Table of Contents
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.
Description
Python for Data Science with Assignments
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