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\nData Visualization in Python\n

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\n\nPublisher:\n\nAPMonitor\n\n\n
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\nData visualization and exploration is one of the first steps in machine learning after the data is gathered and statistically summarized. It is used to graphically represent data to qualitatively understand relationships and data...\nData visualization and exploration is one of the first steps in machine learning after the data is gathered and statistically summarized. It is used to graphically represent data to qualitatively understand relationships and data quality. Visualization is the graphical representation of data while exploration is gaining an understanding of the data to make better informed decisions about how it can be applied. Visualization and exploration gives an understanding of data diversity, relationships, missing data, bad data, or other factors that may influence decisions to exclude or include an appropriate subset for training. Explore data visualization with Python with matplotlib, pandas, seaborn, and plotly.

0:00 Zettabyte Data Era
0:44 Visualize Data
1:22 Careers in Data Science
2:27 Introduction to Data Visualization
3:08 Machine Learning Course
3:48 Install Packages
5:20 Load Data and Summary Statistics
7:04 Plot Data with Pandas
10:07 Pandas Profiling
13:25 Matplotlib
14:26 Plotly Express
15:31 Seaborn
18:04 PVWatts Case Study
30:45 Summary\n
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Resource Details

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Educator Rating
Not yet Rated
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Media Length
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32:00
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Grade
10th - Higher Ed
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Subjects
Social Studies & History
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Media Type
Instructional Videos
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Source:
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\nAPMonitor.com\n\n
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Date
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2021
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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standard deviation, box plot, scatter plots, data science, careers, plot
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Additional Tags

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data engineer, data scientist, analyst, visualize, create, scatter, python, make sense, machine learning, top ten, bit bigger, auto scrolling, pandas profiling, ambient temperature, html file, meter squared, concrete strength, highly correlated, plotly express, array irradiance, profile report, pip install, couple lines, heat map, data analyst, data visualization, data frame, nice package, pair plot, missing values, cell temperature, large amount
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Classroom Considerations

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Best For: Explaining a topic
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