Python Programming Level 3: Data Analysis Using Python Course Outline
Overview
The widespread use of the World Wide Web and social media has resulted in the creation and access to enormous amount of data becoming available. The data needs to be analyzed to be able to apply the information in useful ways in many fields including business, science, and social science. The course will teach you to apply your Python programming skills to complex data analysis problems. You will learn to use Pandas for data analysis and Seaborn for data visualization, with JupyterLab as your IDE. Additionally, you’ll learn how to get, clean, prepare, and analyze data, including time-series data. Moreover, you’ll learn to use linear regression models to predict unknown and future values.
Audience
Previous experience with Python programming.
Prerequisites
Basic Python programming experience. You should be comfortable working with strings, lists, tuples, dictionaries loops and conditionals and writing your own functions. See
ONLC's Python curriculum for all available titles.
COURSE OUTLINE
Introduction to Python for data analysis
What data analysis is
The Python skills that you need for data analysis
How to use JupyterLab as your IDE
How to split the screen between two Notebooks
How to use Magic Commands
The Pandas essentials for data analysis
Introduction to the Pandas DataFrame
How to examine the data
How to access the columns and rows
How to work with the data
How to shape the data
How to analyze the data
The Pandas essentials for data visualization
Introduction to data visualization
How to create 8 types of plots
How to enhance a plot
The Seaborn essentials for data visualization
Introduction to Seaborn
How to enhance and save plots
How to create relational plots
How to create categorical plots
How to create distribution plots
Other techniques for enhancing a plot
How to get the data
How to find the data that you want to analyze
How to import data into a DataFrame
How to get database data into a DataFrame
How to work with a Stata file
How to work with a JSON file
How to clean the data
Introduction to data cleaning
How to simplify the data
How to find and fix missing values
How to fix data type problems
How find and fix outliers
How to prepare the data
How to add and modify columns
How to apply functions and lambda expressions
How to work with indexes
How to combine DataFrames
How to handle the SettingWithCopyWarning
How to analyze the data
How to create and plot long data
How to group and aggregate the data
How to create and use pivot tables
How to work with bins
More skills for data analysis
How to analyze time-series data
How to reindex time-series data
How to resample time-series data
How to work with rolling windows
How to work with running totals
How to make predictions with a linear regression model
Introduction to predictive analysis
How to find correlations between variables
How to use Scikit-learn to work with a linear regression
How to plot regression models with Seaborn
How to make predictions with a multiple regression model
A simple regression model for a Cars dataset
How to work with a multiple regression model
How to work with categorical variables
How to improve a multiple regression model
View outline in Word
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