Data Science Projects: Data Analysis, ML, and Deployment (Udemy.com)
Hands-On Data Science Projects: Streamlit & FastAPI for Real-World ML Model Deployment and Visualization
Created by: Onur Baltacı
Last updated August 2024
What you will 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.
How it compares
Against the other 461 rated Udemy courses in Data Science on CourseDuck:
- Rating4.4 out of 5higher than 28% of them
- Reviews320#286 of 462 by number of reviews
- Length2 hoursshorter than 89% of them
- Last updatedAugust 2024earlier than 70% of them
Course 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!
Instructor Details
- 4.4 Rating
320 Reviews
Onur Baltacı
I work as a Senior Data Scientist, mostly focusing on LLMs, RAG pipelines, and building tools that automate real workflows. Alongside my day job, I've been creating data science content since 2023. I have published 8 courses on Udemy and run a YouTube channel with 370+ videos, reaching 75,000+ learners in total.
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Reviews
By Amine Sehaba on 2/23/2026
Straight to the point, and easy to follow ! Thanks for this great content!
By Deependu Ghosh on 3/10/2025
I could follow till the base coding part of the moel but the creation of ".py" part was new to me and needed more in-depth explanation or supporting videos on the same.
By kehinde Akhere sunday on 6/5/2024
it was extremely great and brief, can i get Data analyst for beginners pls?
By Franco Gabriel Iribarne on 4/5/2024
Everything is perfect. The explanation is very clear and it is so easy to follow.
By Abhijeet Singh on 4/4/2024
One of the best course in the free category. Everything is explained clearly like teaching to a new born baby. I'm sure paid course will be worth it. I highly recommend this course.
By akber khan on 3/21/2024
This course was so easy to understand and most key points were covered.
By Raviteja on 1/1/2024
It would be better if you have mentioned comment and more info about ML keywords
By Mukwani Tafara A on 11/17/2023
It was a good course and all but my jupyter notebook gave me a lot of errors despite doing and writing exactly what the code said as well as the tutor. Overall it was a good course. Thank you sir I learned a lot
By Yasmeen Toukhy on 9/13/2023
Everything is perfect but The only problem is that the code isn't clear enough and I have to zoom my web page in order to see the code which also causes me to write the code with errors since it isn't clear enough The best thing to solve this issue is to add a code file after each section in order to prevent this issue
By Ege Otenen on 7/11/2023
Kodlarin aciklamasi hic yok, sadece yazip geciyor. Temiz bir kod sagliyor olmasi guzel ama nedeninii nasilini biraz daha ogrenebilmeyi beklerdim.
Quality Score
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Overall Score : 88 / 100






