Overview
This course is designed to teach analysts, students interested in data science, statisticians, data scientists on how to analyze realworld data by creating professionallooking charts and using numerical descriptive statistics techniques in Python 3. You will learn how to use charting libraries in Python 3 to analyze realworld 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 realworld 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 handson realworld 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
 Boxplots 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 tertiles, 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 welldelivered 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 handson data visualization in Python and the analysis of descriptive statistics. I will recommend this course anyone trying to come into this domain.”
Course Features
 Lectures 34
 Quizzes 0
 Duration 50 hours
 Skill level All levels
 Language English
 Students 5
 Assessments Yes
Curriculum

Section 1: Getting started with Datavisualization and descriptive statistics course

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.
 Creating a Pie chart using Python 3 matplotlib graphical library
 Side by Side Pie charts using matplotlib library in Python 3
 Creating a stacked area plot using Python seaborn library
 Creating a scatter plot chart in Python 3 using seaborn library.
 Creating a pairplot using Python seaborn graphical library
 Using a Boxplot in Pandas seaborn library to compare groups in data
 Creating a line plot trend of the data using Python pandas library
 Creating a histogram using Python seaborn to analyze data
 Creating a Barplot using colors palettes with Python seaborn library (Part 1)
 Creating a Barplot using colors palettes with Python seaborn library (Part 2)
 Creating a Stacked bar of the missing migrants data using Python seaborn library
 Creating a Pareto type barchart using Python seaborn library
 Creating a heatmap plot using Python seaborn library

Section 3: Projects and hands on applications

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
 Analyzing descriptive statistics using Pandas library in Python 3
 Analyzing Baseball players data with Pandas in Python 3
 Computing descriptive statistics in Python Pandas Part 2
 Computing correlation coefficients with Python Scipy library
 Computing the coefficient of variation in Python scipy statistics library
 Classifying World literacy rate using Pandas libraries in Python
 Finding outliers in data using Python Pandas library with quantiles functions
 Using Python Scipy library to compute various measures of center of the data
 Computing the Z score using Python Scipy library
 Computing percentiles of scores and IQR using Python Scipy library
 Computing trimmed statistics using Python 3 scipy statistics library
 Computing statistics with missing values using the statistics library in Python
 Handling missing values using the statistics library in Python
 Computing various medians using the Statistics library in Python

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.

Section 6: Hands on analysis of Descriptive statistics data in Python 3
Practical applications of the course Datavisualisation and Descriptive statistics
Instructor
Reviews

Mohamed Ayman
Great experience
A seriouse deal of statistical modelling taught with a perfect content. I really appricate the effort put in order to not being "hardtounderstand", but still finding the way to teach complex statistics. You will have a very good useful knowledge of statistical modelling without getting lost through too many greek symbols and long explanations. Recommended course to understand the how to do data analysis using python. Thank you so much!