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Data Visualization

Data Visualization

This data visualization course unlocks the power of presenting complex information clearly and effectively. Students learn to transform raw data into insightful visual narratives. We explore fundamental principles of visual perception and design, ensuring impactful communication. The curriculum covers a wide array of tools, from foundational spreadsheet software to advanced programming libraries like Python’s Matplotlib and Seaborn, and R’s ggplot2. Participants gain hands-on experience in creating various chart types, including bar charts, line graphs, scatter plots, and intricate dashboards. Emphasizing storytelling with data, the course teaches how to identify the right visualization for different datasets and audiences. It delves into techniques for data cleaning, preparation, and transformation, essential prerequisites for effective visualization.

Skills Covered

  • Programming Fundamentals
  • Object-Oriented Programming
  • Data Structures and Algorithms
  • File I/O and Exception Handling
  • Working with Libraries/Modules
  • Debugging and Testing

Data Visualization Training Course Content Overview

  • Python Basics: Syntax, variables, data types, operators.
  • Control Flow: Conditionals (if/else) and loops (for/while).
  • Functions: Defining and calling functions, arguments, return values.
  • Data Structures: Lists, tuples, dictionaries, sets.
  • Object-Oriented Programming (OOP): Classes, objects, inheritance.
  • File Handling: Reading from and writing to files.
  • Error Handling: Try-except blocks for managing errors.
  • Modules & Packages: Importing and using external libraries.
  • Debugging: Techniques for finding and fixing code issues.
  • Intro to Libraries: Basic use of common libraries

FAQ

It's ideal for beginners with no prior coding experience, as well as professionals in various fields looking to automate tasks, analyze data, or build applications.

No, typically no prior programming experience is required. Basic computer literacy is helpful.

You can pursue roles like Data Analyst, Web Developer, Automation Engineer, Machine Learning Engineer, or QA Engineer, among others.

  1. Rapidly evolving threat landscape

The duration varies, but foundational courses often range from 40 to 80 hours, spanning a few weeks to a couple of months.

Absolutely. Most quality Python training emphasizes practical exercises, coding challenges, and often a final project to solidify learning.