Learning Python for Data Science
What you'll learn
- Explore hands-on data analysis and machine learning by coding in Python
- Become proficient in working with real-life data collected from different sources such as CSV files, websites, and databases
- Get hands on with the Numpy for numerical and scientific computation.
- Learn about pre-processing data to make it ready for data analysis
- Carry out visualization with the Matplotlib, and Seaborn libraries
- Understand exploratory data analysis, summarizing data, and creating statistics out of data with Pandas
- Implement Machine Learning algorithms and delve into various machine learning techniques, and their advantages and disadvantages
- Work with regression, classification, clustering, supervised and unsupervised machine learning, and much more!
Course content
- Preview02:09
- 02:28What Is Data Science?
- 02:25Python Data Science Ecosystem
Requirements
- Knowledge of intro-level programming topics such as variables, if-else constructs, for and while loops, and functions is recommended but not required.
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.
Who this course is for:
- This is an introductory-level course for aspiring data scientists who have a basic understanding of coding in Python and little to no knowledge of data analytics. If you already know Python, or another programming language and want to add Python to your skill set, then this course will also be useful.
Instructor
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- 3.9 Instructor Rating
- 59,187 Reviews
- 354,873 Students
- 1,418 Courses
Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.
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