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Section 1: Getting started with Datavisualization and descriptive statistics course
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Download and Install Anaconda distribution for Python 08 minLecture1.1
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Course organization and Jupyter notebook 10 minLecture1.2
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Section 2: Exploratory data analysis using Python 3 graphical libraries.
In this section, students will learn how to use Python 3 graphical libraries such as matplotlib, seaborn and pandas to create professional looking charts of real world data.
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Creating a Pie chart using Python 3 matplotlib graphical library 10 minLecture2.1
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Side by Side Pie charts using matplotlib library in Python 3 10 minLecture2.2
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Creating a stacked area plot using Python seaborn library 10 minLecture2.3
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Creating a scatter plot chart in Python 3 using seaborn library. 10 minLecture2.4
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Creating a pairplot using Python seaborn graphical library 10 minLecture2.5
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Using a Boxplot in Pandas seaborn library to compare groups in data 10 minLecture2.6
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Creating a line plot trend of the data using Python pandas library 10 minLecture2.7
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Creating a histogram using Python seaborn to analyze data 10 minLecture2.8
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Creating a Barplot using colors palettes with Python seaborn library (Part 1) 10 minLecture2.9
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Creating a Barplot using colors palettes with Python seaborn library (Part 2) 10 minLecture2.10
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Creating a Stacked bar of the missing migrants data using Python seaborn library 10 minLecture2.11
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Creating a Pareto type barchart using Python seaborn library 10 minLecture2.12
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Creating a heatmap plot using Python seaborn library 10 minLecture2.13
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Section 3: Projects and hands on applications
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Hands on project about visualizations in Python 10 minLecture3.1
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Section4: Computing descriptive statistics in Python Pandas Part 1
In this section, we will learn how to use the Pandas library to compute descriptive statistics in Python
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Analyzing descriptive statistics using Pandas library in Python 3 10 minLecture4.1
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Analyzing Baseball players data with Pandas in Python 3 10 minLecture4.2
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Computing descriptive statistics in Python Pandas Part 2 10 minLecture4.3
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Computing correlation coefficients with Python Scipy library 10 minLecture4.4
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Computing the coefficient of variation in Python scipy statistics library 10 minLecture4.5
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Classifying World literacy rate using Pandas libraries in Python 10 minLecture4.6
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Finding outliers in data using Python Pandas library with quantiles functions 10 minLecture4.7
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Using Python Scipy library to compute various measures of center of the data 10 minLecture4.8
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Computing the Z score using Python Scipy library 10 minLecture4.9
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Computing percentiles of scores and IQR using Python Scipy library 10 minLecture4.10
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Computing trimmed statistics using Python 3 scipy statistics library 10 minLecture4.11
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Computing statistics with missing values using the statistics library in Python 10 minLecture4.12
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Handling missing values using the statistics library in Python 10 minLecture4.13
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Computing various medians using the Statistics library in Python 10 minLecture4.14
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Section 5: Computing Descriptive Statistics using the Numpy library in Python
Students will learn how to use the Numpy library to compute descriptive statistics in Python. In particular, they will learn how to handle missing values when using that library.
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Handling missing values in Numpy library in Python 10 minLecture5.1
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Computing Descriptive Statistics using the Numpy library in PythonLecture5.2
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Section 6: Hands on analysis of Descriptive statistics data in Python 3
Practical applications of the course Datavisualisation and Descriptive statistics
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Download and Install Anaconda distribution for Python
In this section, we will show you how to obtain Anaconda for Python 3 and how to launch the Jupyter notebooks.
2 Comments
very narrative explanation
The illustration is very interesting!