Course Title: Build 12 Data Science Apps with Python and Streamlit – Full Course

Course Description:

This comprehensive course is designed to empower participants with the skills to develop 12 practical Data Science applications using Python and Streamlit. Through hands-on projects, participants will gain a deep understanding of data visualization, analysis, and application development. The course covers essential Python libraries for data manipulation and analysis, such as Pandas and NumPy, and introduces Streamlit as a powerful tool for creating interactive web applications.

Prerequisites:

• Basic knowledge of Python programming.
• Familiarity with fundamental concepts of data science.

Week 1-2: Introduction to Data Science and Python Basics

• Overview of Data Science and its applications.
• Setting up Python environment (Anaconda, Jupyter Notebooks).
• Python basics: variables, data types, control structures.
• Introduction to Pandas for data manipulation.

Week 3-4: Exploratory Data Analysis (EDA) with Pandas and NumPy

• Exploring datasets with Pandas.
• Data cleaning and preprocessing.
• Descriptive statistics and data visualization with Matplotlib and Seaborn.
• Advanced data manipulation with Pandas.

Week 5-6: Introduction to Streamlit

• Understanding Streamlit and its applications.
• Setting up Streamlit environment.
• Building a basic Streamlit app.

Week 7-8: Building Interactive Dashboards with Streamlit

• Creating interactive dashboards using Streamlit.
• Incorporating widgets for user interaction.
• Deploying Streamlit apps.

Week 9-12: Data Science Applications with Streamlit

Participants will work on the development of 12 different Data Science applications, including but not limited to:

1. Sentiment Analysis App
2. Stock Price Prediction App
3. Image Classification App
4. Customer Segmentation App
5. Recommender System App
6. Time Series Forecasting App
7. Fraud Detection App
8. Natural Language Processing (NLP) App
9. Geographic Information System (GIS) App
10. Social Media Analytics App
11. Data Visualization Dashboard
12. Custom Project (Participants’ choice)

Assessment and Evaluation:

• Weekly quizzes and assignments.
• Participation in class discussions and projects.
• Final project presentation and submission.

Resources:

• Online tutorials and documentation.
• Recommended readings and research papers.
• Community forums for discussion and problem-solving.

Grading:

• Weekly assignments: 40%
• Final project: 50%
• Participation: 10%

Note:

This course emphasizes practical application and problem-solving skills. Participants will not only gain theoretical knowledge but also hands-on experience in developing real-world Data Science applications using Python and Streamlit.

Please note that this is a sample syllabus, and you may need to adapt it based on the specific goals, duration, and level of your course.