EDA-PYTHON.AJ1
Exploratory Data Analysis with Python
Gain the critical skills to visualize and analyze data using Python language and its libraries.
- Practice in 77 Laboratorios prácticos — nothing to install
- 13 Lecciones interactivas y 90 topics mapped to the official exam objectives
- 160 Preguntas del examen de práctica
Intermediate A tu propio ritmo · 1 año de acceso 4.5/5 (80 Revisar)
77 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
13Lecciones interactivas
90Topics
77Laboratorio en vivo
160Preguntas del examen de práctica
80Tarjetas didácticas
80Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
This course is all about practicing Exploratory Data Analysis with Python. You’ll learn to visualize, transform, and analyze data using Python’s powerful tools like Pandas, Seaborn, and Matplotlip. By delving into real-world datasets, you’ll discover patterns and insights that drive decision-making. Ideal for aspiring data scientists, analysts, and anyone keen to enhance their data exploration skills.
- Learn the basics of Exploratory Data Analysis (EDA) in Python
- Use Python libraries like Pandas, Seaborn, and Matplotlib for data analysis
- Visualize data with various types of charts and graphs
- Transform and clean datasets for analysis
- Perform statistical analysis to uncover insights
- Group and aggregate data for deeper analysis
- Analyze correlations and understand their significance
- Handle missing values and perform data imputation
- Conduct hypothesis testing and regression analysis
- Create reproducible data analysis workflows
- Implement machine learning models for data evaluation
Course Highlights
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13 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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77 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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160 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
13 Lecciones interactivas · 90 topics01 Preface 4 topics +
- Who this course is for?
- What this course covers?
- To get the most out of this course
- Conventions used
02 Exploratory Data Analysis Fundamentals 8 topics · 9 Laboratorio en vivo +
- Understanding data science
- The significance of EDA
- Making sense of data
- Comparing EDA with classical and Bayesian analysis
- Software tools available for EDA
- Getting started with EDA
- Summary
- Further reading
9 Laboratorio en vivo in this lesson — see the labs panel →
03 Visual Aids for EDA 14 topics · 11 Laboratorio en vivo +
- Technical requirements
- Line chart
- Bar charts
- Scatter plot
- Area plot and stacked plot
- Pie chart
- Table chart
- Polar chart
- Histogram
- Lollipop chart
- Choosing the best chart
- Other libraries to explore
- Summary
- Further reading
11 Laboratorio en vivo in this lesson — see the labs panel →
04 Activity: EDA with Personal Email 6 topics · 5 Laboratorio en vivo +
- Technical requirements
- Loading the dataset
- Data transformation
- Data analysis
- Summary
- Further reading
5 Laboratorio en vivo in this lesson — see the labs panel →
05 Data Transformation 7 topics · 15 Laboratorio en vivo +
- Technical requirements
- Background
- Merging database-style dataframes
- Transformation techniques
- Benefits of data transformation
- Summary
- Further reading
15 Laboratorio en vivo in this lesson — see the labs panel →
06 Descriptive Statistics 6 topics · 9 Laboratorio en vivo +
- Technical requirements
- Understanding statistics
- Measures of central tendency
- Measures of dispersion
- Summary
- Further reading
9 Laboratorio en vivo in this lesson — see the labs panel →
07 Grouping Datasets 7 topics · 6 Laboratorio en vivo +
- Technical requirements
- Understanding groupby()
- Groupby mechanics
- Data aggregation
- Pivot tables and cross-tabulations
- Summary
- Further reading
6 Laboratorio en vivo in this lesson — see the labs panel →
08 Correlation 8 topics · 1 Laboratorio en vivo +
- Technical requirements
- Introducing correlation
- Types of analysis
- Discussing multivariate analysis using the Titanic dataset
- Outlining Simpson's paradox
- Correlation does not imply causation
- Summary
- Further reading
1 Laboratorio en vivo in this lesson — see the labs panel →
09 Activity: Time Series Analysis 5 topics · 3 Laboratorio en vivo +
- Technical requirements
- Understanding the time series dataset
- TSA with Open Power System Data
- Summary
- Further reading
3 Laboratorio en vivo in this lesson — see the labs panel →
10 Hypothesis Testing and Regression 6 topics · 5 Laboratorio en vivo +
- Hypothesis testing
- p-hacking
- Understanding regression
- Model development and evaluation
- Summary
- Further reading
5 Laboratorio en vivo in this lesson — see the labs panel →
11 Model Development and Evaluation 8 topics · 1 Laboratorio en vivo +
- Technical requirements
- Types of machine learning
- Understanding supervised learning
- Understanding unsupervised learning
- Understanding reinforcement learning
- Unified machine learning workflow
- Summary
- Further reading
1 Laboratorio en vivo in this lesson — see the labs panel →
12 Activity: EDA on Wine Quality Data Analysis 7 topics · 2 Laboratorio en vivo +
- Technical requirements
- Disclosing the wine quality dataset
- Analyzing red wine
- Analyzing white wine
- Model development and evaluation
- Summary
- Further reading
2 Laboratorio en vivo in this lesson — see the labs panel →
13 Appendix 4 topics · 10 Laboratorio en vivo +
- String manipulation
- Using pandas vectorized string functions
- Using regular expressions
- Further reading
10 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
77 Laboratorio en vivos- Styling a Dataframe
- Applying Function to a Dataframe
- Slicing and Subsetting
- Dividing NumPy Arrays
- Inspecting NumPy Arrays
- Defining NumPy arrays
- Selecting rows
- Reading Data from a CSV File
- Creating a Dataframe
- Creating a Line chart
- Creating a Bar Chart
- Creating a Scatter Plot
- Creating a Bubble Chart
- Creating an Area Plot
- Creating a Pie Chart
- Creating a Table Chart
- Creating a Polar Chart
- Adding the Best-Fit Line for the Normal Distribution
- Creating a Histogram
- Creating a Lollipop Chart
- Performing EDA with Email Data
- Extracting Email Using Regex
- Converting a Field to datetime
- Removing NaN Values
- Dropping a Column
- Stacking a Dataframe
- Concatenating Dataframes
- Analyzing Dataframes
- Combining Dataframes
- Merging on Index
- Permuting a Dataframe
- Removing Duplicate Data
- Replacing Values
- Interpolating Missing Values
- Backward and Forward Filling
- Handling NaN values
- Counting Missing Values
- Renaming Axis Indexes
- Binning
- Detecting Outliers
- Generating a Binomial Distribution Plot
- Generating an Exponential Distribution Plot
- Generating a Normal Distribution Plot
- Generating a Uniform Distribution Plot
- Using Statistical Functions
- Calculating Standard Deviation
- Finding Skewness and Kurtosis
- Creating a Box Plot
- Calculating Inter-Quartile Range
- Finding Maximum Value for Each Group
- Grouping a Dataset
- Filtering Data
- Applying Aggregation Functions
- Creating a Pivot Table
- Creating a Cross-Tabulation Table
- Calculating Correlation Coefficient
- Sampling the Data
- Resampling the Data
- Changing the Index of a Dataframe
- Performing Z-Test
- Calculating the P-Value
- Performing T-test
- Scoring the Model
- Understanding the Linear Regression Model
- Using TfidfVectorizer
- Plotting a Heatmap
- Visualizing the Data in 3D Form
- Accessing Characters
- String Slicing
- Updating a String
- Escape Sequencing
- Formatting Strings
- Displaying Last 10 items from a Dataframe
- Using String Functions with a Dataframe
- Finding Words from a String
- Counting Full Stops using Regex
- Matching Characters
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What is Exploratory Data Analysis (EDA)? +
EDA in Python is a critical process in data analysis that helps in understanding the main characteristics of a dataset through visuals and statistical techniques.
What is the difference between EDA and data visualization? +
- EDA: It focuses on analyzing datasets to find patterns, trends, and relationships using statistical methods. It helps to identify and discover patterns.
- Data visualization: It presents these findings visually through charts, graphs, and plots to make insights easier to understand. Data visualization helps communicate them.
Why use Python for EDA? +
Python is ideal for EDA due to its powerful libraries like Pandas, Seaborn, and Matplotlib, which make data manipulation, visualization, and analysis straightforward and efficient.
What are the benefits of EDA?+
Exploratory data analysis techniques in Python help in identifying patterns, spotting anomalies, testing hypotheses, and checking assumptions, all of which are crucial steps before building predictive models. In addition, it will allow you to take on advanced projects and pursue specialized roles in your field.
What is the average salary for a data analyst with EDA skills? +
The average salary for a data analyst with EDA skills ranges from $70,000 to $100,000 per year, depending on experience, location, and industry.
What career opportunities can I pursue after completing this course? +
By mastering EDA with Python, you’ll enhance your ability to interpret and present data, making you a valuable asset in any data-driven organization. Career opportunities include roles such as Data Analyst, Data Scientist, Business Analyst, and Data Engineer, where EDA skills are highly valued.
Getting Started with EDA Using Python
Get hands on exploratory data analysis with Python training and make data-driven decisions in your career.
- 1 año de acceso completo
- 77 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito