Data visualization and Descriptive Statistics with Python 3
- 5.5 hours on-demand video
- 36 downloadable resources
- 1 Practice Test
- Full lifetime access
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- Certificate of Completion
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- Create professional charts with real world data using Python 3
- Understand Python 3 visual analysis tools and how to use them
- Understand how and why some charting types are used to explore data in data science and Python
- Understand how the different Python libraries treat missing values in data
- Be able to use effectively Python statistical libraries to compute descriptive statistics
In this section, we will show you how to obtain Anaconda for Python 3 and how to launch the Jupyter notebooks.
In this lecture, we use Python matplotlib graphical library to create a pie chart of the Ebola data. We illustrate how the chart is created and most importantly how to interpret the chart.
Subplot grid parameters can be written as single integer like 131 or (1,3,1). For example, "131" means "1x3 grid, first subplot" and "234" means "2x3 grid, 4th subplot". So, (1,2,1) will mean a plot with 1 row, 2 columns (charts), for the 1st subplot. As an example, the python code will look like: plt.subplot(1, 3, 2)
We can use regplot in a seaborn to create a scatter plot or (XY) plot. sns.regplot(x="Age", y="SBP", fit_reg=True, data=df)
by default, fit_reg is set to True in which case a regression line is drawn through the data. If we don't want a regression line,
we set use: fit_reg = False
In this lecture we use Python seaborn boxplot library to analyze corruption perception index data in order to compare the score by continent.
- Python Anaconda 3.6 using Jupyter notebook
- Introductory level of Python
- Introductory statistics
This course is designed to teach analysts, students interested in data science, statisticians, data scientists how to analyze real world data by creating professional looking charts and using numerical descriptive statistics techniques in Python 3. You will learn how to use charting libraries in Python 3 to analyze real-world data about corruption perception, infant mortality rate, life expectancy, the Ebola virus, alcohol and liver disease data, World literacy rate, violent crime in the USA, soccer World Cup,
migrants deaths, etc.
You will also learn how to effectively use the various statistical libraries in Python 3 such as numpy, scipy.stats, pandas and statistics to create all descriptive statistics summaries that are necessary for analyzing real world data.
In this course, you will understand how each library handles missing values and you will learn how to compute the various statistics properly when missing values are present in the data.
The course will teach you all that you need to know in order to analyze hands on real world data using Python 3. You will be able to appropriately create the visualizations using seaborn, matplotlib or pandas libraries in Python 3.
Using a wide variety of world datasets, we will analyze each one of the data using these tools within pandas, matplotlib and seaborn:
Box-plots for comparing groups distributions
Time series and lines plots
Side by side comparative pie charts
Stacked bar charts
Histograms of continuous data
Statistical measures of the center of the data
Statistical measures of spread in the data
Statistical measures of relative standing in the data
Calculating Correlation coefficients
Ranking and relative standing in data
Determining outliers in datasets
Binning data in terciles, quartiles, quintiles, deciles, etc.
The course is taught using Anaconda Jupyter notebook, in order to achieve a reproducible research goal, where we use markdowns to clearly
document the codes in order to make them easily understandable and shareable.
This is what some students are saying:
"I really like the tips that you share in every unit in the course sections. This was a well delivered course."
"I am a Data Scientist with many years using Python /Big Data. The content of this course provides a rich resource to students interested in learning hands on data visualization in Python and the analysis of descriptive statistics. I will recommend this course anyone trying to come into this domain."
- Anyone interested in charting real world datasets with Python 3
- Anyone interested in Exploratory Data Analysis using Python 3
- Anyone interested in understanding how Pandas, Numpy, statistics and Scipy libraries treat missing values in Python and how they affect data sciences computations
- Anyone interested in understanding how to compute descriptive statistics using Python libraries
- Anyone interested in understanding how to effectively use the different statistical libraries for computing descriptive statistics