Este curso forma parte de Programa especializado: Análisis de datos contables

4.3
estrellas
75 calificaciones

Ronald Guymon +1 instructores más

10.345 ya inscrito

Ofrecido Por

Parte del título de grado Master of Science in Accountancy (iMSA)
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Programa especializado: Análisis de datos contablesUniversidad de Illinois en Urbana-Champaign

Acerca de este Curso

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This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data).
The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. 
The second half of the course focuses on assembling data for machine learning purposes.  We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.

Fechas límite flexibles

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Curso 2 de 4 en

Programa especializado: Análisis de datos contables

Nivel intermedio

Aprox. 41 horas para completar

Inglés (English)

Qué aprenderás

Know how to operate software that will help you create and run Python code.
Execute Python code for wrangling data from different structures into a Pandas dataframe structure.
Run and interpret fundamental data analytic tasks in Python including descriptive statistics, data visualizations, and regression.
Use relational databases and know how to manipulate such databases directly through the command line, and indirectly through a Python script.

Habilidades que obtendrás

Python Programming
Data Visualization (DataViz)
Linear Regression
SQL
Data Preparation

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 4 en

Programa especializado: Análisis de datos contables

Nivel intermedio

Aprox. 41 horas para completar

Inglés (English)

Instructores

Calificación del instructor

4,15/5 (24 calificaciones)

Ronald Guymon

Senior Lecturer of Accountancy

Accountancy

52.406 alumnos

5 cursos

Linden Lu

Instructor

Department of Accountancy

15.594 alumnos

3 cursos

Ofrecido por

Universidad de Illinois en Urbana-Champaign

Comienza a trabajar para obtener tu maestría

Este curso es parte de Master of Science in Accountancy (iMSA) completamente en línea de Universidad de Illinois en Urbana-Champaign. Si te aceptan en el programa completo, tus cursos cuentan para tu título de grado.

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Programa - Qué aprenderás en este curso

Semana1

Semana 1

1 hora para completar

INTRODUCTION TO THE COURSE

1 hora para completar

4 videos (Total 12 minutos), 5 lecturas

4 horas para completar

MODULE 1: FOUNDATIONS

This module serves as the introduction to the course content and the course Jupyter server, where you will run your analytics scripts. First, you will read about specific examples of how analytics is being employed by Accounting firms. Next, you will learn about the capabilities of the course Jupyter server, and how to create, edit, and run notebooks on the course server. After this, you will learn how to write Markdown formatted documents, which is an easy way to quickly write formatted text, including descriptive text inside a course notebook.

4 horas para completar

14 videos (Total 84 minutos), 2 lecturas, 2 cuestionarios

14 videos

Module 1 Introduction2m1.1 Introduction to Data Analytics2m1.2 Jupyter Notebook4mPython and Integrated Development Environments (IDEs)6mInstalling and Running Python Using the Anaconda Distribution4mInstalling and Running Python Without Anaconda9mNavigating Jupyter Notebook5mNavigating JupyterLab5mUsing Notebook Files9mNavigating Spyder8mComparison of Jupyter Notebook, JupyterLab, and Spyder3m1.3 Introduction to Markdown3mMarkdown Basics9mModule 1 Review6m

2 lecturas

Module 1 Overview and Resources3mLesson 1.1 Readings20m

1 ejercicio de práctica

Module 1 Quiz20m

Semana2

Semana 2

4 horas para completar

MODULE 2: INTRODUCTION TO PYTHON

This module focuses on the basic features in the Python programming language that underlie most data analytics programs (or scripts). First, you will read about why accounting students should learn to write computer programs. In the first lesson, you will also learn the basic concepts of the Python programming language, including how to create variables, basic data types and mathematical operators, and how to document your programs with comments. Next, you will learn about Boolean and logical operators in Python and how they can be used to control the flow of a Python program by using conditional statements. Finally, you will learn about functions and how they can simplify developing and maintaining programs. You will also learn how to create and call functions in Python.

4 horas para completar

12 videos (Total 66 minutos), 1 lectura, 2 cuestionarios

12 videos

Module 2 Introduction7m2.1 Introduction to Python3mPython Code Basics6mVariables, Data Types, and Operators8m2.2 Introduction to Python Functions2mBuilt-In Functions6mUser-Defined Functions9mFunctions vs Methods4m2.3 Conditional Statements in Python2mComparison and Logical Operators3mWorking with Conditional Statements5mModule 2 Review4m

1 lectura

Module 2 Overview and Resources15m

1 ejercicio de práctica

Module 2 Quiz15m

Semana3

Semana 3

5 horas para completar

MODULE 3: INTRODUCTION TO PYTHON PROGRAMMING

5 horas para completar

15 videos (Total 84 minutos), 1 lectura, 2 cuestionarios

15 videos

Module 3 Introduction2m3.1 Introduction to Python Data Structures4mIntroduction to Strings6mIntroduction to Lists5mIntroduction to Dictionaries, Tuples, and Unpacking7mCommon Sequence Operations7m3.2 Working with Python Data Structure2mWorking with Strings9mWorking with Lists and Tuples7mWorking with Dictionaries3m3.3 Introduction to Python Loops2mThe For Loop13mThe While Loop2mComprehensions3mModule 3 Review4m

1 lectura

Module 3 Overview and Resources3m

1 ejercicio de práctica

Module 3 Quiz10m

Semana4

Semana 4

5 horas para completar

MODULE 4: PYTHON PROGRAMMING

5 horas para completar

12 videos (Total 74 minutos), 1 lectura, 2 cuestionarios

Semana5

Semana 5

5 horas para completar

MODULE 5: DATA ANALYSIS WITH PYTHON

5 horas para completar

14 videos (Total 93 minutos), 1 lectura, 2 cuestionarios

14 videos

Module 5 Introduction3m5.1 Python File IO3mReading and Writing Files with Base Python9mReading and Writing Files with Pandas8mPreserving Data Types with Pickling5m5.2 Working with the Pandas DataFrame1mExploring Dataframes8mCopying and Sorting Dataframes7mChanging Column and Row Names of Dataframes6mGrouping and Aggregating with Dataframes6mStacking and Pivoting Dataframes10m5.3 Introduction to Descriptive Statistics1mDescriptive Statistics for Dataframes13mModule 5 Review7m

1 lectura

Module 5 Overview and Resources3m

1 ejercicio de práctica

Module 5 Quiz30m

Semana6

Semana 6

6 horas para completar

MODULE 6: INTRODUCTION TO VISUALIZATION IN PYTHON

6 horas para completar

16 videos (Total 88 minutos), 1 lectura, 2 cuestionarios

16 videos

Module 6 Introduction3m6.1 Introduction to Plotting with Python2mIntroduction to Plotting with Pandas9mMore on Plotting with Pandas5mIntroduction to matplotlib9mMore on Plotting with matplotlib5mIntroduction to Plotting with Seaborn5m6.2 Introduction to One-Dimensional Data Visualization2mIntroduction to Seaborn Histograms5mIntroduction to Seaborn Box Plots5mIntroduction to Seaborn Bar Plots4m6.3 Introduction to Two-Dimensional Data3mIntroduction to Scatter Plots7mIntroduction to Pair Plots5mIntroduction to Joint Plots5mModule 6 Review7m

1 lectura

Module 6 Overview and Resources3m

1 ejercicio de práctica

Module 6 Quiz15m

Semana7

Semana 7

5 horas para completar

MODULE 7: PRODUCTION DATA ANALYTICS

5 horas para completar

16 videos (Total 84 minutos), 1 lectura, 2 cuestionarios

16 videos

Module 7 Introduction3m7.1 Introduction to CRISP-DM3m7.2 Introduction to Data Preparation Techniques2mPandas Functions to Load Data7mFill in Missing Values with Conditional Means8mManipulating String Columns of a Dataframe7mCreating Datetime Values4mSplit, Apply Combine and More on Datetimes6mLambda Functions6m7.3 Linear Regression in Python2mSetting up Data for Regression5mCreating a Simple Regression Model7mPredicting with a Regression Model5mMultiple Regression Model6mCategorical Variables in Regression3mModule 7 Review2m

1 lectura

Module 7 Overview and Resources3m

1 ejercicio de práctica

Module 7 Quiz30m

Semana8

Semana 8

6 horas para completar

MODULE 8: INTRODUCTION TO DATABASES IN PYTHON

6 horas para completar

15 videos (Total 86 minutos), 3 lecturas, 2 cuestionarios

15 videos

Module 8 Introduction3m8.1 Introduction to Data Persistence4mIntroduction to Terminal8mCreating a SQLite Database From Terminal8mCreating a SQLite Table From a CSV File5mUsing Dump and Reading in Files to Create Tables8mAltering Existing SQLite Tables4m8.2 Advanced Concepts2mQuerying Tables with SQL6mSQL Join Queries5m8.3 Python Database Programming3mQuerying Relational Database with Python and SQL7mExploring Databases and Adding Rows to Tables with Python8mModule 8 Review4mGies Online Programs3m

3 lecturas

Module 8 Overview and Resources3mCongratulations!10mGet Your Course Certificate10m

1 ejercicio de práctica

Module 8 Quiz30m

Reseñas

4.3

24 reseña

5 stars
57,33 %
4 stars
25,33 %
3 stars
6,66 %
2 stars
8 %
1 star
2,66 %

Principales reseñas sobre ACCOUNTING DATA ANALYTICS WITH PYTHON

por SA27 de mar. de 2022

very useful and important to computer science engineering students

por EG30 de nov. de 2020

It is very easy to learn and also very interesting because you can modify and try other things.

por SM27 de jun. de 2020

Really Nice course but you will not explain all the module very indepth. All the module are basic and easy to learn. I am very happy to completed this course.

por MF2 de mar. de 2023

Great introductory Python course with alot of knowledge that can be applied to your every day job.

Ver todas las reseñas

Acerca de Programa especializado: Análisis de datos contables

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Accounting Data Analytics with Python

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás