Python for Data Science with Assignments

Python for Data Science with Assignments
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

A Comprehensive and Practical Hands-On Guide to Learning Python for Beginners, Aspiring Developers, Self-Learners, etc.

What you’ll learn
Real-world use cases of Python and its versatility.
Installation of Python on both Mac and Windows operating systems.
Handling data using essential data structures like lists, tuples, sets, and dictionaries.
Utilizing functions and working with parameters and arguments.
Employing filter, map, and zip functions for data processing.
Exploring analytical and aggregate functions for data analysis.
Using built-in functions for regular expressions and handling special characters and sets.
Iterating over elements using for loops and while loops.
Understanding the object-oriented programming (OOP) concepts and principles.
Working with date and time classes, including TimeDelta for time manipulation.
How to use statistics for effective data analysis and decision-making.
Introduction to Python for statistical analysis, including data manipulation and visualization.
Different types of data and their significance in statistical analysis.
Measures of central tendency, spread, dependence, shape, and position.
How to calculate and interpret standard scores and probabilities.
Key concepts in probability theory, set theory, and conditional probability.
Understanding Bayes’ Theorem and its applications.
Permutations, combinations, and their role in solving real-world problems.
Requirements
Students should have a general understanding of how to operate a computer.
Be comfortable with common tasks like file management and using a web browser.
No Prior Programming Experience Required.
A positive attitude and a willingness to learn and explore new concepts are essential for getting the most out of this course.
A basic understanding of mathematics, including algebra and arithmetic.
Familiarity with fundamental concepts in data analysis and problem-solving.
A willingness to learn and engage with statistical concepts and Python programming.
Basic knowledge of Python is a plus but not mandatory, as introductory Python concepts will be covered in the course.

Description
Who this course is for:
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