Learn Python using Statistics: Data Analysis & Data Science

[RoShanXian]

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

This course includes:​

• 9.5 hours on-demand video
• 4 downloadable resources
• Access on mobile and TV
• Full lifetime access
• Certificate of completion

What you'll learn​

• Real-world use cases of Python and its versatility.
• Installation of Python on both Mac and Windows operating systems.
• Fundamentals of programming with Python, including variables and data types.
• Working with various operators in Python to perform operations.
• 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.
• Fundamental concepts and importance of statistics in various fields.
• 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.
• Practical knowledge of various statistical tests, including t-tests, chi-squared tests, and ANOVA, for hypothesis testing and inference.

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[🤖]

This course seems like a comprehensive and practical hands-on guide for beginners who want to learn Python. It offers 9.5 hours of on-demand video content, 4 downloadable resources, and access on mobile and TV. You also get full lifetime access to the course and a certificate of completion.

The course covers a wide range of topics related to Python and statistics. Here are some of the key things you'll learn:

• Real-world use cases of Python and its versatility.
• How to install Python on both Mac and Windows operating systems.
• Fundamentals of programming with Python, including variables and data types.
• Working with various operators in Python for performing operations.
• 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 object-oriented programming (OOP) concepts and principles.
• Working with date and time classes, including TimeDelta for time manipulation.

The course also introduces fundamental concepts and the importance of statistics in various fields. You'll learn how to use statistics for effective data analysis and decision-making. Additionally, you'll gain practical knowledge of Python for statistical analysis, including data manipulation and visualization.

Some of the statistical topics covered in the course include different types of data and their significance in statistical analysis, measures of central tendency, spread, dependence, shape, and position. You'll also learn how to calculate and interpret standard scores and probabilities, understand key concepts in probability theory, set theory, and conditional probability, and learn about Bayes' Theorem and its applications.

The course also covers permutations, combinations, and their role in solving real-world problems. Lastly, you'll gain practical knowledge of various statistical tests, including t-tests, chi-squared tests, and ANOVA, for hypothesis testing and inference.

If you're interested in learning Python and applying it to data analysis and data science, this course seems like a great choice. You can find more information and enroll in the course using the following You do not have permission to view the full content of this post. Log in or register now..

 

[Tnai]

thankyou po