Machine Learning Scientist in Python (DataCamp): 85 hours, US Pricing & Syllabus (2026)

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Facts checked 8 September 2026 against DataCamp’s official page ·

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· Next check October 2026

Course and activity access

Career and Skill tracks: Basic Not included; Premium Included. This program is an official DataCamp Track.. Official source Official source

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Machine Learning Scientist in Python: USD 35; position among 38 other courses and tracks with accepted USD prices. Payment scopes differ. This price checked 2026-09-08. Registry snapshot 2026-09-25.

See the full registry price landscape. Prices retain their stated payment scope; they are not comparable completion costs.

What’s covered

Supervised Learning with scikit-learn
Predictive Modeling for Agriculture
Unsupervised Learning in Python
Clustering Antarctic Penguin Species
Linear Classifiers in Python
Machine Learning with Tree-Based Models in Python
Predicting Movie Rental Durations
Extreme Gradient Boosting with XGBoost
Cluster Analysis in Python
Dimensionality Reduction in Python
Preprocessing for Machine Learning in Python
Machine Learning for Time Series Data in Python
Feature Engineering for Machine Learning in Python
Model Validation in Python
Hyperparameter Tuning in Python
Machine Learning Fundamentals in Python
Natural Language Processing (NLP) in Python
Natural Language Processing with spaCy
Feature Engineering for NLP in Python
Introduction to Deep Learning with PyTorch
Intermediate Deep Learning with PyTorch
Image Processing in Python
Introduction to PySpark
Machine Learning with PySpark
Winning a Kaggle Competition in Python

Prerequisites: The prerequisite panel says no prerequisites; the FAQ says suitable for beginners.

Explore DataCamp

What does the DataCamp Machine Learning Scientist in Python curriculum include?

The Machine Learning Scientist in Python track consists of 21 interactive courses spanning various machine learning disciplines. The curriculum covers foundational and advanced topics, including supervised and unsupervised learning, linear classifiers, and tree-based models. Learners study specific tools and libraries such as scikit-learn, XGBoost, spaCy for natural language processing, PyTorch for deep learning, and PySpark for distributed machine learning. The program also includes bonus materials, specifically three projects and one bonus skill assessment, such as clustering Antarctic penguin species and predictive modeling for agriculture, providing hands-on experience with real datasets.

How much does the Machine Learning Scientist in Python program cost and how long is it?

DataCamp estimates that it typically takes 85 hours to complete the Machine Learning Scientist in Python track. The program does not have a single fixed purchase price. Instead, it requires a DataCamp Premium subscription, which is available for USD 35 per month at the regular monthly rate. This monthly subscription grants access to the platform and the track's content, but it does not represent a guaranteed total cost to finish the program. The final amount depends on the subscription route and billing periods selected. Annual offers are separate alternatives, and the listed workload does not guarantee a completion date or establish whether bonus activities are included in those hours.

What are the prerequisites and credential outcomes for this DataCamp track?

DataCamp states that there are no prerequisites for the Machine Learning Scientist in Python track, noting in their official documentation that it is suitable for both beginners and experienced programmers. The curriculum is designed to gradually increase in complexity. Upon finishing the 21 courses within the track, learners earn a Statement of Accomplishment from DataCamp. This credential acknowledges track completion and can be added to a resume or LinkedIn profile. However, it is a completion statement rather than a formal industry certification. The reviewed terms do not establish a separate exam requirement, expiry, or renewal for this track credential.

What you actually do

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

What the credential is

Credential type: Statement of Accomplishment · www.datacamp.com / machine-learning-scientist-with-python
Credential: Statement of Accomplishment · www.datacamp.com / machine-learning-scientist-with-python
Issuer: DataCamp · www.datacamp.com / machine-learning-scientist-with-python
Sharing: Add to LinkedIn profile, resume or CV; share on social media and in performance review. · www.datacamp.com / machine-learning-scientist-with-python
Credential scope: Track completion credential; separate industry certification inclusion in Premium does not make this track an exam certification. · www.datacamp.com / machine-learning-scientist-with-python

Who should skip it

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

Alternatives in the registry

Programs from other providers, a comparison and a guide.

Change log

2026-09-08: Initial review of official program, curriculum, credential and subscription-access sources. · Source

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