Course: Accounting Data Analytics with Python
Modules:
Course Introduction, About Ronald Guymon, About Linden Lu
Module 1 Introduction, 1.1 Introduction to Data Analytics, 1.2 Jupyter Notebook, Python and Integrated Development Environments (IDEs), Installing and Running Python Using the Anaconda Distribution, Installing and Running Python Without Anaconda, Navigating Jupyter Notebook, Navigating JupyterLab, Using Notebook Files, Navigating Spyder, Comparison of Jupyter Notebook, JupyterLab, and Spyder, Refreshing Folders, 1.3 Introduction to Markdown, Markdown Basics, Module 1 Review
Module 2 Introduction, 2.1 Introduction to Python, Python Code Basics, Variables, Data Types, and Operators, 2.2 Introduction to Python Functions, Built-In Functions, User-Defined Functions, Functions vs Methods, Refreshing Folders, 2.3 Conditional Statements in Python, Comparison and Logical Operators, Working With Conditional Statements, Module 2 Review
Module 3 Introduction, 3.1 Introduction to Python Data Structures, Introduction to Strings, Introduction to Lists, Introduction to Dictionaries, Tuples, and Unpacking, Common Sequence Operations, Refreshing Folders, 3.2 Working With Python Data Structure, Working With Strings, Working With Lists and Tuples, Working With Dictionaries, 3.3 Introduction to Python Loops, The For Loop, The While Loop, Comprehensions, Module 3 Review
Module 4 Introduction, 4.1 Writing Python Programs, Python Modules, Errors and Exceptions, 4.2 Introduction to NumPy, NumPy Array, NumPy Basic Functions, Refreshing Folders, 4.3 Introduction to Pandas, Introduction to Dataframes, Data Selection With Dataframes, Missing Values and Copies With Dataframes, Module 4 Review
Module 5 Introduction, 5.1 Python File IO, Reading and Writing Files With Base Python, Reading and Writing Files With Pandas, Preserving Data Types With Pickling, Refreshing Folders, 5.2 Working With the Pandas DataFrame, Exploring Dataframes, Copying and Sorting Dataframes, Changing Column and Row Names of Dataframes, Grouping and Aggregating With Dataframes, Stacking and Pivoting Dataframes, 5.3 Introduction to Descriptive Statistics, Descriptive Statistics for Dataframes, Module 5 Review
Module 6 Introduction, 6.1 Introduction to Plotting With Python, Introduction to Plotting With Pandas, More on Plotting With Pandas, Introduction to matplotlib, More on Plotting With matplotlib, Introduction to Plotting With Seaborn, Refreshing Folders, 6.2 Introduction to One-Dimensional Data Visualization, Introduction to Seaborn Histograms, Introduction to Seaborn Box Plots, Introduction to Seaborn Bar Plots, 6.3 Introduction to Two-Dimensional Data, Introduction to Scatter Plots, Introduction to Pair Plots, Introduction to Joint Plots, Module 6 Review
Module 7 Introduction, 7.1 Introduction to CRISP-DM, 7.2 Introduction to Data Preparation Techniques, Pandas Functions to Load Data, Fill in Missing Values With Conditional Means, Manipulating String Columns of a Dataframe, Creating Datetime Values, Split, Apply Combine and More on Datetimes, Lambda Functions, Refreshing Folders, 7.3 Linear Regression in Python, Setting up Data for Regression, Creating a Simple Regression Model, Predicting with a Regression Model, Multiple Regression Model, Categorical Variables in Regression, Module 7 Review
Module 8 Introduction, 8.1 Introduction to Data Persistence, Introduction to Terminal, Creating a SQLite Database From Terminal, Creating a SQLite Table From a CSV File, Using Dump and Reading in Files to Create Tables, Altering Existing SQLite Tables, Refreshing Folders, 8.2 Advanced Concepts, Querying Tables with SQL, SQL Join Queries, 8.3 Python Database Programming, Querying Relational Database With Python and SQL, Exploring Databases and Adding Rows to Tables With Python, Module 8 Review, Learn on Your Terms