Practical Data Science with Python (Learning Path)
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Difficulty: Advanced | Genre: eLearning | Language: English | Duration: 23 Lectures (4h) | Size: 2.43 GB

Description
This learning path providing two learning experiences in one: it explores the world of data science while at the same time giving you hands-on, practical tutorials on how to use Python at an advanced level.

You will learn what data science is and the methods used for data analysis and statistical inference. A large number of guided walkthroughs show you how to use Python and its features. You will learn how to set up Anaconda and Jupyter Notebook and learn, using real-world examples, how to write Python code in Jupyter, with useful tips within the context of data science.

The learning path then moves on to explore a variety of Python features including loops, classes, variables, stringification, and dictionaries and how to create them - all of which is explained through the use of practical demonstrations.

You will also have the chance to put your Python programming skills into practice. This learning path includes a hands-on lab that gives you a guided tutorial in Python, as well as a lab playground for you to try out anything you feel like in Python. Finally, a lab challenge sets you a task that you will have to complete on your own and without any help - the ultimate test of your programming abilities!

Learning Objectives
Understand the fundamentals of data science
Enhance your programming knowledge with Python
Know how to analyze data through summary statistics
Use a range of Python features for numerical analysis
Explore and visualize data using Python

Intended Audience
People starting their journey in the world of data science
IT professionals wishing to learn about data analysis and data science
IT professionals wanting to advance their Python programming skills

Prerequisites
To get the most from this learning path, you should already be familiar and comfortable with logical and mathematical thinking, as well as having existing knowledge of programming (variables, scope, functions).

Screenshots

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