Fundamentals of Data Visualization for Data Scientists
Join 4,000 other students to learn one of the essential components of data science: data visualization. Get started with the methods and techniques here!
In this course, we cover introductory methods and techniques for getting started with data visualization and creating plots in Python using Matplotlib. Armed with a starting point for plotting data, we can master data representation and visualization like pro data scientists.
Data visualization is all about taking complex ideas, numbers, data and turning it into something easy to look at and understand.
This course is all about learning how to start visualizing all your data directly in your code.
If you're looking to:
- analyze data
- visualize data
- incorporate data visualization into your work
- expand your overall Python programming language
This course is perfect for you.
Thorough data analysis is becoming increasingly demanded in modern day businesses and data science.
To start off with data analysis (and ultimately provide nice images of our results), we need to be able to plot our data, preferably in the exact way we imagine it in our heads.
So how do we do that?
Matplotlib provides many great plotting opportunities and methods for data visualization, and in this course, we look at some introductory methods for getting started with creating plots in Python.
Once we have a starting point for plotting data, we can easily expand our knowledge to different areas to make sure we can best represent all of our data.
At the end of the course, you'll have mastered how to:
- Make line plots, scatter plots, 1-D and 2-D Histogram plots in Python
- Customize your plots via color, line styles, axes, tick labels, titles, custom text, sizing, legends, and more
- Save figures in desired formats
- Change the scale of the axis to better graph logarithmic data
Hey, Max! What beautiful visualizations are we going to learn to make?
Well, I'm glad you asked! Here are some examples of what we'll learn to make.
So if you're looking to make your raw data/numbers turn into perfect visualizations for understanding your own data & data analysis, giving nice presentations at work, university research presentations, comparing your data to look for patterns and seeing future trends - data visualization is right for you.
If in addition to all of the above, you have some very basic Python knowledge and a Python environment to code in - this course is right for you.
Why is data visualization so amazing?
- Humans are visual people - we process images faster and better than text and numbers; data visualization allows us to understand information and see relationships and trends immediately
- It is perfect for big data. Huge dataset with millions of pieces of data? NO problem - visualize the data. You can take tons of data and put it all into one simple little graph to reveal all the key pieces of info
- Tells a story - visualization shows us a story and a development of facts that can help us identify the past
- Predict the future - when you see a pattern or a trend in the visual representations, you can make educated predictions for how it will continue forward in the future - super useful for finance, investment, banking, even medicine and law
"Graphics reveal data. Indeed graphics can be more precise and revealing than conventional statistical computations." - Edward Tufte
"Your course was helpful for me because I could replace an old chart library that I have used to matplotlib, making my job too easy and fast. It's a great course. Congratulations!"
- Jose Rocha, Operating System Specialist
So... have a lot of data and no way to visually represent it?
Let's get started with creating some great visuals for your data.
PreviewIntroduction to Matplotlib (2:51)
PreviewImporting Libraries in Python (8:40)
StartDealing with Files in Python (14:18)
PreviewMaking Line and Scatter Plots (14:07)
StartAdding Labels, Titles, Axis Ticks, and Changing Line Styles (9:53)
StartRotating Axis Ticks, Adding Text and Annotations (7:57)
StartAdjusting Plot Sizes, Adding a Legend, and Saving the Plots (8:33)
StartCreating 1-Dimensional and 2-Dimensional Histograms (13:43)
StartChanging the Axis Scales (13:27)
Frequently Asked Questions
In June 2016, I graduated with a degree in Physics, but instead of delving into a life of research and the lab, I decided to explore other possible options for my life and career.
I found myself more and more drawn towards programming, data mining, data processing, and data analytics. So I decided to teach myself Python, C, C++, Machine Learning, Data Science, Web-Scrapping, APIs, and Data Mining.
Now, I work as a Data Scientist for an e-sports company (total dream job).
So, I thought I would turn the tables and teach you all the knowledge I've been able to gather to make you the ultimate programmer or data scientist.
I have over 9,000 students on various platforms learning programming and data science - join them and get started with your project!