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Learning Python for Data Science

Gain an in-depth understanding of data analysis with various Python packages

Description

Python is an open source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity, data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to deploy its features for data science applications.

In this course, you will learn all the necessary libraries that make data analytics with Python rewarding and effective. You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the NumPy library used for numerical and scientific computation. You will employ useful libraries for visualization (Matplotlib and Seaborn) to provide insights into data. Further, you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, to enable you to utilize your learning within your own projects.

By the end of this course, you'll have progressed through a journey from data cleaning and preparation to creating summary tables, and from visualization to machine learning and prediction. This video course will prepare you to enter the world of data science. Welcome to our journey!

This course uses Python 3.6, while not the latest version available, it provides relevant and informative content for legacy users of Python. 

About the Author

Ilyas Ustun is a data  scientist. He is passionate about creating data-driven analytical  solutions that are of outstanding merit. Visualization is his favorite.  After all, a picture is worth a thousand words. He has over 5 years of  data analytics experience in various fields like transportation, vehicle  re-identification, smartphone sensors, motion detection, and digital  agriculture. His Ph.D. dissertation focused on developing robust machine  learning models in detecting vehicle motion from smartphone  accelerometer data (without using GPS).

In his spare time, he loves to swim and enjoy the nature. He loves  gardening and his dream is to have a house with a small garden so he can  fill it in with all kind of flowers.

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