PYTHON-DS.AE1
Python® for Data Science for Beginners
Use Python for Data Science – because basic is for spreadsheets!
- Practice in 30 Laboratorios prácticos — nothing to install
- 23 Lecciones interactivas y 116 topics mapped to the official exam objectives
- 240 Preguntas del examen de práctica
Beginner A tu propio ritmo · 1 año de acceso
30 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
23Lecciones interactivas
116Topics
30Laboratorio en vivo
240Preguntas del examen de práctica
76Tarjetas didácticas
76Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
This Python for data science training course is your map, starting from the ABCs of coding and guiding you through the thrilling experience of data analysis. You’ll encounter Google Colab and Jupyter Notebook along the way.
In addition, harness the strength of Numpy and Pandas for data conditioning and visualization by enrolling in this Python for Data Science course. Along the way, you’ll decode complex numbers with machine learning (ML), unearth hidden patterns, and become a professional data scientist.
- Gain proficiency in Python coding
- Learn to install and use essential Python tools
- Learn data handling and processing from various data sources
- Clean and condition data to maintain accuracy and reliability
- Visualize data with graphs and plots, turning raw numbers into compelling stories
- Apply machine learning to identify patterns and trends in data
- Become proficient in using Google Colab and Jupyter Notebooks to streamline your workflow
- Perform exploratory data analysis (EDA) to picture data better
- Optimize models for better performance and maximum impact
Course Highlights
-
23 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
-
30 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
-
240 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
-
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
23 Lecciones interactivas · 116 topics01 Introduction 4 topics +
- About This Course
- False Assumptions
- Icons Used in This Course
- Where to Go from Here
02 Discovering the Match between Data Science and Python 4 topics +
- Defining the Sexiest Job of the 21st Century
- Creating the Data Science Pipeline
- Understanding Python’s Role in Data Science
- Learning to Use Python Fast
03 Introducing Python’s Capabilities and Wonders 6 topics +
- Why Python?
- Working with Python
- Performing Rapid Prototyping and Experimentation
- Considering Speed of Execution
- Visualizing Power
- Using the Python Ecosystem for Data Science
04 Setting Up Python for Data Science 5 topics +
- Considering the Off-the-Shelf Cross-Platform Scientific Distributions
- Installing Anaconda on Windows
- Installing Anaconda on Linux
- Installing Anaconda on Mac OS X
- Downloading the Datasets and Example Code
05 Working with Google Colab 9 topics +
- Defining Google Colab
- Getting a Google Account
- Working with Notebooks
- Performing Common Tasks
- Using Hardware Acceleration
- Executing the Code
- Viewing Your Notebook
- Sharing Your Notebook
- Getting Help
06 Understanding the Tools 3 topics +
- Using the Jupyter Console
- Using Jupyter Notebook
- Performing Multimedia and Graphic Integration
07 Working with Real Data 6 topics +
- Uploading, Streaming, and Sampling Data
- Accessing Data in Structured Flat-File Form
- Sending Data in Unstructured File Form
- Managing Data from Relational Databases
- Interacting with Data from NoSQL Databases
- Accessing Data from the Web
08 Conditioning Your Data 8 topics · 20 Laboratorio en vivo +
- Juggling between NumPy and pandas
- Validating Your Data
- Manipulating Categorical Variables
- Dealing with Dates in Your Data
- Dealing with Missing Data
- Slicing and Dicing: Filtering and Selecting Data
- Concatenating and Transforming
- Aggregating Data at Any Level
20 Laboratorio en vivo in this lesson — see the labs panel →
09 Shaping Data 4 topics · 1 Laboratorio en vivo +
- Working with HTML Pages
- Working with Raw Text
- Using the Bag of Words Model and Beyond
- Working with Graph Data
1 Laboratorio en vivo in this lesson — see the labs panel →
10 Putting What You Know in Action 3 topics · 2 Laboratorio en vivo +
- Contextualizing Problems and Data
- Considering the Art of Feature Creation
- Performing Operations on Arrays
2 Laboratorio en vivo in this lesson — see the labs panel →
11 Getting a Crash Course in MatPlotLib 4 topics +
- Starting with a Graph
- Setting the Axis, Ticks, Grids
- Defining the Line Appearance
- Using Labels, Annotations, and Legends
12 Visualizing the Data 5 topics +
- Choosing the Right Graph
- Creating Advanced Scatterplots
- Plotting Time Series
- Plotting Geographical Data
- Visualizing Graphs
13 Stretching Python’s Capabilities 4 topics · 1 Laboratorio en vivo +
- Playing with Scikit-learn
- Performing the Hashing Trick
- Considering Timing and Performance
- Running in Parallel on Multiple Cores
1 Laboratorio en vivo in this lesson — see the labs panel →
14 Exploring Data Analysis 6 topics · 1 Laboratorio en vivo +
- The EDA Approach
- Defining Descriptive Statistics for Numeric Data
- Counting for Categorical Data
- Creating Applied Visualization for EDA
- Understanding Correlation
- Modifying Data Distributions
1 Laboratorio en vivo in this lesson — see the labs panel →
15 Reducing Dimensionality 3 topics · 1 Laboratorio en vivo +
- Understanding SVD
- Performing Factor Analysis and PCA
- Understanding Some Applications
1 Laboratorio en vivo in this lesson — see the labs panel →
16 Clustering 3 topics · 1 Laboratorio en vivo +
- Clustering with K-means
- Performing Hierarchical Clustering
- Discovering New Groups with DBScan
1 Laboratorio en vivo in this lesson — see the labs panel →
17 Detecting Outliers in Data 3 topics +
- Considering Outlier Detection
- Examining a Simple Univariate Method
- Developing a Multivariate Approach
18 Exploring Four Simple and Effective Algorithms 4 topics · 1 Laboratorio en vivo +
- Guessing the Number: Linear Regression
- Moving to Logistic Regression
- Making Things as Simple as Naïve Bayes
- Learning Lazily with Nearest Neighbors
1 Laboratorio en vivo in this lesson — see the labs panel →
19 Performing Cross-Validation, Selection, and Optimization 4 topics · 1 Laboratorio en vivo +
- Pondering the Problem of Fitting a Model
- Cross-Validating
- Selecting Variables Like a Pro
- Pumping Up Your Hyperparameters
1 Laboratorio en vivo in this lesson — see the labs panel →
20 Increasing Complexity with Linear and Nonlinear Tricks 5 topics +
- Using Nonlinear Transformations
- Regularizing Linear Models
- Fighting with Big Data Chunk by Chunk
- Understanding Support Vector Machines
- Playing with Neural Networks
21 Understanding the Power of the Many 3 topics · 1 Laboratorio en vivo +
- Starting with a Plain Decision Tree
- Making Machine Learning Accessible
- Boosting Predictions
1 Laboratorio en vivo in this lesson — see the labs panel →
22 Ten Essential Data Resources 10 topics +
- Discovering the News with Subreddit
- Getting a Good Start with KDnuggets
- Locating Free Learning Resources with Quora
- Gaining Insights with Oracle’s Data Science Blog
- Accessing the Huge List of Resources on Data Science Central
- Learning New Tricks from the Aspirational Data Scientist
- Obtaining the Most Authoritative Sources at Udacity
- Receiving Help with Advanced Topics at Conductrics
- Obtaining the Facts of Open Source Data Science from Masters
- Zeroing In on Developer Resources with Jonathan Bower
23 Ten Data Challenges You Should Take 10 topics +
- Meeting the Data Science London + Scikit-learn Challenge
- Predicting Survival on the Titanic
- Finding a Kaggle Competition that Suits Your Needs
- Honing Your Overfit Strategies
- Trudging Through the MovieLens Dataset
- Getting Rid of Spam E-mails
- Working with Handwritten Information
- Working with Pictures
- Analyzing Amazon.com Reviews
- Interacting with a Huge Graph
Laboratorios prácticos Our edge
30 Laboratorio en vivos- Checking the Version of Pandas
- Creating Categorical Variables
- Finding the Missing Data
- Encoding Missingness
- Sorting and Shuffling
- Creating n-grams
- Calculating TF-IDF
- Modifying Graphs Using NetworkX
- Creating an Adjacency Matrix Using NetworkX
- Defining a Plot
- Creating a Line Plot
- Creating a Legend
- Creating a Pie Chart
- Creating a Scatterplot
- Creating an Undirected Graph
- Using Parallel Coordinates
- Calculating Descriptive Statistics
- Visualizing the Validation Curve
- Visualizing a Subset of Images
- Adding New Cases and Variables
- Extracting a Telephone Number
- Using Vectorization
- Performing Matrix Multiplication
- Building a Predictor
- Loading the Iris Dataset
- Creating a Numpy Array
- Understanding Centroid-Based Algorithms
- Using K-Nearest Neighbors and PCA
- Loading the Boston Housing Dataset
- Optimizing the Depth of Decision Tree
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What Python skills are needed for data science? +
You need to know how to write basic Python code, work with libraries like Pandas and NumPy, and understand data structures and basic algorithms.
What are the prerequisites for taking this course? +
No prerequisites needed! This Python for data science for beginners course starts from scratch, so no prior programming experience is needed. Just bring your curiosity and enthusiasm.
What are the prerequisites for this course? +
All you need is a fast WiFi connection, a modern browser, and a willingness to learn. We’ll guide you through everything else.
Will there be other learners in this course with who I can interact? +
No, this Python for data science course is designed for individual study, but there are tons of online forums and communities where you can share your progress and get help if needed.
What Python library is required for data science? +
The big ones you’ll use are Pandas, NumPy, and Matplotlib. These libraries will help you handle data, perform analysis, and visualize your findings.
Keep going
You might also like
From Python Newbies to Data Professionals
Learn to analyze, visualize, and leverage data to make game-changing decisions with our Python for Data Science course.
- 1 año de acceso completo
- 30 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito