Python Programming Level 3: Data Analysis Using Python (Live Online)

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.AudiencePrevious experience with Python programming.PrerequisitesBasic Python programming experience. You should be comfortable working with strings, lists, tuples, dictionaries loops and conditionals and writing your own functions.Course OutlineIntroduction to Python for data analysisWhat data analysis isThe Python skills that you need for data analysisHow to use JupyterLab as your IDEHow to split the screen between two NotebooksHow to use Magic CommandsThe Pandas essentials for data analysisIntroduction to the Pandas DataFrameHow to examine the dataHow to access the columns and rowsHow to work with the dataHow to shape the dataHow to analyze the dataThe Pandas essentials for data visualizationIntroduction to data visualizationHow to create 8 types of plotsHow to enhance a plotThe Seaborn essentials for data visualizationIntroduction to SeabornHow to enhance and save plotsHow to create relational plotsHow to create categorical plotsHow to create distribution plotsOther techniques for enhancing a plotHow to get the dataHow to find the data that you want to analyzeHow to import data into a DataFrameHow to get database data into a DataFrameHow to work with a Stata fileHow to work with a JSON fileHow to clean the dataIntroduction to data cleaningHow to simplify the dataHow to find and fix missing valuesHow to fix data type problemsHow find and fix outliersHow to prepare the dataHow to add and modify columnsHow to apply functions and lambda expressionsHow to work with indexesHow to combine DataFramesHow to handle the SettingWithCopyWarningHow to analyze the dataHow to create and plot long dataHow to group and aggregate the dataHow to create and use pivot tablesHow to work with binsMore skills for data analysisHow to analyze time-series dataHow to reindex time-series dataHow to resample time-series dataHow to work with rolling windowsHow to work with running totalsHow to make predictions with a linear regression modelIntroduction to predictive analysisHow to find correlations between variablesHow to use Scikit-learn to work with a linear regressionHow to plot regression models with SeabornHow to make predictions with a multiple regression modelA simple regression model for a Cars datasetHow to work with a multiple regression modelHow to work with categorical variablesHow to improve a multiple regression model