This course is designed for those interested to learn the basics of Streamlit, how to use this framework to build data science applications, and how to authenticate these Streamlit applications. Read more.
Derrick Mwiti is a Google Developer Expert in machine learning. He has a great passion for sharing knowledge. He is an avid contributor to the data science community.
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About This Course
Who this course is for:
- People who want to create things with data science and machine learning using Python.
What you’ll learn:Â
- Python crash course
- NumPy crash course
- Introduction to the Streamlit app
- Integrating Matplotlit and Seaborn in Streamlit
- Using Altair and Vega-Lite in Streamlit
- Understand all Streamlit widgets
- Upload and process files
- Build an Image Processing Application
- Develop a Natural Language Processing Application
- Integrate Maps with Streamlit
- Implement Plotly graphs
- Authenticate your applications
- Laying out your application in Streamlit
- Developing with Streamlit components
- Deploying applications of data science
Requirements:Â
- Basic Python Programming, however a Python crash course is included
Diving into the intricacies of data analysis and constructing machine learning models marks the initial phase of the journey. Yet, presenting these analyses and models in a way that is easily shareable constitutes an entirely distinct challenge. This course has a clear objective: to guide you through the swiftest and most uncomplicated approach to develop and share data applications using Streamlit. The beauty of it all is that you won’t need any prior experience in creating front-end applications.
As you progress through the course, you’ll find yourself creating a variety of applications, each serving as a valuable addition to your data science portfolio. These projects won’t just be lines on your resume—they will be tangible demonstrations of your capabilities. Moreover, the newfound skill of working with Streamlit that you’ll acquire is more than just a feather in your cap; it’s a valuable asset that can set you apart in the competitive landscape of data science and machine learning.
Imagine having not only the theoretical knowledge of data analysis and machine learning but also the practical ability to present and share your findings seamlessly. This course provides you with that unique advantage. It’s not just about learning; it’s about equipping yourself with the tools and skills to make your mark in the field. So, get ready to elevate your expertise, create impactful data applications, and carve a distinctive niche for yourself in the world of data science!
If you enjoyed this, check out My Profile for more!
Our Promise to You
By the end of this course, you will have learned Data Science applications with Streamlit.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today and learn more about Data Science applications with Streamlit.
Course Curriculum
| Section 1 - Introduction | |||
| Introduction To Streamlit | 00:00:00 | ||
| Download All The Course Files | 00:00:00 | ||
| Section 2 - Python Crash Course | |||
| Section Introduction | 00:00:00 | ||
| Install Anaconda | 00:00:00 | ||
| The Data Science Process | 00:00:00 | ||
| Python For Data Science | 00:00:00 | ||
| A Linux Launch Notebook | 00:00:00 | ||
| B Windows Launch Notebook | 00:00:00 | ||
| Folder Structure | 00:00:00 | ||
| Python Operations And Comments | 00:00:00 | ||
| Python Types | 00:00:00 | ||
| Lists And Indexing | 00:00:00 | ||
| Lists - Negative Indexing | 00:00:00 | ||
| Python Dictionaries | 00:00:00 | ||
| Python Tuples | 00:00:00 | ||
| Python Sets | 00:00:00 | ||
| Python Boolean Operators | 00:00:00 | ||
| Conditional Statements | 00:00:00 | ||
| Python Functions | 00:00:00 | ||
| Python For Loop | 00:00:00 | ||
| Python While Loop | 00:00:00 | ||
| Python Map Function | 00:00:00 | ||
| Python Range Function | 00:00:00 | ||
| Python Exercise | 00:00:00 | ||
| Python Solutions | 00:00:00 | ||
| Section 3 - Package Management In Python | |||
| Section Introduction | 00:00:00 | ||
| Virtual Environment | 00:00:00 | ||
| Pip Practical | 00:00:00 | ||
| Anaconda Package Installation | 00:00:00 | ||
| Section 4 - Numpy Crash Course | |||
| NumPy Introduction | 00:00:00 | ||
| NumPy Zeros, Ones, And Linspace | 00:00:00 | ||
| Checking NumPy Documentation | 00:00:00 | ||
| One Dimensional Indexing | 00:00:00 | ||
| Multi Dimensional Indexing | 00:00:00 | ||
| Broadcasting In NumPy | 00:00:00 | ||
| Operations In NumPy | 00:00:00 | ||
| NumPy Exercise | 00:00:00 | ||
| NumPy Solutions | 00:00:00 | ||
| Section 5 - Pandas Crash Course | |||
| Section Introduction | 00:00:00 | ||
| Introduction To Pandas | 00:00:00 | ||
| Pandas Dataframes | 00:00:00 | ||
| Resetting The Index | 00:00:00 | ||
| Dropping Columns | 00:00:00 | ||
| Dealing With Null Values | 00:00:00 | ||
| Creating New Columns | 00:00:00 | ||
| Selecting In Pandas | 00:00:00 | ||
| Grouping Data | 00:00:00 | ||
| Exporting Data Frames | 00:00:00 | ||
| Loading Data | 00:00:00 | ||
| Pivot Tables | 00:00:00 | ||
| Pandas Project | 00:00:00 | ||
| Solutions Part 1 | 00:00:00 | ||
| Solutions Part 2 | 00:00:00 | ||
| Solutions Part 3 | 00:00:00 | ||
| Solutions Part 4 | 00:00:00 | ||
| Solutions Part 5 | 00:00:00 | ||
| Solutions Part 6 | 00:00:00 | ||
| Solutions Part 7 | 00:00:00 | ||
| Section 6 - Visualization Guide | |||
| Visualization Guide | 00:00:00 | ||
| Section 7 - Matplotlib With Streamlit | |||
| Matplotlib Introduction | 00:00:00 | ||
| Set Up Environment | 00:00:00 | ||
| First Visual | 00:00:00 | ||
| Markdown | 00:00:00 | ||
| Bar Plot | 00:00:00 | ||
| Create Horizontal Bar | 00:00:00 | ||
| Create Scatter Plot | 00:00:00 | ||
| Histogram | 00:00:00 | ||
| Pie Chart | 00:00:00 | ||
| Make Sub Plots | 00:00:00 | ||
| Create Four Sub Plots | 00:00:00 | ||
| Figure And Axes | 00:00:00 | ||
| Four Plots With Figure And Axes | 00:00:00 | ||
| Section 8 - Streamlit With Seaborn | |||
| Section Introduction | 00:00:00 | ||
| Data Introduction | 00:00:00 | ||
| App Introduction | 00:00:00 | ||
| Create Count Plot | 00:00:00 | ||
| Stripplot And Violin Plot | 00:00:00 | ||
| Exercise | 00:00:00 | ||
| Show Trend | 00:00:00 | ||
| Seaborn Sub Plots Exercise | 00:00:00 | ||
| Figure And Axes | 00:00:00 | ||
| Word Cloud | 00:00:00 | ||
| Section 9 - Extras | |||
| Extras - Page Title, Favicon Etc | 00:00:00 | ||
| Section 10 - File Upload | |||
| File Upload | 00:00:00 | ||
| Section 11 - Mapping | |||
| Map | 00:00:00 | ||
| Section 12 - Image Processing Application | |||
| Section Introduction | 00:00:00 | ||
| Show Image | 00:00:00 | ||
| Rotate Image | 00:00:00 | ||
| Create Thumbnail | 00:00:00 | ||
| Cropped Image [Exercise Included] | 00:00:00 | ||
| [Exercise] Merging Images | 00:00:00 | ||
| Flip Image | 00:00:00 | ||
| Convert To Black And White | 00:00:00 | ||
| Sharpen The Image | 00:00:00 | ||
| Enhance The Edges | 00:00:00 | ||
| Contrast The Image | 00:00:00 | ||
| Section 13 - Streamlit Components | |||
| Section Introduction | 00:00:00 | ||
| Integrate Streamlit Components | 00:00:00 | ||
| Section 14 - Streamlit Authentication | |||
| Authenticate Your App | 00:00:00 | ||
| Section 15 - Plotly With Streamlit | |||
| Section Introduction | 00:00:00 | ||
| Plotly Introduction | 00:00:00 | ||
| Load The Data | 00:00:00 | ||
| Create Pie Chart - Categorical Plot | 00:00:00 | ||
| Create Donut Chart | 00:00:00 | ||
| Create Scatter Plot | 00:00:00 | ||
| Scatter With Columns | 00:00:00 | ||
| Show The Trend | 00:00:00 | ||
| Create Bar Chart | 00:00:00 | ||
| Stack The Bar Chart | 00:00:00 | ||
| Create Animations | 00:00:00 | ||
| Make Subplots | 00:00:00 | ||
| Section 16 - Streamlit With Altair | |||
| Altair Introduction | 00:00:00 | ||
| Vega Lite | 00:00:00 | ||
| Vega Lite Scatter Plot | 00:00:00 | ||
| Create Altair Line Chart | 00:00:00 | ||
| Create Altair Line Plot | 00:00:00 | ||
| Create Altair Scatter Plot | 00:00:00 | ||
| Create Altair Area Chart | 00:00:00 | ||
| Create Altair Scatter Matrix | 00:00:00 | ||
| Scatter With Links [ Exercise] | 00:00:00 | ||
| Create Altair Heatmap [Exercise] | 00:00:00 | ||
| Create Horizontal Altair Bar Plot [Exercise] | 00:00:00 | ||
| Set Theme | 00:00:00 | ||
| Build An Altair Grouped Bar Chart | 00:00:00 | ||
| Stack The Altair Bar Chart [Exercise] | 00:00:00 | ||
| Normalized Stacked Bar Chart [Exercise | 00:00:00 | ||
| Add Text To The Chart | 00:00:00 | ||
| Add Altair Boxplot | 00:00:00 | ||
| Create An Interactive Legend In Altair | 00:00:00 | ||
| Concatenate Charts | 00:00:00 | ||
| Create Dual Y Axis In Altair | 00:00:00 | ||
| Horizontal Concatenation [Exercise] | 00:00:00 | ||
| Vertical Concatenation [Exercise] | 00:00:00 | ||
| Interactive Chart | 00:00:00 | ||
| Section 17 - Streamlit Layout | |||
| Laying Out Your Application | 00:00:00 | ||
| Add Interactivity - Date Inputs | 00:00:00 | ||
| Add Interactivity - Input Box | 00:00:00 | ||
| Add Interactivity - Drop Down [Exercise] | 00:00:00 | ||
| Add Interactivity - Select Slider [Exercise] | 00:00:00 | ||
| Add Interactivity - Select Box | 00:00:00 | ||
| Add Interactivity - Button | 00:00:00 | ||
| Section 18 - Natural Language Processing | |||
| Section Introduction | 00:00:00 | ||
| Sentiment Analysis | 00:00:00 | ||
| Create A Question Answering App | 00:00:00 | ||
| Generate Text [Exercise] | 00:00:00 | ||
| Named Entity Recognition[Exercise] | 00:00:00 | ||
| Summarize Text | 00:00:00 | ||
| Translate Text [Exercise] | 00:00:00 | ||
| Section 19 - Deploy Streamlit Application | |||
| Section Introduction | 00:00:00 | ||
| Streamlit Sharing | 00:00:00 | ||
| Heroku | 00:00:00 | ||
About This Course
Who this course is for:
- People who want to create things with data science and machine learning using Python.
What you’ll learn:Â
- Python crash course
- NumPy crash course
- Introduction to the Streamlit app
- Integrating Matplotlit and Seaborn in Streamlit
- Using Altair and Vega-Lite in Streamlit
- Understand all Streamlit widgets
- Upload and process files
- Build an Image Processing Application
- Develop a Natural Language Processing Application
- Integrate Maps with Streamlit
- Implement Plotly graphs
- Authenticate your applications
- Laying out your application in Streamlit
- Developing with Streamlit components
- Deploying applications of data science
Requirements:Â
- Basic Python Programming, however a Python crash course is included
Diving into the intricacies of data analysis and constructing machine learning models marks the initial phase of the journey. Yet, presenting these analyses and models in a way that is easily shareable constitutes an entirely distinct challenge. This course has a clear objective: to guide you through the swiftest and most uncomplicated approach to develop and share data applications using Streamlit. The beauty of it all is that you won’t need any prior experience in creating front-end applications.
As you progress through the course, you’ll find yourself creating a variety of applications, each serving as a valuable addition to your data science portfolio. These projects won’t just be lines on your resume—they will be tangible demonstrations of your capabilities. Moreover, the newfound skill of working with Streamlit that you’ll acquire is more than just a feather in your cap; it’s a valuable asset that can set you apart in the competitive landscape of data science and machine learning.
Imagine having not only the theoretical knowledge of data analysis and machine learning but also the practical ability to present and share your findings seamlessly. This course provides you with that unique advantage. It’s not just about learning; it’s about equipping yourself with the tools and skills to make your mark in the field. So, get ready to elevate your expertise, create impactful data applications, and carve a distinctive niche for yourself in the world of data science!
If you enjoyed this, check out My Profile for more!
Our Promise to You
By the end of this course, you will have learned Data Science applications with Streamlit.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today and learn more about Data Science applications with Streamlit.
Course Curriculum
| Section 1 - Introduction | |||
| Introduction To Streamlit | 00:00:00 | ||
| Download All The Course Files | 00:00:00 | ||
| Section 2 - Python Crash Course | |||
| Section Introduction | 00:00:00 | ||
| Install Anaconda | 00:00:00 | ||
| The Data Science Process | 00:00:00 | ||
| Python For Data Science | 00:00:00 | ||
| A Linux Launch Notebook | 00:00:00 | ||
| B Windows Launch Notebook | 00:00:00 | ||
| Folder Structure | 00:00:00 | ||
| Python Operations And Comments | 00:00:00 | ||
| Python Types | 00:00:00 | ||
| Lists And Indexing | 00:00:00 | ||
| Lists - Negative Indexing | 00:00:00 | ||
| Python Dictionaries | 00:00:00 | ||
| Python Tuples | 00:00:00 | ||
| Python Sets | 00:00:00 | ||
| Python Boolean Operators | 00:00:00 | ||
| Conditional Statements | 00:00:00 | ||
| Python Functions | 00:00:00 | ||
| Python For Loop | 00:00:00 | ||
| Python While Loop | 00:00:00 | ||
| Python Map Function | 00:00:00 | ||
| Python Range Function | 00:00:00 | ||
| Python Exercise | 00:00:00 | ||
| Python Solutions | 00:00:00 | ||
| Section 3 - Package Management In Python | |||
| Section Introduction | 00:00:00 | ||
| Virtual Environment | 00:00:00 | ||
| Pip Practical | 00:00:00 | ||
| Anaconda Package Installation | 00:00:00 | ||
| Section 4 - Numpy Crash Course | |||
| NumPy Introduction | 00:00:00 | ||
| NumPy Zeros, Ones, And Linspace | 00:00:00 | ||
| Checking NumPy Documentation | 00:00:00 | ||
| One Dimensional Indexing | 00:00:00 | ||
| Multi Dimensional Indexing | 00:00:00 | ||
| Broadcasting In NumPy | 00:00:00 | ||
| Operations In NumPy | 00:00:00 | ||
| NumPy Exercise | 00:00:00 | ||
| NumPy Solutions | 00:00:00 | ||
| Section 5 - Pandas Crash Course | |||
| Section Introduction | 00:00:00 | ||
| Introduction To Pandas | 00:00:00 | ||
| Pandas Dataframes | 00:00:00 | ||
| Resetting The Index | 00:00:00 | ||
| Dropping Columns | 00:00:00 | ||
| Dealing With Null Values | 00:00:00 | ||
| Creating New Columns | 00:00:00 | ||
| Selecting In Pandas | 00:00:00 | ||
| Grouping Data | 00:00:00 | ||
| Exporting Data Frames | 00:00:00 | ||
| Loading Data | 00:00:00 | ||
| Pivot Tables | 00:00:00 | ||
| Pandas Project | 00:00:00 | ||
| Solutions Part 1 | 00:00:00 | ||
| Solutions Part 2 | 00:00:00 | ||
| Solutions Part 3 | 00:00:00 | ||
| Solutions Part 4 | 00:00:00 | ||
| Solutions Part 5 | 00:00:00 | ||
| Solutions Part 6 | 00:00:00 | ||
| Solutions Part 7 | 00:00:00 | ||
| Section 6 - Visualization Guide | |||
| Visualization Guide | 00:00:00 | ||
| Section 7 - Matplotlib With Streamlit | |||
| Matplotlib Introduction | 00:00:00 | ||
| Set Up Environment | 00:00:00 | ||
| First Visual | 00:00:00 | ||
| Markdown | 00:00:00 | ||
| Bar Plot | 00:00:00 | ||
| Create Horizontal Bar | 00:00:00 | ||
| Create Scatter Plot | 00:00:00 | ||
| Histogram | 00:00:00 | ||
| Pie Chart | 00:00:00 | ||
| Make Sub Plots | 00:00:00 | ||
| Create Four Sub Plots | 00:00:00 | ||
| Figure And Axes | 00:00:00 | ||
| Four Plots With Figure And Axes | 00:00:00 | ||
| Section 8 - Streamlit With Seaborn | |||
| Section Introduction | 00:00:00 | ||
| Data Introduction | 00:00:00 | ||
| App Introduction | 00:00:00 | ||
| Create Count Plot | 00:00:00 | ||
| Stripplot And Violin Plot | 00:00:00 | ||
| Exercise | 00:00:00 | ||
| Show Trend | 00:00:00 | ||
| Seaborn Sub Plots Exercise | 00:00:00 | ||
| Figure And Axes | 00:00:00 | ||
| Word Cloud | 00:00:00 | ||
| Section 9 - Extras | |||
| Extras - Page Title, Favicon Etc | 00:00:00 | ||
| Section 10 - File Upload | |||
| File Upload | 00:00:00 | ||
| Section 11 - Mapping | |||
| Map | 00:00:00 | ||
| Section 12 - Image Processing Application | |||
| Section Introduction | 00:00:00 | ||
| Show Image | 00:00:00 | ||
| Rotate Image | 00:00:00 | ||
| Create Thumbnail | 00:00:00 | ||
| Cropped Image [Exercise Included] | 00:00:00 | ||
| [Exercise] Merging Images | 00:00:00 | ||
| Flip Image | 00:00:00 | ||
| Convert To Black And White | 00:00:00 | ||
| Sharpen The Image | 00:00:00 | ||
| Enhance The Edges | 00:00:00 | ||
| Contrast The Image | 00:00:00 | ||
| Section 13 - Streamlit Components | |||
| Section Introduction | 00:00:00 | ||
| Integrate Streamlit Components | 00:00:00 | ||
| Section 14 - Streamlit Authentication | |||
| Authenticate Your App | 00:00:00 | ||
| Section 15 - Plotly With Streamlit | |||
| Section Introduction | 00:00:00 | ||
| Plotly Introduction | 00:00:00 | ||
| Load The Data | 00:00:00 | ||
| Create Pie Chart - Categorical Plot | 00:00:00 | ||
| Create Donut Chart | 00:00:00 | ||
| Create Scatter Plot | 00:00:00 | ||
| Scatter With Columns | 00:00:00 | ||
| Show The Trend | 00:00:00 | ||
| Create Bar Chart | 00:00:00 | ||
| Stack The Bar Chart | 00:00:00 | ||
| Create Animations | 00:00:00 | ||
| Make Subplots | 00:00:00 | ||
| Section 16 - Streamlit With Altair | |||
| Altair Introduction | 00:00:00 | ||
| Vega Lite | 00:00:00 | ||
| Vega Lite Scatter Plot | 00:00:00 | ||
| Create Altair Line Chart | 00:00:00 | ||
| Create Altair Line Plot | 00:00:00 | ||
| Create Altair Scatter Plot | 00:00:00 | ||
| Create Altair Area Chart | 00:00:00 | ||
| Create Altair Scatter Matrix | 00:00:00 | ||
| Scatter With Links [ Exercise] | 00:00:00 | ||
| Create Altair Heatmap [Exercise] | 00:00:00 | ||
| Create Horizontal Altair Bar Plot [Exercise] | 00:00:00 | ||
| Set Theme | 00:00:00 | ||
| Build An Altair Grouped Bar Chart | 00:00:00 | ||
| Stack The Altair Bar Chart [Exercise] | 00:00:00 | ||
| Normalized Stacked Bar Chart [Exercise | 00:00:00 | ||
| Add Text To The Chart | 00:00:00 | ||
| Add Altair Boxplot | 00:00:00 | ||
| Create An Interactive Legend In Altair | 00:00:00 | ||
| Concatenate Charts | 00:00:00 | ||
| Create Dual Y Axis In Altair | 00:00:00 | ||
| Horizontal Concatenation [Exercise] | 00:00:00 | ||
| Vertical Concatenation [Exercise] | 00:00:00 | ||
| Interactive Chart | 00:00:00 | ||
| Section 17 - Streamlit Layout | |||
| Laying Out Your Application | 00:00:00 | ||
| Add Interactivity - Date Inputs | 00:00:00 | ||
| Add Interactivity - Input Box | 00:00:00 | ||
| Add Interactivity - Drop Down [Exercise] | 00:00:00 | ||
| Add Interactivity - Select Slider [Exercise] | 00:00:00 | ||
| Add Interactivity - Select Box | 00:00:00 | ||
| Add Interactivity - Button | 00:00:00 | ||
| Section 18 - Natural Language Processing | |||
| Section Introduction | 00:00:00 | ||
| Sentiment Analysis | 00:00:00 | ||
| Create A Question Answering App | 00:00:00 | ||
| Generate Text [Exercise] | 00:00:00 | ||
| Named Entity Recognition[Exercise] | 00:00:00 | ||
| Summarize Text | 00:00:00 | ||
| Translate Text [Exercise] | 00:00:00 | ||
| Section 19 - Deploy Streamlit Application | |||
| Section Introduction | 00:00:00 | ||
| Streamlit Sharing | 00:00:00 | ||
| Heroku | 00:00:00 | ||
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