This is where we host our tutorial notebooks, showing how to use our API, Python API wrapper (documentation here), and giving tutorials and tips on common building data science topics.
| Notebook | Description | Colab |
|---|---|---|
| API Introduction | A brief overview introducing the Onboard Python client. |  |
| Exploring Data Points | A further look into the data types in the Onboard Ontology | |
| Introduction to Timeseries Data | Introduction to timeseries analysis with the Onboard client. | |
| Notebook | Description | Colab | Medium |
|---|---|---|---|
| Timeseries Cleaning and Imputation | Basic timeseries cleaning and data imputation. | |
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| Forecasting, Part 1: Feature Selection | Part 1: feature exploration and selection for a forecasting model. | |
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| Forecasting, Part 2: Trend Forecasting | Part 2: making timeseries forecasting models in Prophet. | |
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| Outlier and Anomaly Detection | Finding outliers and anomalies in data, including more Prophet models! | |
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Open Source Spotlight: open-fdd |
Automating fault detection and diagnosis with open source package, open-fdd. |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.