Program profile · official sources
Machine Learning Scientist in Python
The Machine Learning Scientist in Python track by DataCamp is an online program with 85 listed hours covering supervised, unsupervised, and deep learning. Designed for aspiring machine learning scientists, it introduces Python programming concepts using real-world datasets. The curriculum features 21 interactive courses covering topics like scikit-learn, XGBoost, natural language processing with spaCy, and deep learning with PyTorch. Learners explore feature engineering, time series data, and encounter three bonus projects and one bonus skill assessment. DataCamp states there are no prerequisites, making it suitable for beginners. Access requires a Premium subscription at USD 35 per month regular, which is not a guaranteed total completion cost. Upon finishing the 21 courses, learners earn a Statement of Accomplishment.
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
- Price
- DataCamp Premium — USD 35/month for course content, billed monthly at the regular price. (see provider page)
Premium for Individuals. English public pages with USD explicitly selected; visitor country not stated. Monthly regular access is USD 35 with no monthly promotion shown. Annual regular USD 330 and promotional USD 165 are separate billed totals; no promotion expiry or additional account/geographic restriction is stated. Access price is not a guarantee of track completion. · Official pricing - Duration
- 85 hours
- Level
- Beginner
- Format
- Online track with interactive courses, projects and a bonus skill assessment
- Credential
- Statement of Accomplishment
- Prerequisites
- The prerequisite panel says no prerequisites; the FAQ says suitable for beginners.
Sources ↗
- Price — https://www.datacamp.com/pricing?period=monthly
- Duration — https://www.datacamp.com/tracks/machine-learning-scientist-with-python
- Official program page — https://www.datacamp.com/tracks/machine-learning-scientist-with-python
- Provider subscription terms — https://www.datacamp.com/pricing?period=monthly
Course and activity access
Career and Skill tracks: Basic Not included; Premium Included. This program is an official DataCamp Track.. Official source Official source
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.
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 · 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
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
Related by topic or level.
- IBM Generative AI Engineering Professional Certificate — Coursera Plus Monthly — USD 59 / month regular; USD promotional terms; not available to residents of India. Outside the US, local currency and pricing are shown at checkout.; Estimated 6 months at 6 hours a week; self-paced.
- Associate AI Engineer for Data Scientists — DataCamp Premium — USD 35/month for course content, billed monthly at the regular price; 40 hours of listed content.
- AI engineer — Check official price; 32 hours of content.
Change log
- : Initial review of official program, curriculum, credential and subscription-access sources. · Source