Online Course – Google Certified Professional Internship in Accounting Data Analytics, University of Illinois Urbana-Champaign

Learn data analytics skills for accountants. These specializations unlock skills in data preparation, data visualization, data analysis, data interpretation, and machine learning algorithms and their applications to real-world problems.

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קורס אונליין - התמחות מקצועית מוסמכת באנליטיקת נתוני חשבונאות של Google

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Analytical thinking
  • Data preparation
  • Data visualization
  • Analysis using Excel
  • Skills in using Python for data preparation
  • Data visualization
  • Data Analytics
  • Data interpretation
  • Classification
  • Regression
  • Groups
  • Text analysis
  • Time series analysis
  • Model optimization
  • Introducing the CRISP-DM Framework Process
  • Demonstration of data analysis skills
  • Applying knowledge and skills in data analysis to practical problems

What you will learn in the course

Courses for which the course is suitable

  • Data Analyst
  • Data Scientist
  • Machine learning model developer
  • Information Systems Analyst
  • Data Project Manager
  • Data Consultant
  • Python programmer
  • Text analysis expert
  • Time series analyzer
  • Model Optimization Expert

Internship – a course series of 3 courses

This specialization develops students’ analytical thinking and knowledge of data analysis tools and techniques. It focuses on developing analytical skills by:

  • Displaying analytical thinking
  • Data preparation
  • Data visualization
  • Analysis using Excel

Further, this specialization enhances skills in using Python for data preparation, data visualization, data analysis, and data interpretation, while applying these skills in situations relevant to accounting.

Additionally, this specialization focuses on developing skills in machine learning algorithms (using Python), including:

  • Classification
  • Regression
  • Groups
  • Text analysis
  • Time series analysis
  • Model optimization

Hands-on Learning Project

The projects included in this specialization allow learners to apply the skills acquired to real-world problems. Learners will be able to:

  • Present the overall process of the CRISP-DM framework
  • Demonstrate data analysis skills in data preparation, data visualization, modeling, and model evaluation
  • Apply knowledge and skills in data analysis to practical problems

For example, in the final project, learners will develop a machine learning model to predict whether a loan will be paid in full and build a loan portfolio using the analysis.

Details of the courses that make up the specialization

Introduction to analysis and visualization of accounting data

Course 1
20 hours
4.8 (429 ratings)

What you’ll learn

Accounting has always been associated with analytical thinking. This course is designed to help accounting students develop analytical thinking and prepare them to use data analysis programming languages ​​such as Python and R.

  • Link between accounting and analytics.
  • Data collection and its importance.
  • Using Excel and Tableau to analyze big data.
  • Demonstrating the power of programming languages ​​for data analysis.

Skills you will acquire

  • Predictive analysis
  • Data Analytics
  • programming
  • Data visualization
  • Data architecture

Analyzing accounting data with Python

Course 2
42 hours
4.3 (91 ratings)

What you’ll learn

  • Software operation for creating and running Python code.
  • Adapting data from different structures to a Pandas dataframe.
  • Basic analytical tasks in Python.
  • Operating relational databases via the command line and Python script.

Skills you will acquire

  • Data preparation
  • Python programming
  • Linear regression
  • SQL
  • Data visualization

Machine Learning for Accounting with Python

Course 3
64 hours
4.6 (41 ratings)

What you’ll learn

  • Various machine learning algorithms.
  • Applying machine learning models to datasets with Python in Jupyter Notebook.
  • Evaluation and optimization of machine learning models.

Skills you will acquire

  • Python programming
  • Evaluating and optimizing machine learning models
  • Basic time series analysis
  • Machine learning models
  • Text analysis

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