETL course does a good job of introducing concepts and advanced techniques, but it would benefit from more emphasis on orchestration, dependency management, and backfills. Similarly, the CI/CD module with GitHub Actions is a solid introduction, though additional depth on environments, secrets, and release gating would make it more directly applicable to enterprise setups.
Things I Didn’t Like
Two modules could be stronger. The Introduction to Shell module progresses from simple commands (e.g., cp, grep) to basic scripts. The idea is sound, but the delivery feels uneven: it emphasises memorising commands over practical workflows such as piping, redirection, error handling, and reproducible scripting. A greater focus on real-world tasks—data inspection, batch processing, and automation—would make it more effective. The ETL/ELT module also feels underpowered for production use: centring pipelines on Pandas limits scalability and reliability; more on orchestration, data contracts, schema evolution, and recovery patterns would improve its relevance.
Verdict
Overall, the track explains the core theoretical concepts well and reinforces them with practical—if basic—examples. The end-to-end flow from scikit-learn fundamentals through MLOps, deployment, and monitoring is coherent, so learners can grasp how the pieces fit together. That said, a few modules feel light on depth: the Introduction to Shell is very basic and prioritises command recall over real workflows, and the ETL content centred on Pandas is not representative of typical production pipelines, which are usually far more complex, integrate multiple systems, and often rely on distributed engines such as Spark. Treated as an introduction to ideas and tooling, the track is a solid starting point; plan to supplement the “light” modules with more production-focused resources.