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Data Science Projects: Data Analysis, ML, and Deployment
Rating: 4.4 out of 5(319 ratings)
15,078 students

Data Science Projects: Data Analysis, ML, and Deployment

Hands-On Data Science Projects: Streamlit & FastAPI for Real-World ML Model Deployment and Visualization
Created byOnur Baltacı
Last updated 8/2024
English

What you'll learn

  • Data Analysis and Visualization: Learn to analyze data and create visualizations with Pandas and Matplotlib
  • Machine Learning Fundamentals: Build, train, and evaluate ML models, mastering algorithms and their real-world applications to solve data problems.
  • Feature Engineering: Learn feature engineering to create impactful features, improving model performance and prediction accuracy effectively.
  • Streamlit for Interactive Apps: Develop interactive web apps with Streamlit to deploy and visualize ML models, offering a user-friendly experience.
  • FastAPI for Model Deployment: Create scalable APIs with FastAPI to deploy ML models, enabling real-time predictions and seamless integration.
  • End-to-End Project Workflow: Experience the full data science lifecycle from data cleaning and exploration to model deployment with hands-on projects.

Explore related topics

Course content

4 sections • 8 lectures • 2h 0m total length

  • Dataset0:03
  • Salary Prediction Project Part 111:51
  • Salary Prediction Project Part 223:39

Requirements

  • Basic Python Knowledge
  • Understanding of Basic Data Science Fundamentals

Description

Welcome to Data Science Projects - Hands-On Projects with Streamlit and FastAPI! This course is designed for aspiring data scientists who want to elevate their skills through practical, real-world projects. Dive into the exciting world of data science with three comprehensive projects that cover every essential aspect: data analysis, data cleaning, data visualization, feature engineering, and machine learning.

In this course, you will:

  • Analyze and Clean Data: Learn techniques for effective data cleaning and exploratory data analysis to uncover insights.

  • Visualize Data: Create impactful visualizations to tell compelling data stories.

  • Engineer Features: Understand feature engineering principles to enhance your models' performance.

  • Build and Evaluate ML Models: Develop, train, and evaluate machine learning models using popular algorithms.

Additionally, you will gain hands-on experience with:

  • Streamlit: Build interactive web applications to showcase your machine learning models in two projects.

  • FastAPI: Create a robust and scalable API for deploying your machine learning model in one project.

By the end of this course, you will have built three portfolio-worthy projects, demonstrating your ability to handle end-to-end data science processes and deploy machine learning models in real-time applications. Whether you are a beginner or an intermediate data scientist, this course will provide you with the practical skills needed to excel in the data science field. Join us and transform your data science expertise today!

Who this course is for:

  • Aspiring Data Scientists
  • Intermediate Data Analysts
  • Machine Learning Enthusiasts
  • Python Developers
  • Students in Data Science

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