refactor: restructure monorepo for clean portfolio layout

- Move timesfm-forecast into apps/ directory
- Flatten Udacity portfolio projects from deep URL-encoded paths
  into data-engineering/01-XX numbered directories
- Remove old My-Data-Engineering-Portifolio/ parent directory
- Rewrite root README.md: professional overview with badges,
  project table, and repo structure diagram
- Create data-engineering/README.md with per-project descriptions
- Add README.md for 02-cassandra-modeling (was missing)
- Add README.md for 05-airflow-pipelines (was missing)
- Normalize capstone readme.md -> README.md
- Update .gitignore: add *.cfg, *.env, *.zip, *.sas7bdat,
  Jupyter checkpoints, IDE dirs; remove uv.lock exclusion
- Add dwh.cfg.example and dl.cfg.example credential templates
- Untrack real credential files (dwh.cfg, dl.cfg)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

apps/timesfm-forecast/tests/test_pipeline.py

@@ -0,0 +1,49 @@
+import numpy as np
+import pandas as pd
+
+from timesfm_app.core import ForecastConfig, ForecastPipeline, TimesFMService
+
+
+class FakeTimesFMService(TimesFMService):
+    """
+    Replace the model call with deterministic values for tests.
+    Returns predictions: [1, 2, ..., max_h] for each series in the batch.
+    """
+
+    def __init__(self):
+        pass
+
+    def forecast_batch(self, past_values, freq_code: int, max_h: int) -> np.ndarray:
+        base = np.arange(1, max_h + 1, dtype=float)
+        return np.tile(base, (len(past_values), 1))
+
+
+def test_pipeline_long_output_shape():
+    df = pd.DataFrame(
+        {
+            "date": pd.date_range("2023-01-01", periods=5, freq="ME").strftime(
+                "%Y-%m-%d"
+            ),
+            "value": [1, 2, 3, 4, 5],
+            "key": ["A"] * 5,
+        }
+    )
+
+    cfg = ForecastConfig(
+        model_name="dummy",
+        device="cpu",
+        batch_size=2,
+        timesfm_freq_code=2,
+        pandas_freq_fallback="M",
+        horizon_min=1,
+        horizon_max=3,
+    )
+
+    svc = FakeTimesFMService()
+    pipe = ForecastPipeline(svc=svc, config=cfg)
+    out = pipe.run(df)
+
+    assert len(out) == 3
+    assert out["key"].unique().tolist() == ["A"]
+    assert out["step"].tolist() == [1, 2, 3]
+    assert np.allclose(out["forecast"].values, [1.0, 2.0, 3.0])