--- title: "Titanic Data Engineering with Databricks and Python" canonical: "https://data-driven.com/blog/data-science-for-dummies-data-engineering-with-titanic-dataset-databricks-python-tech-talk-3-of-9/" description: "An archived Tech Talk showing how the Titanic dataset was prepared and engineered in Databricks before model training." --- Tech Talk archive ยท Part 3 of 9 # Prepare Titanic data for _model training_ An archived Tech Talk showing how the Titanic dataset was prepared and engineered in Databricks before model training. 3 May 2019 2 min read Updated 25 Aug 2026 Reader brief What this archive contains 1. This 2019 session focused on preparation and feature engineering. 2. The example used the Titanic dataset, Databricks, and Python. 3. Product interfaces and recommended workflows may have changed. ![Data Science for Dummies Tech Talk series artwork](/_astro/data-science-for-dummies.DyQBFWaJ_1hYo6T.webp) Dated reference This article was published on 3 May 2019. Product details, interfaces, pricing, and linked resources may have changed since then, so confirm current guidance before acting. This 2019 Tech Talk used the Titanic dataset to show the work that happens before model training. The session covered ingestion, preparation, and feature engineering in Databricks with Python. The archive preserves the session outline. It does not reproduce a current Databricks tutorial, and the original embedded slide player did not survive the site migration. ## Why preparation took center stage Even a small dataset can expose difficult questions. A field may be missing, a category may need a consistent definition, or a feature may only make sense with domain knowledge. The talk used those decisions to show why data engineering and data science need to work together. Preparing the table is not a neutral step: every transformation changes what the model can learn. ## Session path 1. Inspect the Titanic source fields and missing values. 2. Decide which fields could support the prediction task. 3. Create features in a repeatable notebook workflow. 4. Produce a training table for the next modeling session. The original Slideshare reference was `150788201`. Keep that ID with the archive if the deck is recovered. ![Archived photo used with the Titanic Tech Talk series](/_astro/20190502_224959.ByjsTLjx_Z20ELVs.webp) ## The nine-part series 1. Data Science overview with Databricks 2. Titanic survival prediction with Azure Machine Learning Studio and Kaggle 3. **Data engineering with the Titanic dataset, Databricks, and Python** 4. Titanic with Databricks and Spark ML 5. Titanic with Databricks and Azure Machine Learning 6. Titanic with Databricks and AutoML 7. Titanic with Databricks and MLflow 8. Titanic with .NET and ML.NET 9. Deployment, DevOps, MLOps, and production Browse the [Data Science for Dummies archive](/tag/data-science-for-dummies/) for the parts that remain available. Filed under - [beginner](/tag/beginner/) - [data-science](/tag/data-science/) - [databricks](/tag/databricks/) - [learning](/tag/learning/) - [python](/tag/python/) - [spark](/tag/spark/) Continue reading ## Related perspectives [![Databricks artwork used for the 2019 introductory Tech Talk](/_astro/Databricks-unified-analytics-background.Ei32byCp_Z20b5Ar.webp)](/blog/data-science-for-dummies-data-science-overview-with-databricks-tech-talk-1-of-9/) Databricks ### [Data Science Overview with Databricks](/blog/data-science-for-dummies-data-science-overview-with-databricks-tech-talk-1-of-9/) An archived introductory Tech Talk covering the Databricks workflow and its place in the nine-part Data Science for Dummies series. [![Delta Lake and Spark lakehouse meetup artwork from October 2021](/_astro/Deep-dive-into-building-a-Data-Lakehouse-with-Delta-Lake-and-Spark-1.D6ycXdHU_Z1FsmPv.webp)](/blog/deep-dive-into-building-a-data-lakehouse-with-delta-lake-and-spark/) Databricks ### [Delta Lake and Spark Lakehouse Webinar](/blog/deep-dive-into-building-a-data-lakehouse-with-delta-lake-and-spark/) An archived Sydney Databricks Meetup session on building an ingestion pipeline with Delta Lake and Spark, with source details retained. [![Azure sentiment analysis workflow using Databricks and PySpark](/_astro/Azure-cognitive-services.oSFay03s_ZDdbnM.webp)](/blog/azure-cognitive-services-sentiment-analysis-v3-0-using-databricks-pyspark/) Databricks ### [Azure Sentiment Analysis with Databricks PySpark](/blog/azure-cognitive-services-sentiment-analysis-v3-0-using-databricks-pyspark/) Follow a legacy PySpark walkthrough that sends text to Azure's sentiment API, parses the JSON response, and joins scores to source data. ## Continue the Data Science for Dummies series Browse the retained series pages and check each publication date before following product steps. [Browse the series](/tag/data-science-for-dummies/) [Explore Databricks consulting](/our-story/databricks-partner-consulting/)