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Full Stack Data Professional Course

Train in Data Science and Data Engineering

32 hours (16 classes)Live online via Google MeetFrom scratch

At Webstarted Academy we offer specialized programs to train you in the world of data. The Full Stack Data Professional Course is designed to develop key skills in both Data Science and Data Engineering, so you can work with data from end to end.

Who it is for

This is for you if you want to get started in the world of data or take the leap toward a more complete profile. We cover the fundamentals from scratch and advance towards professional tools and practices.

Methodology

Classes are online and live via Google Meet. They are all recorded and shared along with supplementary material, so if you can’t attend live, you can access the recording whenever you want.

The course does not yet have a confirmed start date. If you are interested, leave us your details and we will notify you when we open the next edition.

By the end you will be able to

  • Analyze and transform data to support decision-making.
  • Write SQL queries and work with databases.
  • Use Python to manipulate and process data.
  • Create visualizations and dashboards that communicate results.
  • Understand the role of Data Science and Data Engineering in a complete data pipeline.

Syllabus

  1. 1
    Module 1: Data and analysis fundamentals

    Data types, the data lifecycle, and the foundations of analysis for decision-making.

  2. 2
    Module 2: SQL and databases

    Queries, relational modeling, and data manipulation in databases.

  3. 3
    Module 3: Python for data

    Python programming applied to analysis, using libraries like pandas to clean and transform data.

  4. 4
    Module 4: Visualization and storytelling

    Building dashboards and communicating results effectively through visualization.

  5. 5
    Module 5: Introduction to Data Science

    Applied statistics and first models to find patterns and generate predictions.

  6. 6
    Module 6: Data Engineering and production deployment

    Data pipelines, automation, and best practices for taking solutions to production.