What you'll learn:
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

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 areas 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.