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Practical Python: Data Tables Manipulation & Visualization

Learn Python programming while applying it to data analysis and visualization. We will prepare customized datasets based on your interests.

This course is no longer available.

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Professionals from

Latesys
Santander
Iberdrola
Banco Bilbao Vizcaya Argentaria
endesa

Course overview

Bring your own data and practical cases

An exceptional structure that progressively challenges you to develop a better version of your data application.


You can even bring your own data tables because the materials are adapted to work with any.


You'll get direct support to focus on the essential matters of building data apps so that, after the course, you'll have a great MVP.


P.S.: Don't worry if you don't have data or a practical case, we'll give you several proposals.

Reason the code; don't assume you know what you don't.

01

Instead of wasting your time adapting solutions from Google/ChatGPT, we'll teach you how to ask and adapt their solutions step by step.

02

The instructor will monitor students' screen to catch most common errors and explain best practices to optimize your programming skills.

03

Instense learning program with real-world exercises that trains lifelong skills you'll apply to your professional projects after finishing.

Outcomes

End-to-end project

You will build the application during the course by downloading, preprocessing, filtering, and aggregating the raw data.


Jesús will solve all your questions and doubts until successful completion.

Bring your own data

​If you are a professional who wants to develop solutions with the data you manage at your workplace, you can bring your data to build the web application.


​Otherwise, we've covered you with datasets from energy, finance, medicine, or any industry; I'll get it sorted for you.

Published data app on URL

You'll publish the data application you develop to a URL that you can share (public or private) with friends, colleagues, and customers.

No to redundant theory; yes to practical concepts

Instead of imposing the lessons with theory, you'll practice the methodology with exercises meticulously prepared to solve the most common errors you experience.

Guided hands-on sessions to solve problems

Why spend your time figuring things out alone when you can benefit from practice sessions with Jesús?


He will ensure you stay on track and apply the best coding practices, enhancing your learning experience without overlooking the value of independent problem-solving.

What’s included

Jesús López

Live sessions

Learn directly from Jesús López in a real-time, interactive format.

Lifetime access

Go back to course content and recordings whenever you need to.

Community of peers

Stay accountable and share insights with like-minded professionals.

Certificate of completion

Share your new skills with your employer or on LinkedIn.

Maven Guarantee

This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.

Course syllabus

3 live sessions • 20 lessons • 2 projects

Week 1

Jun 6—Jun 8

    Jun

    6

    Session 1

    Thu 6/68:00 PM—11:00 PM (UTC)

    Jun

    7

    Session 2

    Fri 6/78:00 PM—11:00 PM (UTC)

    Jun

    8

    Session 3

    Sat 6/83:00 PM—6:00 PM (UTC)

    Python Thinking

    • 📄

      Autocompletion tool to avoid memorization

    • 📄

      Adapt examples from documentation

    Data visualization

    • 📄

      Bivariate

    • 📄

      Multivariate

    • 📄

      Dataviz libraries (summary)

    • 📄

      Proficient self-learning

    Join

    • 📄

      Concatenate

    • 📄

      Merge

    Filter

    • 📄

      Essential DataFrame concepts

    • 📄

      Conditionals to select rows

    • 📄

      Locate by labels in table

    Web App

    • 📄

      Develop backend logic

    • 📄

      Simulate frontend input

    • ✍️

      Publish app and share URL

      Submit by May 28

    Aggregate

    • 📄

      Automatic temporal resampling

    • 📄

      Group by categories

    • ✍️

      Streamlit web application v2

      Submit by May 28
    • 📄

      Pivot values to columns

Bonus

    Data API Examples

    • 📄

      EIA (Energy Information Administration) [US Energy]

    • 📄

      FRED (Federal Reserve Economic Data) [Macroeconomy]

    • 📄

      Alpaca [Trading]

    • 📄

      ESIOS API REE [Spain Energy]

What students are saying

Meet your instructor

Jesús López

Jesús López

Instructor @LinkedIn Learning | +8,000h 1:1 Training & Consulting Data w/ Python

Learn more about my professional background here.

Frequently asked questions