Data Science for AI

4 Interactive Sessions

Short, interactive sessions: watch it work, try it, then build it yourself. You can go deeper at any time in the written chapters.

1. 1

   Use a line to predict a number

   A model can be as simple as a line through your data. Find the trend, then read new answers off the line.

2. 2

   Teach a model to choose a category

   When the answer is a category, the model learns a boundary that separates the categories. You grade it with precision and recall.

3. 3

   Find patterns in data with no answers

   Even with no labels, a model can find patterns. It can group similar things (clustering) and flag the one that does not fit (anomaly detection).

4. 4

   Choose the right kind of model

   A neural network builds its own features from raw data. That is powerful for photos and sound, and too much for a spreadsheet. The skill is matching the kind of model to the problem.

How this certificate is scored

Your certificate shows how many of your written answers met the rubric below, checked by an AI grader. Anyone who checks your certificate sees it.

• You answer 6 short questions, one for each module, in your own words (at least 40 words each).
• An AI grader checks each answer against what its module asks for: it must answer the question, use the course's practice case specifically, and be correct.
• To pass, at least 5 of the 6 answers must meet that bar. The certificate says the answers were graded by AI.

The rubric

1. Module 1, Data Thinking: EDA skills: distributions, quality issues, correlation vs causation.
2. Module 2, Regression & Prediction: Linear/logistic regression, features, overfitting, evaluation metrics.
3. Module 3, Classification & Clustering: Decision trees, random forests, K-means, precision/recall trade-offs.
4. Module 4, Time Series & Forecasting: Trend decomposition, ARIMA, anomaly detection, chronological validation.
5. Module 5, Neural Networks Demystified: Layers, backpropagation, CNNs, transformers, when NNs are overkill.
6. Module 6, From Statistics to Foundation Models: Paradigm selection framework: classical ML vs DL vs foundation models.

What you walk away with