Python for Data Science Bootcamp 2022: From Zero to Hero
Updated on
Course overview
- Provider
- Udemy
- Course type
- Paid course
- Level
- All Levels
- Duration
- 18 hours
- Lessons
- 124 lessons
- Certificate
- Available on completion
- Course author
- Frank Andrade
-
- Learn to use Pandas for Data Analysis
- Use SciKit-Learn for Machine Learning Tasks
- Learn Static and Interactive Visualization with Pandas
- NLP: Binary Text Classification
- Use Python for Data Science and Machine Learning
- Implement Machine Learning Algorithms
- Data Cleaning with Python
- Basic Web Scraping with Python
Description
Welcome to the Python for Data Science Bootcamp: From Zero to Hero. In this course, we're going to learn how to use Python for Data Science. In this practical course, we'll learn how to collect data, clean data, make visualizations and build a machine learning model using Python.
The main goal of this course is to take your programming and analytical skills to the next level to build your career in Data Science. To achieve this goal, we're going to solve hundreds of exercises and many cool projects that will help you put into practice all the programming concepts used in Data Science.
We'll learn the top Python Libraries used in Data Science such as Pandas, Numpy and Scikit Learn and we will use them to learn to solve tasks data scientists deal with on a daily basis (Data Cleaning, Data Visualization, Data Collection and Model Building)
This course covers 4 main sections.
1. Python for Data Science Crash Course: In the first section, we'll learn all the Python core concepts you need to know for Data Science. We'll learn how to use variables, lists, dictionaries and more.
2. Python for Data Analysis: We'll learn Python libraries used for data analysis such as Pandas and Numpy. Both are great tools for exploring and working with data. We'll use Pandas and Numpy to deal with data science tasks such as cleaning and preparing data.
3. Python for Data Visualization: In the third section, we'll learn how to make static and interactive visualizations with Pandas. Also, I'll show you some techniques to properly make data visualization.
4. Machine Learning with Python: In the fourth section, we'll learn scikit-learn by solving a text classification problem in Python. This is the most popular machine learning library in Python and we'll not only learn how to implement machine learning algorithms in Python but also we'll learn the core concepts behind the most common algorithms using practical examples.
Bonus (Basic Web Scraping with Python): Remember that at the end of this course, there's a bonus section where you will learn web scraping. Web scraping allows us to build our own dataset by extracting data from websites. This is a must-have skill for data scientists and we'll learn this technique with the Beautiful Soup library.
What makes this course different from the others, and why you should enroll?
This is the most updated and complete Python course for data science.
Tired of ton of tutorials but no way to practice what you've learned? In this course, you will find lots of exercises to learn Python by solving problems.
This is the most project-based course you will find. We will solve 4 projects to put into practice all the concepts we will learn in this course
30 days money back guarantee by Udemy
After finishing this course, you will be able to do data analysis, create data visualization and build machine learning models with Python.
Join me now and go from zero coding skills to data scientist!
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