DATA-SCI.AA1
Data Science Labs
Start your data science journey with Python. Gain hands-on experience with NumPy, Pandas, Matplotlib, Seaborn, and more.
- Practice in 25 Laboratorios prácticos — nothing to install
- 7 Lecciones interactivas y 56 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
25 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
7Lecciones interactivas
56Topics
25Laboratorio en vivo
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
Data Science Labs is a comprehensive online training course that focuses on Python for data analysis, manipulation and visualization. Discover the fundamentals of Python programming, explore data structures and master data manipulation techniques. Gain practical skills in data analysis using Pandas, perform numerical calculations, create clear and insightful visualizations, and develop predictive models using regression. Additionally, this data science training course has been fully equipped with hands-on Labs where you can practice and solidify your learnings.
- Create, manipulate, and analyze data frames with Pandas
- Ability to use NumPy for arrays, indexing, slicing, and mathematical operations
- Create informative visuals with Matplotlib, Seaborn, Plotly, and Bokeh
- Build and evaluate regression models
- Knowledge of logistic regression for classification tasks
- Exploratory Data Analysis (EDA) for hypothesis testing, data visualization, and identifying patterns
- Extract, transform, and load data using ETL techniques
- Conduct in-depth exploratory data analysis
Course Highlights
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7 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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25 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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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
7 Lecciones interactivas · 56 topics01 Pandas 7 topics · 3 Laboratorio en vivo +
- About DataFrames
- Creating DataFrames
- Interacting with DataFrame Data
- Manipulating DataFrames
- Manipulating Data
- Interactive Display
- Summary
3 Laboratorio en vivo in this lesson — see the labs panel →
02 NumPy 10 topics · 2 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
2 Laboratorio en vivo in this lesson — see the labs panel →
03 Visualization Libraries 6 topics · 9 Laboratorio en vivo +
- matplotlib
- Seaborn
- Plotly
- Bokeh
- Other Visualization Libraries
- Summary
9 Laboratorio en vivo in this lesson — see the labs panel →
04 Extracting, Transforming, and Loading Data 4 topics · 2 Laboratorio en vivo +
- Topic A: Extract Data
- Topic B: Transform Data
- Topic C: Load Data
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Developing Regression Models 3 topics · 1 Laboratorio en vivo +
- Topic A: Train and Tune Regression Models
- Topic B: Evaluate Regression Models
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
06 Logistic Regression 14 topics · 1 Laboratorio en vivo +
- Simple Example of Logistic Regression
- Maximum Likelihood Estimation
- Interpreting Logistic Regression Output
- Inference: Are the Predictors Significant?
- Odds Ratio and Relative Risk
- Interpreting Logistic Regression for a Dichotomous Predictor
- Interpreting Logistic Regression for a Polychotomous Predictor
- Interpreting Logistic Regression for a Continuous Predictor
- Assumption of Linearity
- Zero-Cell Problem
- Multiple Logistic Regression
- Introducing Higher Order Terms to Handle Nonlinearity
- Validating the Logistic Regression Model
- WEKA: Hands-On Analysis Using Logistic Regression
1 Laboratorio en vivo in this lesson — see the labs panel →
07 Exploratory Data Analysis 12 topics · 7 Laboratorio en vivo +
- Hypothesis Testing Versus Exploratory Data Analysis
- Getting to Know The Data Set
- Exploring Categorical Variables
- Exploring Numeric Variables
- Exploring Multivariate Relationships
- Selecting Interesting Subsets of the Data for Further Investigation
- Using EDA to Uncover Anomalous Fields
- Binning Based on Predictive Value
- Deriving New Variables: Flag Variables
- Deriving New Variables: Numerical Variables
- Using EDA to Investigate Correlated Predictor Variables
- Summary of Our EDA
7 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
25 Laboratorio en vivos- Creating a Series from a List Using pandas
- Creating a Series from a Dictionary Using pandas
- Using the read_csv() Function
- Creating a One-Dimensional Array Using numpy
- Creating a Multi-Dimensional Array Using numpy
- Creating a Bar Plot Using matplotlib
- Creating a Line Plot Using matplotlib
- Creating a Scatter Plot Using matplotlib
- Creating a Pie Chart Using matplotlib
- Creating a Confusion Matrix
- Creating a Line Plot Using seaborn
- Adding Animation to a Choropleth Map Using Plotly Express
- Creating Different Shapes Using bokeh
- Creating a Linked Scatter Plot Using altair
- Performing Data Cleaning
- Handling the Missing Values
- Performing Linear Regression on the Salary Dataset
- Performing Logistic Regression
- Analyzing Students' Performance
- Performing Data Analysis on Movies and TV Shows on Netflix
- Performing Data Analysis on Movies and TV Shows on Amazon Prime
- Comparing Movies and TV Shows Data on Amazon Prime and Netflix
- Performing Data Analysis on Google Play Store Data
- Performing Data Analysis on Video Game Sales Data
- Performing Exploratory Data Analysis
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What is this practical Data Science training course about?+
It is an online training and skill development course that teaches the use of Python for data analysis. It is an elaborate course that talks about Pandas, NumPy, data extraction, and regression.
What is the benefit of using Python for data analysis?+
Python is a popular language for data analysis. It has a clean syntax which makes it easy to learn and understand, even for those without a programming background. Plus, Python's rich ecosystem, versatility, community support, and open-source nature make it an ideal choice.
Is this data science online training beginner-friendly?+
Yes. Although it is a beginner-friendly course that covers all the fundamentals, it is recommended that the student should have basic programming skills, an understanding of mathematical and statistical concepts with a keen interest in data and willingness to explore.
Will I get a certificate with this online Data Science training?+
Yes, you’ll be awarded a certification of completion after finishing the course.
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- 1 año de acceso completo
- 25 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito