Data Visualization with Python
Course outline and certification by IBM
Certification offered by
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Key Points About This Course
Duration: 4 Days
Public Class Fee: RM 4,500.00
Virtual Class Fee: RM 3,825.00
“A picture is worth a thousand words”. We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.
One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.
The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.
Skills You Will Gain
- Python Programming
- Data Virtualization
- Data Visualization (DataViz)
This course is part of multiple programs
This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
- Applied Data Science Specialization
- IBM Data Science Professional Certificate
Part 1: Introduction to Data Visualization Tools
In this module, you will learn about data visualization and some of the best practices to keep in mind when creating plots and visuals. You will also learn about the history and the architecture of Matplotlib and learn about basic plotting with Matplotlib. In addition, you will learn about the dataset on immigration to Canada, which will be used extensively throughout the course. Finally, you will briefly learn how to read csv files into a pandas dataframe and process and manipulate the data in the dataframe, and how to generate line plots using Matplotlib.
Part 2: Basic and Specialized Visualization Tools
In this module, you learn about area plots and how to create them with Matplotlib, histograms and how to create them with Matplotlib, bar charts, and how to create them with Matplotlib, pie charts, and how to create them with Matplotlib, box plots and how to create them with Matplotlib, and scatter plots and bubble plots and how to create them with Matplotlib.
Part 3: Advanced Visualizations and Geospatial Data
In this module, you will learn about advanced visualization tools such as waffle charts and word clouds and how to create them. You will also learn about seaborn, which is another visualization library, and how to use it to generate attractive regression plots. In addition, you will learn about Folium, which is another visualization library, designed especially for visualizing geospatial data. Finally, you will learn how to use Folium to create maps of different regions of the world and how to superimpose markers on top of a map, and how to create choropleth maps.
Part 4: Sequence Mutation and Accumulation Patterns
We will present deeper knowledge on using lists, strings, and python objects in general. We will also cover how to use the accumulation pattern with lists and with strings. The final assignment will test your knowledge and skills through application, much like previous assessments and assignments did, though with a more difficult set of tasks now that you have learned the basics.
- Introduction: Transforming Sequences
- List Element Deletion
- Objects and References
- Methods on Lists
- Append vs. Concatenate
- Non-Mutating Methods on Strings
- String Format Method
- The Accumulator Pattern with Lists
- The Accumulator Pattern with Strings
- Accumulator Pattern Strategies
- Don’t Mutate A List That You Are Iterating Through
- Course Feedback