PYTHON-DS.AP1
Foundational Python for Data Science
Learn the ropes of Python programming to transform raw data into meaningful information – effortlessly.
- Practice in 33 Laboratorios prácticos — nothing to install
- 16 Lecciones interactivas y 94 topics mapped to the official exam objectives
- 276 Preguntas del examen de práctica y 2 Pruebas completas
Beginner A tu propio ritmo · 1 año de acceso 5.0/5 (70 Revisar)
33 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
16Lecciones interactivas
94Topics
33Laboratorio en vivo
276Preguntas del examen de práctica
136Tarjetas didácticas
136Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
This foundational Python for data science course will start with the basics and smoothly transition into more advanced concepts, making sure you’re well-equipped to handle data. Also, you can practice using Pandas and NumPy in a risk-free online lab environment to learn data manipulation and analysis. Then, visualize your findings with Matplotlib and Seaborn. By the end, you’ll convert raw data into actionable information – all while having a bit of gamified experience along the way.
- Get comfortable with Python programming essentials
- Utilize Pandas and NumPy to clean, organize, and manipulate your data
- Create clear and compelling charts with Matplotlib and Seaborn
- Identify patterns and develop insights from raw data
- Learn how to use data frames for efficient data handling
- Apply your skills to resolve everyday data science problems
- Save time by automating repetitive data tasks with Python
- Build a strong foundation for more advanced data science courses
Course Highlights
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16 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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33 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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276 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
16 Lecciones interactivas · 94 topics01 Introduction 1 topics +
- About This eBook
02 Introduction to Notebooks 5 topics +
- Running Python Statements
- Jupyter Notebooks
- Google Colab
- Summary
- Questions
03 Fundamentals of Python 5 topics · 2 Laboratorio en vivo +
- Basic Types in Python
- Performing Basic Math Operations
- Using Classes and Objects with Dot Notation
- Summary
- Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
04 Sequences 6 topics · 3 Laboratorio en vivo +
- Shared Operations
- Lists and Tuples
- Strings
- Ranges
- Summary
- Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
05 Other Data Structures 5 topics · 3 Laboratorio en vivo +
- Dictionaries
- Sets
- Frozensets
- Summary
- Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
06 Execution Control 7 topics · 4 Laboratorio en vivo +
- Compound Statements
- if Statements
- while Loops
- for Loops
- break and continue Statements
- Summary
- Questions
4 Laboratorio en vivo in this lesson — see the labs panel →
07 Functions 6 topics · 2 Laboratorio en vivo +
- Defining Functions
- Scope in Functions
- Decorators
- Anonymous Functions
- Summary
- Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
08 NumPy 11 topics · 3 Laboratorio en vivo +
- Installing and Importing NumPy
- Creating Arrays
- Indexing and Slicing
- Element-by-Element Operations
- Filtering Values
- Views Versus Copies
- Some Array Methods
- Broadcasting
- NumPy Math
- Summary
- Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
09 SciPy 6 topics · 2 Laboratorio en vivo +
- SciPy Overview
- The scipy.misc Submodule
- The scipy.special Submodule
- The scipy.stats Submodule
- Summary
- Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
10 Pandas 8 topics · 3 Laboratorio en vivo +
- About DataFrames
- Creating DataFrames
- Interacting with DataFrame Data
- Manipulating DataFrames
- Manipulating Data
- Interactive Display
- Summary
- Questions
3 Laboratorio en vivo in this lesson — see the labs panel →
11 Visualization Libraries 7 topics · 4 Laboratorio en vivo +
- matplotlib
- Seaborn
- Plotly
- Bokeh
- Other Visualization Libraries
- Summary
- Questions
4 Laboratorio en vivo in this lesson — see the labs panel →
12 Machine Learning Libraries 5 topics · 1 Laboratorio en vivo +
- Popular Machine Learning Libraries
- How Machine Learning Works
- Learning More About Scikit-learn
- Summary
- Questions
1 Laboratorio en vivo in this lesson — see the labs panel →
13 Natural Language Toolkit 6 topics · 1 Laboratorio en vivo +
- NLTK Sample Texts
- Frequency Distributions
- Text Objects
- Classifying Text
- Summary
- Questions
1 Laboratorio en vivo in this lesson — see the labs panel →
14 Functional Programming 5 topics · 2 Laboratorio en vivo +
- Introduction to Functional Programming
- List Comprehensions
- Generators
- Summary
- Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
15 Object-Oriented Programming 5 topics · 1 Laboratorio en vivo +
- Grouping State and Function
- Special Methods
- Inheritance
- Summary
- Questions
1 Laboratorio en vivo in this lesson — see the labs panel →
16 Other Topics 6 topics · 2 Laboratorio en vivo +
- Sorting
- Reading and Writing Files
- datetime Objects
- Regular Expressions
- Summary
- Questions
2 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
33 Laboratorio en vivos- Computing Leaves of an Employee
- Calculating Expenses Using Multiple Statements
- Performing Shared Operations
- Adding and Removing Items
- Performing Data Analysis
- Accessing, Adding, and Updating Data by Using Keys
- Performing Set Operations
- Using Frozensets
- Determining if a Person is Eligible to Vote
- Determining Average and Grades Using Scores of Subjects
- Computing the Factorial of a Number
- Displaying the Number of Transactions
- Accessing Library Data
- Using the lambda Function
- Visualizing Data Using the reshape Method
- Computing Mathematical Data
- Performing Matrix Operations on NumPy Data
- Executing Image Processing
- Performing Customer Analysis
- Storing Employee Details
- Manipulating Employee Details
- Updating Student Data
- Visualizing Survey Data
- Creating a Styling Plot
- Analyzing Statistical Data
- Visualizing Tips According to the Total Bill
- Modifying Data Using Transformation
- Finding the Frequency of Words
- Modifying Outer Scope
- Changing Mutable Data
- Using Inheritance
- Sorting Data
- Demonstrating Regular Expressions
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
Why is Python important for data science? +
Python is simple, easy to learn, and packed with powerful tools that make working with data efficient. Whether you’re organizing data, analyzing trends, or visualizing patterns, Python’s got your back.
Do I need any prior experience with Python to take this course? +
No prior experience needed! This Python for Data Science course starts with the basics, so you’ll get up to speed quickly.
What Python libraries will I learn in this course? +
You’ll learn to use some of the coolest Python libraries like Pandas for data manipulation, NumPy for numerical operations, Matplotlib for plotting, and Seaborn for creating stunning visualizations.
What types of projects will I work on during the course?+
You’ll work on fun, hands-on projects that take you from cleaning and analyzing data to visualizing trends.
Develop In-demand Python Skills
Learn Python for Data Science to close the skill gap and gain a competitive advantage in your field.
- 1 año de acceso completo
- 33 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito