Accounting Data Analytics with Python
Become an Accounting Analytics expert with the Accounting Data Analytics with Python online course by Coursera. Master Python and business data preparation.
Intermediate
Online
9 Weeks
3,369 INR Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Accounting Data Analytics with Python Programme by Coursera has been developed for the candidates who are looking to build proficiency in the Python language, especially for business intelligence and analytics. Provided in partnership with the University of Illinois, the programme aims to utilize Python’s robust computational capabilities and libraries. It focuses heavily on working with multiple domains of accounting data, be it EDGAR financial statement data, stock data, or point-of-sale data.
The Coursera Accounting Data Analytics with Python Training covers fundamental Python programming skills like functions, conditional statements, loops, and mathematical operators. Moreover, since the course is a part of UIUC’s Master of Business Administration Degree, a greater emphasis is placed on data visualization and presentation. Furthermore, certification training also covers data structuring and manipulation, along with Python and SQL integration.
Lastly, the Accounting Data Analytics with Python Training Course is a 100 percent online learning experience that allows you to learn and train at your convenience. Along with the video lectures, you will have a collection of reading material carefully curated by Coursera at your disposal. Upon course completion, you will receive an industry revered Coursera Certificate.
The Highlights
- Shareable Certificate
- 100% online
- Flexible deadlines
- Intermediate Level
- Free enrolment
- Approx. 42 hours to complete
Programme Offerings
- Self Paced Online Learning
- community forum
- Course completion certificate
- lifetime access to course material
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
Accounting Data Analytics with Python Fee Structure is :
Course | Fees |
1 Month | Rs. 1,676 |
3 Months | Rs. 3,369 |
6 Months | Rs. 5,029 |
Eligibility Criteria
You need to pass all the assigned tasks in the curriculum or secure a cumulative graded course passing threshold to earn a Coursera certificate for the Accounting Data Analytics with Python Programme. In addition, you need to buy a subscription. The deadline to earn the certification is 180 days, and failing to do so means you will have to buy the course for certification again.
What you will learn
On completion of the Accounting Data Analytics with Python Training Course, you will have:
- Comprehensive knowledge of the Python fundamentals, data types, loops, and its various use cases in the field of accounting
- Proficiency in writing Python code and visualizing one- and two-dimensional data using libraries such as Matplotlib and Seaborn
- Mastery in working with Pandas Dataframe to read and save Dataframes, filter functions, apply pivot table functions, and calculate descriptive statistics
- Knowledge of working with SQL data in Python
- Expertise in manipulating and structuring data using Python Libraries like NumPy and Pandas
- Expertise in creating and utilizing a group of functions, which are also known as modules
- Working knowledge of creating and interacting with relational databases (RDBMS) in Python
Who it is for
Admission Details
To enroll in the Accounting Data Analytics with Python course follow these steps:
- Visit the Course page.
- When the page opens, log in through your credentials, or Google or Facebook account
- On the top left, you’ll find the “Enroll for free” button. Click on it.
- Make a choice between Purchase Course and Audit only options
- Auditing the course will take you to course lessons. If you choose the “Purchase course” option, it will take you to a payment gateway. Make the payment and open the course.
Application Details
To join the Accounting Data Analytics with Python Programme by Coursera, sign up on Coursera with your Facebook/Google account. Access the free to audit option to view the course material. Enroll in the course for free and then upgrade to view graded assignments and to earn a course certificate.
The Syllabus
Videos
- Course Introduction
- About Ronald Guymon
- About Linden Lu
Readings
- Syllabus
- Glossary
- About the Discussion Forums
- Online Education at Gies College of Business
- Update Your Profile
Discussion Prompt
- Get to Know Your Fellow Learners
Plugin
- Welcome! Please Tell Us About Yourself
Videos
- Module 1 Introduction
- Introduction to Data Analytics
- 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
- Introduction to Markdown
- Markdown Basics
- Module 1 Review
Readings
- Module 1 Overview
- Module 1 Readings
- Lesson 1.1 Readings
Quiz
- Module 1 Quiz
Programming Assignment
- Module 1 Programming Assignment Score
Discussion Prompt
- Make Connections to Topic
Ungraded Labs
- Introduction to Jupyter Notebook
- Introduction to Markdown
- Module 1 Programming Assignment
Videos
- 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
- 2.3 Conditional Statements in Python
- Comparison and Logical Operators
- Working with Conditional Statements
- Module 2 Review
Reading
- Module 2 Overview
- Module 2 Readings
Quiz
- Module 2 Quiz
Programming Assignment
- Module 2 Programming Assignment Score
Ungraded Labs
- Introduction to Python
- Introduction to Python Functions
- Python Conditional Statements
- Module 2 Programming Assignment
Videos
- 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
- 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 Loop1
- The While Loop
- Comprehensions
- Module 3 Review
Readings
- Module 3 Overview
- Module 3 Readings
Quiz
- Module 3 Quiz
Programming Assignment
- Module 3 Programming Assignment Score
Ungraded Labs
- Introduction to Python Data Structures
- Working With Python Data Structures
- Introduction to Python Loops
- Module 3 Programming Assignment
Videos
- Module 4 Introduction
- 4.1 Writing Python Programs
- Python Modules
- Errors and Exceptions
- 4.2 Introduction to NumPy
- NumPy Array
- NumPy Basic Functions
- 4.3 Introduction to Pandas
- Introduction to Dataframes
- Data Selection with Dataframes
- Missing Values and Copies with Dataframes
- Module 4 Review
Readings
- Module 4 Overview
- Module 4 Readings
Quiz
- Module 4 Quiz
Programming Assignment
- Module 4 Programming Assignment Score
Ungraded Labs
- Writing Python Programs
- Introduction to NumPy
- Introduction to Pandas
- Module 4 Programming Assignment
Videos
- 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
- 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
Readings
- Module 5 Overview
- Module 5 Readings
Quiz
- Module 5 Quiz
Programming Assignment
- Module 5 Programming Assignment Score
Ungraded Labs
- Python File Input/Output
- Working With the Pandas DataFrame
- Introduction to Descriptive Statistics
- Module 5 Programming Assignment
Videos
- 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
- 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
Reading
- Module 6 Overview
- Module 6 Readings
Quiz
- Module 6 Quiz
Programming Assignment
- Module 6 Programming Assignment Score
Ungraded Labs
- Introduction to Plotting With Python
- Introduction to One-Dimensional Data Visualizations
- Introduction to Two-Dimensional Data Visualizations
- Module 6 Programming Assignment
Videos
- 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
- 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
Reading
- Module 7 Overview
- Module 7 Readings
Quiz
- Module 7 Quiz
Programming Assignment
- Module 7 Programming Assignment Score
Ungraded Labs
- Introduction to CRISP-DM
- Introduction to Data Preparation Techniques
- Introduction to Linear Regression
- Module 7 Programming Assignment
Videos
- 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
- 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
Readings
- Module 8 Overview
- Module 8 Readings
- Congratulations on completing the course!
- Get Your Course Certificate
Quiz
- Module 8 Quiz
Programming Assignment
- Module 8 Programming Assignment Score
Ungraded Labs
- Introduction to Data Persistence
- SQL: Advanced Concepts
- Python Database Programming
- Module 8 Programming Assignment
Plugin
- End of Course Survey
Instructors
University of Illinois, Urbana Champaign Frequently Asked Questions (FAQ's)
Analytics can be applied in Accountings to reveal valuable insights, identify avenues to improve and increase a company’s efficiency, and most importantly, manage risks.
It entirely depends upon your type of enrollment. Candidates who have opted for auditing the course, might not be able to access some of the course material. They ended up purchasing the certificate to get complete access.
If you purchase the certificate, you will get access to all course elements of the Accounting Data Analytics with Python training. Upon successful course completion, you will be awarded a certificate, which will adorn your Coursera accomplishment page. Since the certificate is printable and shareable, you can add it to your LinkedIn profile, CVs, and resume with ease.
Students who can’t afford to pay the course fee get the option of financial aid. They have to apply for the aid by clicking on an option that’s next to the ‘Enroll’ button, fill up the application, and wait for the approval.