Comparison of DataCamp Machine Learning Scientist in Python and DataCamp Machine Learning Engineer: which fits your learning goal?

Machine Learning Scientist in Python

• Supervised Learning with scikit-learn — course: Supervised prediction with real-world datasets and scikit-learn · Source
• Predictive Modeling for Agriculture — bonus project: Supervised learning and feature selection for crop cultivation · Source
• Unsupervised Learning in Python — course: Clustering, transformation, visualization with scikit-learn and scipy · Source
• Clustering Antarctic Penguin Species — bonus project: K-means clustering of penguin species · Source
• Linear Classifiers in Python — course: Logistic regression and SVM · Source
• Machine Learning with Tree-Based Models in Python — course: Tree-based models and ensembles for regression and classification · Source
• Predicting Movie Rental Durations — bonus project: Build and evaluate a regression model · Source
• Extreme Gradient Boosting with XGBoost — course: Classification and regression using gradient boosting · Source
• Cluster Analysis in Python — course: Hierarchical and k-means clustering using SciPy · Source
• Dimensionality Reduction in Python — course: Reducing data dimensionality · Source
• Preprocessing for Machine Learning in Python — course: Clean and prepare data · Source
• Machine Learning for Time Series Data in Python — course: Feature engineering and machine learning for time series · Source
• Feature Engineering for Machine Learning in Python — course: Create features to improve model performance · Source
• Model Validation in Python — course: Model validation techniques · Source
• Hyperparameter Tuning in Python — course: Grid, random and informed search · Source
• Machine Learning Fundamentals in Python — bonus skill assessment · Source
• Natural Language Processing (NLP) in Python — course: Text preprocessing through transformer models · Source
• Natural Language Processing with spaCy — course: Train NLP models, extract information and match patterns · Source
• Feature Engineering for NLP in Python — course: Extract text features for machine learning · Source
• Introduction to Deep Learning with PyTorch — course: Neural networks, hyperparameters, classification and regression · Source
• Intermediate Deep Learning with PyTorch — course: CNNs, RNNs, LSTMs and GRUs · Source
• Image Processing in Python — course: Process, transform and manipulate images · Source
• Introduction to PySpark — course: Process, query and optimize large datasets · Source
• Machine Learning with PySpark — course: Decision trees, regression, ensembles and pipelines · Source
• Winning a Kaggle Competition in Python — course: Approach Kaggle competitions · Source

Machine Learning Engineer

• Supervised Learning with scikit-learn — course: Prediction using scikit-learn · Source
• MLOps Concepts — course: Move local models into production · Source
• Introduction to Shell — course: Unix command line and automation · Source
• Predictive Modeling for Agriculture — bonus project: Supervised learning and feature selection · Source
• MLOps Deployment and Life Cycling — course: MLOps framework, lifecycle and deployment · Source
• Introduction to MLflow — course: Tracking, projects, models and model registry · Source
• Predicting Temperature in London — bonus project: Experiment to find the best temperature prediction model · Source
• ETL and ELT in Python — course: Reliable performant data pipelines · Source
• Introduction to Data Quality with Great Expectations — course: Data quality for data science/engineering · Source
• Introduction to Data Versioning with DVC — course: ML data management, pipelines and model evaluation · Source
• Monitoring Machine Learning Concepts — course: Data/concept drift and model degradation · Source
• Monitoring Machine Learning in Python — course: Build a basic monitoring system · Source
• Introduction to Docker — course: Containers and images · Source
• CI/CD for Machine Learning — course: GitHub Actions and Data Version Control · Source
• Machine Learning Engineer — bonus skill assessment · Source

Machine Learning Scientist in Python

Credential type

Statement of Accomplishment · Source

Credential

Statement of Accomplishment · Source

Issuer

DataCamp · Source

Sharing

Add to LinkedIn profile, resume or CV; share on social media and in performance review. · Source

Credential scope

Track completion credential; separate industry certification inclusion in Premium does not make this track an exam certification. · Source

Machine Learning Engineer

Credential type

Statement of Accomplishment · Source

Credential

Statement of Accomplishment · Source

Issuer

DataCamp · Source

Sharing

Add to LinkedIn profile, resume or CV; share on social media and in performance review. · Source

Credential scope

Track completion credential; separate industry certification inclusion in Premium does not make this track an exam certification. · Source

Machine Learning Scientist in Python

• Learners seeking a formal, exam-based professional certification rather than a platform completion credential. · Source
• Students looking for a fixed-price, lifetime-access program, as the sourced Premium route is subscription access rather than a completion fee. · Source
• Individuals primarily interested in R or other programming languages, as this track is exclusively focused on Python. · Source

Machine Learning Engineer

• Absolute beginners with no prior experience in Python data manipulation or machine learning model training and evaluation. · Source
• Learners seeking a formal industry certification exam rather than a Statement of Accomplishment. · Source
• Individuals who prefer purchasing a course for a single flat fee rather than paying a recurring monthly subscription. · Source

Machine Learning Scientist in Python

Choose the DataCamp Machine Learning Scientist in Python track if you want to broaden your capability to develop predictive models across various domains, including natural language processing, time series, and deep learning.

Machine Learning Engineer

Choose the DataCamp Machine Learning Engineer track if you already know how to train models and need specific operational training on how to deploy, version, and monitor those models in production environments.