Data Scientist Professional Certificate

Turn raw data into findings and predictions: Python, data wrangling, probability, statistics, machine learning and forecasting.

Start with Python for data science and the pandas and NumPy skills to wrangle real data, then build a firm footing in probability and the practical statistics data scientists use every day. Go on to visualisation, feature engineering and model evaluation, classification and clustering, forecasting time series and building recommender systems, and finish by explaining what your models do and why. Every course is hands-on, with labs and a capstone project your instructors review.

Prepares you for

Data scientist, Applied scientist, Machine learning engineer, Quantitative analyst

10 courses in this Professional Certificate

  • Python Foundations for Data Science: Learn the Python data scientists use every day: the IPython shell, built-in data structures, functions and lambdas, Jupyter notebooks, and tidy scripts that cope with messy data. (Beginner, about 13 hours)
  • Data Wrangling with pandas and NumPy: Turn messy, scattered data into clean, analysis-ready tables: vectorised NumPy, pandas' index and dtypes, missing data, validated merges, reshaping and group operations. (Intermediate, about 15 hours)
  • Foundations of Probability and Statistics: Build the probability that data science rests on: rules and counting, Bayes' theorem, discrete and continuous distributions, expectation and inference, derived, calculated and checked by simulation. (Intermediate, about 14 hours)
  • Practical Statistics for Data Scientists: Apply statistics to real decisions: robust summaries, the bootstrap, A/B tests done properly, regression with diagnostics, and logistic models judged by the costs of their errors. (Intermediate, about 15 hours)
  • Data Visualisation with Matplotlib, Seaborn and Plotly: Choose the right chart, build it precisely with Matplotlib, explore data with seaborn, share interactive charts with Plotly, and make every figure clear, accessible and ready to publish. (Beginner, about 12 hours)
  • Feature Engineering and Model Evaluation: Prepare real, messy data for machine learning, evaluate models reliably with cross-validation, tune them honestly, and wrap it all in leak-proof scikit-learn pipelines. (Intermediate, about 15 hours)
  • Classical Classification and Clustering: Apply and tune logistic regression, k-NN, SVMs, decision trees and ensembles, then find structure in unlabelled data with k-means and PCA, all in scikit-learn. (Intermediate, about 16 hours)
  • Time Series Analysis and Forecasting: Analyse and forecast time series in Python: decomposition, stationarity, exponential smoothing, SARIMA and gradient boosting, tested with honest backtests and prediction intervals. (Intermediate, about 15 hours)
  • Recommender Systems: From Collaborative Filtering to Two-Tower Models: Build, evaluate and improve recommenders in Python: neighbourhood methods, matrix factorisation, implicit ALS and two-tower retrieval in PyTorch, with honest offline evaluation and cold-start strategies. (Advanced, about 15 hours)
  • Explainable AI: Interpreting Models with SHAP and Beyond: Explain machine learning models in Python: permutation importance, partial dependence, SHAP, local surrogates and counterfactuals, with the checks that show when an explanation can be trusted. (Intermediate, about 13 hours)