What are the prerequisites for this course?

A basic understanding of Python programming and familiarity with data manipulation libraries such as Pandas or NumPy is recommended.

How can I apply the skills learned in this course?

The skills you acquire will be valuable for roles in data analytics, business intelligence, and data science. You can use these skills to create impactful visualizations, analyze complex datasets, and communicate insights effectively.

Which Python libraries will I use for visualization and interactive dashboards?

You’ll work with Matplotlib, Seaborn, Plotly, and Streamlit to create both static and interactive data visualizations.

Can I build interactive dashboards and deploy them with Streamlit?

Yes, you’ll learn how to design interactive dashboards in Streamlit and deploy them to share with others.

Do I need prior experience with data manipulation libraries like Pandas or NumPy?

No prior experience is required. The course introduces Pandas and NumPy from scratch and uses them in practical examples.

What career opportunities can data visualization skills open up?

Data visualization skills qualify you for roles like Data Analyst, Business Intelligence Developer, and Data Scientist.

Will I earn a certificate after completing the course?

Yes, you’ll receive a Coursera certificate to showcase your skills to employers and share on LinkedIn.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Python for Data Visualization and Analysis

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Python for Data Visualization and Analysis

Q: What practical skills will I gain from this course?

By the end of the course, you’ll be able to:Design professional and interactive data visualizations.Perform EDA to uncover patterns and trends in data.Deploy data visualization applications using Streamlit.

Ce cours fait partie de Spécialisation "Applied Data Analytics"

Instructeur : Edureka

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Create impactful visualizations using Matplotlib and Seaborn to represent complex datasets effectively.
Build dynamic, interactive charts and dashboards with Plotly and IPyWidgets for enhanced data exploration.
Build dynamic, interactive charts and dashboards with Plotly and IPyWidgets for enhanced data exploration.
Deploy interactive data visualization applications seamlessly with Streamlit to share analysis results.

Compétences que vous acquerrez

Catégorie : Exploratory Data Analysis
Catégorie : Data Visualization
Catégorie : Data Storytelling
Catégorie : Data Presentation
Catégorie : Dashboard Creation
Catégorie : Plot (Graphics)
Catégorie : Matplotlib

Outils que vous découvrirez

Catégorie : Seaborn
Catégorie : Plotly

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

13 devoirs

Enseigné en Anglais

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Ce cours fait partie de la Spécialisation "Applied Data Analytics"

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Acquérez une compréhension de base d'un sujet ou d'un outil
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Obtenez un certificat professionnel partageable

Il y a 4 modules dans ce cours

This Python for Data Visualization Analysis course provides a practical introduction to data visualization and exploratory data analysis (EDA) using Python. You will work with Matplotlib and Seaborn to create clear and effective visualizations, use Plotly to build interactive charts and dashboards, and apply advanced graphical techniques for EDA on complex datasets. Learn to present data clearly and extract meaningful insights through visual analysis.

By the end of this course, you’ll be able to: - Understand the importance of various visualization techniques. - Select appropriate chart types for visualizing diverse datasets. - Create professional-quality visuals with Matplotlib, Seaborn, and Plotly. - Develop interactive dashboards and visuals with Plotly and IPyWidgets. - Perform EDA on complex datasets and deploy the results using Streamlit. This course is ideal for learners with foundational knowledge of Python programming and a basic understanding of data manipulation. Familiarity with libraries such as Pandas or NumPy is recommended. Whether you're a data analyst, aspiring data scientist, or Python programmer looking to sharpen your data visualization skills, this course equips you with the tools to transform raw data into meaningful stories. Elevate your data analysis journey—enroll in Data Visualization and Exploratory Data Analysis with Python today!

In this module, learners will explore how to create various types of visualizations using Matplotlib. They will learn to apply these visuals to complex datasets, uncovering hidden insights that facilitate informed decision-making.

Inclus

17 vidéos5 lectures4 devoirs1 sujet de discussion

17 vidéosTotal 52 minutes

Course Introduction3 minutes
Environment Set-Up3 minutes
Importance of Data Visualization2 minutes
Line Plot2 minutes
Bar Chart2 minutes
Horizontal Bar Chart2 minutes
Stacked Bar Chart1 minute
Histogram2 minutes
Demonstration: Plotting Line and Bar Graph6 minutes
Demonstration: Plotting Histogram5 minutes
Scatter Plot3 minutes
Pie Chart3 minutes
Box Plot4 minutes
Customizing Charts2 minutes
Demonstration: Pie Chart3 minutes
Demonstration: Scatter Plot and Box Plot4 minutes
Summary of Visualization with Matplotlib3 minutes

5 lecturesTotal 95 minutes

Welcome to Python for Data Visualization and Analysis10 minutes
From Numbers to Narratives20 minutes
Choosing the Right Chart: Bar Charts, Line Charts and Histogram25 minutes
Choosing the Right Chart Type20 minutes
Choosing the Right Chart: Scatter, Pie and Box20 minutes

4 devoirsTotal 29 minutes

Knowledge Check : Visualizing Data with Matplotlib20 minutes
Practice Quiz : Setting Up Matplotlib3 minutes
Practice Quiz : Types of Plots and Charts3 minutes
Practice Quiz : Plotting Different Charts3 minutes

1 sujet de discussionTotal 10 minutes

Introduce Yourself10 minutes

In this module, learners will delve into data visualization with Seaborn, mastering the creation of diverse plots while developing skills to customize and refine visuals for improved presentation and interactivity.

Inclus

12 vidéos2 lectures3 devoirs1 sujet de discussion

12 vidéosTotal 41 minutes

What is Seaborn?2 minutes
Installing and Setting Up Seaborn2 minutes
Comparing Seaborn with Matplotlib2 minutes
Relational Plot (Rel plot)2 minutes
Distribution Plot (Dist Plot)2 minutes
Categorical Plot (Cat Plot)3 minutes
Demonstration: Visualizing Charts with Seaborn5 minutes
Demonstration: Visualizing HeatMap2 minutes
Demonstration: Category, Relational and Distribution Plots 6 minutes
Demonstration: Personalizing Charts and Visuals7 minutes
Demonstration: Tailoring Graphs and Visuals6 minutes
Summary for Data Visualization with Seaborn 3 minutes

2 lecturesTotal 40 minutes

Seaborn with Matplotlib20 minutes
A Guide to Seaborn20 minutes

3 devoirsTotal 26 minutes

Knowledge Check : Visualizing Data with Seaborn20 minutes
Practice Quiz : Seaborn Library3 minutes
Practice Quiz : Plot Types in Seaborn3 minutes

1 sujet de discussionTotal 10 minutes

Which of the following is easier to use, Seaborn or Matplotlib?10 minutes

In this module, learners will explore how to create interactive plots using Plotly, enhance exploratory data analysis (EDA) with IPyWidgets, and build shareable web applications with Streamlit. They will also gain the skills to develop dynamic dashboards and interactive reports for effective data presentation.

Inclus

24 vidéos3 lectures5 devoirs1 sujet de discussion

24 vidéosTotal 99 minutes

Plotly4 minutes
Customizing Basic Plot - Background and Markers4 minutes
Customizing Basic Plot - Lines, Titles and Labels 3 minutes
Customizing Basic Plot - Interactive3 minutes
Interactive Plots3 minutes
Demonstration: Plots with Hover Feature3 minutes
Demonstration: Customizing Hover Features and Tooltips4 minutes
Plotly Dash6 minutes
Demonstration: Defining Layout and Structure4 minutes
Demonstration: Building Web Apps 4 minutes
Demonstration: Chaining Callbacks4 minutes
Demonstration: Multiple Inputs and Outputs with Interactions4 minutes
Demonstration: Importing Airbnb Data2 minutes
Demonstration: Web App for Airbnb Data4 minutes
IPyWidgets5 minutes
Displaying Widgets Layouts and Container Widgets4 minutes
Interactive Controls Combining Multiple Widgets for Interactivity5 minutes
Custom Widgets Creating and Registering Custom Widgets4 minutes
Extending Widget Functionality5 minutes
What is Streamlit?2 minutes
Demonstration: Code Details 6 minutes
Demonstration: Executing the App4 minutes
Demonstration: Data Visualization on Streamlit6 minutes
Summary for Interactive Data Visualization3 minutes

3 lecturesTotal 40 minutes

Turning Static Plots Interactive20 minutes
Plotly dash: Best Practices10 minutes
Building with Streamlit10 minutes

5 devoirsTotal 32 minutes

Knowledge Check : Interactive Visuals with Plotly and IPyWidgets20 minutes
Practice Quiz : Plotly Library3 minutes
Practice Quiz : Plotly Dashboard3 minutes
Practice Quiz : Working of IPyWidgets3 minutes
Practice Quiz : Streamlit3 minutes

1 sujet de discussionTotal 10 minutes

How do IPyWidgets enhance the interactivity of Jupyter Notebook projects?10 minutes

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on data visualization concepts, Matploltlib, Seaborn, Plotly and Association rule mining.

Inclus

1 vidéo1 lecture1 devoir1 sujet de discussion

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

Edureka

187 Cours176 137 apprenants

Offert par

Edureka

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Foire Aux Questions

This course is ideal for data analysts, aspiring data scientists, and Python programmers who want to develop skills in data visualization and exploratory data analysis using Python. A basic understanding of Python programming and familiarity with libraries like Pandas or NumPy is recommended.

No prior experience in data visualization is required. This course provides a step-by-step approach, starting with foundational concepts and progressing to advanced techniques using tools like Matplotlib, Seaborn, and Plotly.

By the end of the course, you’ll be able to:

- Design professional and interactive data visualizations.

- Perform EDA to uncover patterns and trends in data.

- Deploy data visualization applications using Streamlit.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.