Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 6 Minute(s)
Language
English
Taught by
Stone River eLearning
Rating
Course Overview
Python,Data Visualization,Matplotlib
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.
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
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.
Tools Used
Python 3: Python is a general purpose programming language which a focus on readability and concise code, making it a great language for new coders to learn. Learning Python gives a solid foundation for learning more advanced coding languages, and allows for a wide variety of applications.
Matplotlib: Matplotlib is a plotting library that works with the Python programming language and its numerical mathematics extension 'NumPy'. It allows the user to embed plots into applications using various general purpose toolkits (essentially, it's what turns the data into the graph).
IDLE: IDLE is an Integrated Development Environment for Python; i.e where you turn the data into the graph. Although you can use any other IDE to do so, we recommend the use of IDLE for this particular course.
Course Content
- 8 section(s)
- 59 lecture(s)
- Section 1 Course Introduction
- Section 2 Different types of basic Matplotlib charts
- Section 3 Basic Customization Options
- Section 4 Advanced Customization Options
- Section 5 Geographical Plotting with Basemap
- Section 6 3D graphing
- Section 7 Course Conclusion
- Section 8 Bonus Material
What You’ll Learn
- Visualize multiple forms of both 2D and 3D graphs, like line graphs, scatter plots, bar charts, and more
- Load data from files or from internet sources for data visualization.
- Create live graphs
- Customize graphs, modifying colors, lines, fonts, and more
- Visualize Geographical data on maps
Skills covered in this course
Reviews
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DDavid Paradice
This course just needs to be updated to get 5 stars. Looks like it was created in 2015. The Yahoo API is no longer available, so some of the classes can't be done as shown. matplotlib.finance has been replaced by MPLfinance, so candlestick graphs are very different now. Basecamp has been deprecated; Cartopy is now encouraged but you can still get Basecamp and run the examples. The 1st wireframe graph throws an error (but a straight "plot" generates what is in the lesson). There's still a lot of useful material here, but this six-hour course becomes a lot longer (and potentially frustrating) if you include the time spent resolving these issues so that you can follow along / practice. Minor suggestion: change the name of the resources from "Matplotlib sourcecode" to something like "Course Examples" or "Code used in course". My first thought was this was the source code for Matplotlib. I was curious, so I opened the file. I can imagine others would not look at it. Another suggestion: this course really depends on using the IDLE to get the full value from it. I typically use Anaconda, and I lost some of the functionality using Anaconda compared to what is demonstrated in the classes. I'm comfortable with the IDLE, so no big deal for me. But, many novices only know Python through something like Anaconda. It would be a nice touch to have the reliance on the IDLE made clearer in the description of the course. All things considered, just updating this course would make it a 5 star course.
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DDouglas Clementson
Great overview of matplotlib with a brief look into geo and 3d plotting. Worthwhile. The instructor moves too quickly for someone unfamiliar with Python and there are many instances when you will have to rewind to understand the many instances he offers the "and here we'll do this" type of explanation, so be certain you're familiar with the basics. I enjoyed going deep into plotting stocks but I wish we could have gone deep into plotting an additional type of data as well.
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AAli Hejazizo
Pretty old, not using modern IDEs and visualization tools. not covering new libraries such as seaborn.
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TTarquin Tate
I really like the course. There is lots of good content, and it has real-world applications. However, there is a lot of out of date content. E.g. using the Yahoo API, or importing the candlestick_ohlc, Basemap installation. I am glad I did the course and would do it again, but it is not the most current course.
