IBM watsonx Data Scientist

A data scientist rarely lacks tools and often picks the wrong one. You weigh notebooks, automated model building and visual preparation against team skill, time, explainability and the need to reproduce the result.

• Notebooks and the Python data stack
• Automated model building with AutoAI
• Visual tools against code for data preparation
• Projects, assets and collaboration in watsonx
• Matching tool to team, timeline and audience
• Reproducibility and versioning of experiments

You build A tool-selection note for a project, with the reasons each tool was chosen or rejected.

Before any model, learn what is in the data. You summarize distributions, relationships and categories, plot them, and find the surprises that change what you build next.

• Data types and what they allow
• Central tendency, spread and skew
• Distributions and how to plot them
• Correlation and its limits
• Exploring categorical variables
• Spotting duplicates and inconsistent records
• Recording findings in a data profile

You build An exploratory notebook with a short data profile of a messy dataset.

Outliers and gaps bias a model quietly. You detect anomalies with several methods, judge whether each is an error or a signal, and handle missing values by deletion or imputation while guarding against leakage.

• Outliers by z-score and interquartile range
• Error or signal: deciding what an anomaly is
• Types of missingness and why they matter
• Deleting against imputing
• Simple and model-based imputation
• Avoiding leakage when imputing
• Documenting every cleaning decision

You build A cleaning pipeline that handles outliers and gaps and records why.

Models learn from features, not raw columns. You encode categories, scale and transform numbers, create features from dates and combinations, select the ones that help, and split data before fitting anything that learns from it.

• Encoding categorical variables
• Scaling and transforming numeric features
• Features from dates, text lengths and ratios
• Interactions and binning
• Feature selection: filter, wrapper and embedded
• Train and test splits before any fitting
• Spotting target leakage

You build A reusable feature pipeline that is fit on training data only.

When one outcome is rare, a model can look accurate while ignoring it. You recognize imbalance, choose between resampling and class weights, split data so the rare class survives, and pick metrics that expose the problem.

• Why accuracy misleads on rare outcomes
• Stratified splitting
• Oversampling and undersampling
• Synthetic oversampling and its risks
• Class weights in training
• Moving the decision threshold

You build A rare-event classifier compared across three imbalance strategies.

Train the main families of model and learn what each one assumes. You fit regression, tree-based and ensemble models, recognize overfitting from learning curves, and use cross-validation and tuning to get honest results.

• Linear and logistic regression
• Decision trees and ensembles
• Clustering with k-means
• Bias, variance and overfitting
• Regularization
• Cross-validation
• Hyperparameter search

You build Three candidate models trained and cross-validated on one prepared dataset.

The best score is rarely the best choice. You compare candidates on error cost, interpretability, speed and stability, and decide which model to take forward and what evidence supports the choice.

• Comparing models on the same folds
• Accuracy against interpretability
• Cost of different kinds of error
• Stability across data samples
• Using automated model building as a benchmark
• Recording the decision

You build A model comparison table that ends in a justified choice.

A metric is an argument about what matters. You choose regression and classification measures, read confusion matrices and curves, set a threshold from business cost, and examine where the model fails.

• MAE, RMSE and R-squared for regression
• Precision, recall and F1 for classification
• ROC and precision-recall curves
• Confusion matrices and thresholds
• Calibration of predicted probabilities
• Error analysis by segment
• Explaining performance to a stakeholder

You build An evaluation report that justifies a metric and a decision threshold.

Run one project through every stage: framing, tools, exploration, cleaning, features, training, selection and evaluation. The result is a model and a record of every decision behind it, ready for questions.

• Scoping the project
• Exploring and cleaning the data
• Engineering features
• Training and comparing models
• Evaluating and choosing
• Presenting the evidence

You build A finished modeling project with a decision log and an evaluation report.