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Data Visualization with Python and Matplotlib

Load and organise data from various sources for visualisation

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Intermediate

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6 Hours

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Load and organise data from various sources for visualisation

Course Information

About this course:

More and more people are realising the vast benefits and uses of analysing big data. However, the majority of people lack the skills and the time needed to understand this data in its original form. That's where data visualisation comes in; creating easy to read, simple to understand graphs, charts and other visual representations of data. Python 3 and Matplotlib are the most easily accessible and efficient to use programs to do just this. With over 58 lectures and 6 hours of content, this course covers almost every major chart that Matplotlib is capable of providing. Intended for students who already have a basic understanding of Python, you'll take a step-by-step approach to create line graphs, scatter plots, stack plots, pie charts, bar charts, 3D lines, 3D wire frames, 3D bar charts, 3D scatter plots, geographic maps, live updating graphs, and virtually anything else you can think of! Starting with basic functions like labels, titles, window buttons and legends, you'll then move onto each of the most popular types of graph, covering how to import data from both a CSV and NumPy. You'll then move on to more advanced features like customised spines, styles, annotations, averages and indicators, geographical plotting with Basemap and advanced wireframes. This course has been specially designed for students who want to learn a variety of ways to visually display python data. On completion of this course, you will not only have gained a deep understanding of the options available for visualising data, but you'll have the know-how to create well presented, visually appealing graphs too.

The average salary for Python Developer is $92,000 per year.

Course Objective: 

After completing this course, students will be able to: 

  • Learn Big Data Python
  • Visualise multiple forms of 2D and 3D graphs; line graphs, scatter plots, bar charts, etc.
  • Load and organise data from various sources for visualisation
  • Create and customise live graphs
  • Add finesse and style to make your graphs visually appealling
  • Python Data Visualisation made Easy

Audience: 

This course is intended for:

  • Anyone who wants to learn data visualization in Python.

Prerequisites:

  • Basic Python programming experience.

Suggested prerequisites courses:

Outline

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SubjectsApp Development, Big Data
Lab AccessNo
TechnologyProgramming Language
Learning StyleSelf-Paced Learning
Learning TypeCourse
DifficultyIntermediate
Course Duration6 Hours
LanguageEnglish

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Course Expert:

Author

Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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