This is the code repository for Python Data Science Essentials [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
The Python Data Science Essentials video series takes you through all you need to know to succeed in data science using Python. Get insights into the core of Python data, including the latest versions of Jupyter Notebook, NumPy, Pandas and scikit-learn. In this course, you will delve into building your essential Python 3.6 data science toolbox, using a single-source approach that will allow to work with Python 2.7 as well with some basic changes. Get to grips fast with data munging and preprocessing, and prepare for machine learning and visualization techniques.
- Set up your data science toolbox using a Python scientific environment
- Get data ready for your data science project
- Manipulate, fix, and explore data in order to solve data science problems
- Set up an experimental pipeline to test your data science hypotheses
- Choose the most effective and scalable learning algorithm for your data science tasks
- Optimize your machine learning models to get the best performance
To fully benefit from the coverage included in this course, you will need:
Working knowledge of data analysis and Python
This course has the following software requirements:
Python 3.6
JupyterLab latest version
Anaconda 4.x
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