Data visualization and Descriptive Statistics with Python 3

Name: Data visualization and Descriptive Statistics with Python 3
Rating: 4.5 (39 reviews)

Using practical real-world datasets to showcase how to visualize and analyze data with Python Pandas, scipy and numpy

Created byLuc Zio

Last updated 2/2024

English

What you'll learn

Create effective data visualizations, including histograms, boxplots, scatterplots, bar charts, and pie charts using Python libraries
Learn how to explore datasets, identify patterns, and gain insights through both statistical measures and visual tools.
Understand how the different Python libraries treat missing values in calculating descriptive statistics
Be able to use effectively Python statistical libraries to compute descriptive statistics
Create professional charts with real world data using Python 3
Understand how and why some charting types are used to explore data in data science and Python

Course content

9 sections • 34 lectures • 5h 25m total length

Download and Install Anaconda distribution for Python7:35
In this section, we will show you how to obtain Anaconda for Python 3 and how to launch the Jupyter notebooks.
Course organization, Jupyter notebooks, data and project files using Python 313:09
In this lecture, you will learn how the course is organized. In particular, you will learn how to easily download the files necessary for each lecture as well as the projects files.

Creating a Pie chart using Python matplotlib graphical library12:26
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.
Showing the graphical display in Python
Side by Side Pie charts using matplotlib library in Python 314:15
In this video, we learn how to construct side by side pie charts of the Ebola data usin matplotlib library in Python 3
Understanding subploting in Python data visualization
Creating a stacked area plot using Python seaborn library12:26
In this lecture, we showcase the creation of the stacked area plot using Washington DC crime statistics data and how to interpret it.
Understanding stacked area and stacked bar plots in Python
Creating a scatterplot chart in Python 3 using seaborn library.8:48
In this lecture, we are using Seaborn graphical library to create a scatterplot of the systolic blood pressure data and also showcase to interpret the chart.
Understanding regplot for creating scatterplot in seaborn.
Creating a pairplot using Python seaborn graphical library5:09
In this lecture, we are looking at pairwise relationships between quantitative variables using Python PAIRPLOT library in seaborn.
Understanding the use of pairplot in Seaborn
Using a Boxplot in Pandas seaborn library to compare groups in data7:24
In this lecture we use Python seaborn boxplot library to analyze corruption perception index data in order to compare the score by continent.
Creating a line plot trend of the data using Python pandas library10:07
In this video, we use line plot library in pandas to show the trend of life expectancy in countries of the world
Creating a histogram using Python seaborn to analyze data6:43
In this lecture, we use Python seaborn library to create a histogram about the world corruption perception index data
Creating a Barplot using Python seaborn library to analyze the Ebola data7:44
In this lecture, we use a Barplot to analyze the EBOLA data in Sierra Leone and Guinea
Creating a Barplot using colors palettes with Python seaborn library5:04
In this lecture, we create a professionally looking barchart using colors palettes with Python seaborn graphical library.
Creating a Stacked bar of the missing migrants data using Python seaborn library10:26
In this lecture, we construct a stacked bar using a real world dataset about missing migrants.
Creating a Pareto type barchart using Python seaborn library8:19
In this lecture, we are constructing an ordered barchart of Duncan's PRESTIGE DATA. This ordered barchart is equivalent to a Paerto's chart .
Creating a heatmap plot using Python seaborn library10:03
In this lecture, you will learn how to construct a heatmap in Python seaborn using the Washington DC crime statistics data

Computing descriptive statistics in Python Pandas Part 118:29
In this lecture, we use Pandas in Python 3 to compute various descriptive statistics using Real World datasets. We are computing statistics such as the mean, standard deviation, mean absolute deviation within the Pandas library.
Analyzing Baseball players data with Pandas in Python 312:41
In this lecture, we show how to use Pandas in Python 3 to aggregate data by computing the number of observations, mean absolute deviation, mean, min, max, median, standard deviation and skewness coefficient.
Computing descriptive statistics in Python Pandas Part 219:20
In addition to computing Pearson, Kendall and Spearman correlation using Python Pandas library, in this lecture we compute skewness coefficient, percentiles and ranks using real world datasets.
Computing correlation coefficients with Python Scipy library5:37
In this lecture, you will learn how to compute Kendall tau coefficient of correlation, Pearson Correlation coefficient and Spearman Rank correlation using real world datasets.
Computing the coefficient of variation in Python scipy statistics library4:55
In this lecture, we compute the coefficient of variation in scipy stats and explain how its useful in our daily work.
Classifying World literacy rate using Pandas libraries in Python20:50
At the end of this video, students will be able to tackle Real World applications using the classification techniques taught in the course using Pandas in Python 3.
Finding outliers in data using Python Pandas library with quantiles functions11:30
In this lecture, we show how to use Pandas quantile functions to determine outliers in the data using real world infant mortality dataset.

Using Python Scipy library to compute various measures of center of the data10:25
In this lecture, we use libraries in Python scipy stats to compute various means such as geometric and harmonic means.
Computing the Z score using Python Scipy library7:48
In this lecture, you learn how to compute and interpret the Z score from the world corruption perception data.
Computing percentiles of scores and IQR using Python Scipy library7:39
In this lecture, we use Python Scipy PercentOfScore to compute percentiles of the data as well as ranking the data.
Computing trimmed statistics using Python 3 scipy statistics library10:55
In this lecture, we showcase how to compute trimmed means using Scipy stats library in Python

Computing statistics with missing values using the statistics library in Python6:59
In this lecture, you will learn about missing values and their effect statistical computations within the statistics library in Python.
Handling missing values using the statistics library in Python8:52
In this lecture, we showcase how to handle missing values to perform proper statistical computations using the statistics library in Python
Computing various medians using the Statistics library in Python9:43
In this lecture, we showcase how to compute the median, median low and median high using the statistics library in Python

Handling missing values in Numpy library in Python9:23
In this lecture, we show how to handle missing data in Pandas Numpy and specifically how to compute the descriptive statistics functions
Computing Descriptive Statistics using the Numpy library in Python11:39
In this lecture we illustrate how to use numpy to compute various statistical functions including weighted means
Understanding numpy computations when missing values are in the data

Requirements

Python Anaconda latest distribution using Jupyter notebook
Basic Python programming Knowledge
Understanding of Basic Statistics
Familiarity with Jupyter Notebooks Experience using Jupyter Notebooks or similar environments for coding is beneficial, as they are often used in data visualization workflows.

Description

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:

Correlation plots
Box-plots for comparing groups distributions
Time series and lines plots
Side by side comparative pie charts
Areas charts
Stacked bar charts
Histograms of continuous data
Bar charts
Regression plots
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."

Who this course is for:

A genuine curiosity about using Python for statistical data analysis and creating impactful visualizations.
Anyone interested in charting real world datasets with Python 3
Anyone interested in Exploratory Data Analysis (EDA) 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

Data visualization and Descriptive Statistics with Python 3

What you'll learn

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Course content

Getting started with the course2 lectures • 21min

Exploratory data analysis using Python 3 graphical libraries.13 lectures • 1hr 59min

Projects and hands on applications1 lecture • 6min

Analyzing descriptive statistics using Pandas libraries in Python 37 lectures • 1hr 33min

Computing descriptive statistics in Python Scipy library4 lectures • 37min

Computing Descriptive Statistics using the Statistics library in Python3 lectures • 26min

Computing Descriptive Statistics using the Numpy library in Python2 lectures • 21min

Hands on analysis of Descriptive statistics data in Python 31 lecture • 2min

Conclusion for the course1 lecture • 2min

Requirements

Description

Who this course is for: