Data Analysis and Visualization using Python Libraries and Streamlit Training

Live Online & Classroom Enterprise Training

Data Analysis and Visualization using Python and Streamlit covers data processing with libraries like Pandas, NumPy, and Matplotlib, alongside interactive web app development with Streamlit. It enables users to create dynamic, shareable data dashboards with minimal coding.

Looking for a private batch ?

REQUEST A CALLBACK

Enterprise Reporting
Lifetime Access
CloudLabs
24x7 Support
Real-time code analysis and feedback

What is Data Analysis and Visualization using Python Libraries and Streamlit Training about?

This course provides a hands-on introduction to data analysis and visualization using the Python ecosystem. Learners will explore how to clean, transform, and analyze datasets using libraries like Pandas and NumPy, and build compelling visualizations with Matplotlib and Seaborn. The course also introduces Streamlit, an open-source framework that allows you to build and share interactive web applications for data storytelling and analytics. By the end of the course, participants will have the skills to turn raw data into insightful visual applications ready for presentation or deployment.

What are the objectives of Data Analysis and Visualization using Python Libraries and Streamlit Training ?

Perform data manipulation and analysis using Pandas and NumPy.
Create visualizations with Matplotlib and Seaborn to communicate insights.
Build interactive dashboards using Streamlit.
Apply best practices in data cleaning, transformation, and presentation.
Integrate data analysis workflows into user-friendly web apps.

Who is Data Analysis and Visualization using Python Libraries and Streamlit Training for?

Data analysts and aspiring data scientists.
Business professionals seeking to make data-driven decisions.
Developers interested in building data visualization tools.
Students learning applied data analytics with Python.
Professionals transitioning to data-centric roles.

What are the prerequisites for Data Analysis and Visualization using Python Libraries and Streamlit Training?

Prerequisites:

Basic knowledge of Python programming.
Understanding of data types and structures (lists, dictionaries, arrays).
Familiarity with basic statistics and data interpretation.
Willingness to work with Jupyter Notebooks or IDEs.
Interest in visual analytics or interactive dashboards.

Learning Path:

Introduction to Data Analysis and Visualization Concepts
Data Manipulation with Pandas and NumPy
Building Visualizations with Matplotlib and Seaborn
Creating Interactive Dashboards using Streamlit
End-to-End Project: From Data Cleaning to Dashboard Deployment

Related Courses:

Python for Data Science
Data Visualization with Power BI or Tableau
Machine Learning with Python
Streamlit Advanced Applications and Deployment

Available Training Modes

Live Online Training

2 Days

Course Outline Expand All

Expand All

Module 1: Introduction to Data Analysis with Python

Overview of data analysis and visualization

Introduction to Pandas for data manipulation

Handling missing data and data preprocessing

Module 2: Data Visualization with Matplotlib and Seaborn

Creating static visualizations with Matplotlib

Enhancing visual aesthetics using Seaborn

Customizing plots and working with different chart types

Module 3: Interactive Visualizations with Plotly

Introduction to Plotly for interactive charts

Building dynamic visualizations with hover effects

Creating animated and multi-dimensional plots

Module 4: Introduction to Streamlit for Data Applications

Setting up a Streamlit environment

Creating a simple Streamlit application

Adding widgets and interactive elements

Module 5: Building Interactive Dashboards with Streamlit

Connecting data sources to Streamlit

Implementing user-driven filters and selectors

Deploying Streamlit apps on cloud platforms

Module 6: Advanced Data Analysis and Visualization Techniques

Time series analysis and visualization

Geospatial visualizations with Folium and Plotly

Case study: Building a complete data analysis and visualization project

Module 7: Real-World Applications and Deployment

Integrating Streamlit with machine learning models

Deploying Streamlit applications on AWS, Heroku, or Streamlit Cloud

Best practices for performance optimization

Who is the instructor for this training?

The trainer for this Data Analysis and Visualization using Python Libraries and Streamlit Training has extensive experience in this domain, including years of experience training & mentoring professionals.

Reviews

My outlook on training changed completely after attending SpringPeople BPC training. The content, the trainer and infrastructure at SpringPeople were top notch and perfectly in tune with the industry requirements. Regardless to say, training is now something that I look forward to to. Kudos to everyone at SpringPeople!

Shweta Priya

Sony

I attended the 3-day AngularJs training at SpringPeople. The trainer was an industry veteran with vast experience in the subject. Notably, the hands-on training, and the Q&A session stood out. Overall, I found SpringPeople a great place to learn with excellent facilities and great trainers. Would recommend SpringPeople to my colleagues and friends.

Swati Singh

I attended the training on API Design for Mulesoft. The sessions were well planned and value-laden. I benefited immensely from the hands-on experience enabled through virtual labs. I would like to specifically commend the efficiency of the support team who were always available to resolve my concerns.

Nikhil Kohli

Stryker

I attended the jQuery training batch, conducted by Mr. Vijay, an SME who did a thorough coverage of all the essentials. He took us through concepts such as jQuery animations, event handlers, plugins, and jQuery-UI by small programs, very easily. The sessions were useful and well structured. By the end of the training, I was well equipped to develop a SPA on Product Management System. Overall, the learning experience at SpringPeople was great!

Heena Rajan

Mindtree