"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.
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Informações sobre o curso
O que você vai aprender
Describe the importance of data visualization.
Relate the history of Matplotlib and its architecture.
Apply Matplotlib to create plots using Jupyter notebooks.
Discover how to read CSV files into a Pandas DataFrame; process and manipulate the data in the DataFrame; and generate line plots using Matplotlib.
Habilidades que você terá
- Python Programming
- Data Virtualization
- Plotly
- Matplotlib
- Data Visualization (DataViz)
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IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Programa - O que você aprenderá com este curso
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.
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.
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.
Creating Dashboards with Plotly and Dash
In this module you will get started with dashboard creation using the Plotly library. You will create a dashboard with a theme `US Domestic Airline Flights Performance`. You will do this using a US airline reporting carrier on-time performance dataset, Plotly, and Dash concepts learned throughout the course. Hands-on labs will follow each concept to make you comfortable with using the library. Reading lists will reference additional resources to learn more about the concepts covered.
Avaliações
- 5 stars68,94%
- 4 stars19,81%
- 3 stars6,06%
- 2 stars2,76%
- 1 star2,41%
Principais avaliações do DATA VISUALIZATION WITH PYTHON
It's a really great course with proper hands on time and the assignments are great too. i got enough opportunity to explore the things which were taught in the course. Really Satisfied. Thanks :)
The labs were good but the issue was the extremely rushed up videos. A lot of concepts, especially the artist layer was not covered will in the videos, which made me give this course 4 stars.
Good coverage of different plots. Videos are somewhat repetitive regarding the dataset (most of them could be about 20% shorter due to this). Labs (in Jupyter Notebooks) are great practice.
The course was beautifully structured. I would like to request to add the conditions on which tiles Mapbox Bright works. At times the tiles dont work and we are not sure of the root cause.
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