DATA-WRGLG-PYTHON.AJ1
Data Wrangling with Python
Achieve proficiency in the data analysis process in no time!
- Practice in 45 Laboratorios prácticos — nothing to install
- 10 Lecciones interactivas y 51 topics mapped to the official exam objectives
- 98 Preguntas del examen de práctica
Intermediate A tu propio ritmo · 1 año de acceso 4.7/5 (212 Revisar)
45 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
01 / Habilidades que obtendrás
What you will be able to do
Course Highlights
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10 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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45 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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98 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
10 Lecciones interactivas · 51 topics01 Introduction 8 topics +
- About the Course
- Learning Objectives
- Approach
- Audience
- Minimum Hardware Requirements
- Software Requirements
- Conventions
- Installation and Setup
02 Introduction to Data Wrangling with Python 4 topics · 5 Laboratorio en vivo +
- Introduction
- Python for Data Wrangling
- Lists, Sets, Strings, Tuples, and Dictionaries
- Summary
5 Laboratorio en vivo in this lesson — see the labs panel →
03 Advanced Data Structures and File Handling 4 topics · 5 Laboratorio en vivo +
- Introduction
- Advanced Data Structures
- Basic File Operations in Python
- Summary
5 Laboratorio en vivo in this lesson — see the labs panel →
04 Introduction to NumPy, Pandas, and Matplotlib 5 topics · 6 Laboratorio en vivo +
- Introduction
- NumPy Arrays
- Pandas DataFrames
- Statistics and Visualization with NumPy and Pandas
- Summary
6 Laboratorio en vivo in this lesson — see the labs panel →
05 A Deep Dive into Data Wrangling with Python 6 topics · 7 Laboratorio en vivo +
- Introduction
- Subsetting, Filtering, and Grouping
- Detecting Outliers and Handling Missing Values
- Concatenating, Merging, and Joining
- Useful Methods of Pandas
- Summary
7 Laboratorio en vivo in this lesson — see the labs panel →
06 Getting Comfortable with Different Kinds of Data Sources 4 topics · 4 Laboratorio en vivo +
- Introduction
- Reading Data from Different Text-Based (and Non-Text-Based) Sources
- Introduction to Beautiful Soup 4 and Web Page Parsing
- Summary
4 Laboratorio en vivo in this lesson — see the labs panel →
07 Learning the Hidden Secrets of Data Wrangling 5 topics · 5 Laboratorio en vivo +
- Introduction
- Advanced List Comprehension and the zip Function
- Data Formatting
- Identify and Clean Outliers
- Summary
5 Laboratorio en vivo in this lesson — see the labs panel →
08 Advanced Web Scraping and Data Gathering 6 topics · 6 Laboratorio en vivo +
- Introduction
- The Basics of Web Scraping and the Beautiful Soup Library
- Reading Data from XML
- Reading Data from an API
- Fundamentals of Regular Expressions (RegEx)
- Summary
6 Laboratorio en vivo in this lesson — see the labs panel →
09 RDBMS and SQL 5 topics · 7 Laboratorio en vivo +
- Introduction
- Refresher of RDBMS and SQL
- Using an RDBMS (MySQL/PostgreSQL/SQLite)
- Reading Data from a Database in SQLite
- Summary
7 Laboratorio en vivo in this lesson — see the labs panel →
10 Application of Data Wrangling in Real Life 4 topics · 1 Laboratorio en vivo +
- Introduction
- Applying Your Knowledge to a Real-life Data Wrangling Task
- An Extension to Data Wrangling
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
45 Laboratorio en vivos- Sorting a List
- Generating a List
- Deleting a Value from a Dictionary
- Accessing and Setting Values in a Dictionary
- Slicing a String
- Implementing a Queue
- Splitting a String
- Implementing Multi-Element Membership Checking
- Implementing a Stack
- Opening a File and Printing its Content
- Generating Arrays Using arange and linspace
- Multiplying Two Arrays
- Adding Two NumPy Arrays
- Creating a NumPy Array
- Filtering Elements from a Matrix
- Stacking Arrays
- Subsetting a DataFrame
- Grouping a DataFrame
- Dropping the Missing Values
- Replacing Missing Values in a DataFrame
- Joining DataFrames
- Concatenating Data Frames
- Counting Values
- Bypassing the Headers of a CSV File
- Reading Data from a CSV File
- Stacking URLs from a Document Using bs4
- Counting Tags
- Using the zip Function
- Using a One-Liner Generator Expression
- Using a Generator Expression
- Using the format Function
- Using a Box Plot
- Checking the Status of the Web Request
- Extracting Text from a Section
- Traversing an XML Tree
- Checking Whether the Input String Begins with a Specific Word
- Matching Pattern
- Finding the Number of Words in a List That End with ing
- Deleting the Data
- Using Joins
- Using the Foreign Key
- Updating Data
- Using the ORDER BY Clause
- Using the SELECT Statement
- Using the SELECT Statement
- Skipping the First Row of the Data Set
03 / Preguntas frecuentes
Preguntas antes de empezar
How do I clean data using Python? +
Do I need prior programming experience to take a data wrangling course? +
What are the best Python libraries for data wrangling? +
The top Python libraries for data wrangling include:
Pandas: For data manipulation and analysis
NumPy: For numerical operations
Matplotlib and Seaborn: For data visualization
PyJanitor: For extended data cleaning functions
What is the difference between data cleaning and data wrangling? +
Data Cleaning is the process of identifying and correcting errors in the data.
Data Wrangling is a broader process that includes data cleaning, transforming, and mapping raw data into a more useful format for analysis.
What are some common data wrangling techniques? +
Common data wrangling techniques in Python include:
Data Merging: Combining multiple data sources into one dataset.
Data Transformation: Changing the format or structure of the data.
Data Subsetting: Selecting specific rows or columns of interest.
Handling Outliers: Identifying and correcting outliers in the data.
Data Aggregation: Summarizing data by grouping and calculating statistics.
What is the role of NumPy and Pandas in data wrangling? +
NumPy provides support for numerical operations on large, multi-dimensional arrays and matrices, which are essential for efficient data manipulation.
Pandas offers data structures and functions designed to make data manipulation and analysis easy, such as DataFrames for handling tabular data.
Which roles can I pursue after completing a data wrangling course?+
Career opportunities after completing our Python for data wrangling course include roles such as:
- Data Analyst
- Data Scientist
- Data Engineer
- Business Analyst
- ML Engineer
Cleaning & Transforming Data Simplified
Learn quick Python data wrangling techniques to turn raw data into clear, actionable insights.
- 1 año de acceso completo
- 45 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito