Week 1 check in

  Chapter 5: Operators Operators in Python are symbols used to perform various operations on variables and values. They can be categorized into several types: Arithmetic Operators: Addition (+): Adds two values. Subtraction (-): Subtracts the right operand from the left operand. Multiplication (*): Multiplies two values. Division (/): Divides the left operand by the right operand. Comparison Operators: Equal (==): Returns True if the two operands are equal. Not equal (!=): Returns True if the two operands are not equal. Greater than (>): Returns True if the left operand is greater than the right operand. Less than (<): Returns True if the left operand is less than the right operand. Greater than or equal to (>=): Returns True if the left operand is greater than or equal to the right operand. Less than or equal to (<=): Returns True if the left operand is less than or equal to the right operand. Logical Operators: and: Returns True if both operands are true. or: Returns Tru

  Chapter 1 - Getting Started with Python Python is a versatile and powerful programming language known for its readability and simplicity. Created by Guido van Rossum in the late 1980s, Python has since become widely adopted across various domains due to its extensive library ecosystem and active community support. Python finds applications in web development, data analysis, machine learning, automation, game development, desktop applications, networking, scientific computing, blockchain, and Internet of Things (IoT) projects. Its flexibility and ease of use make it an ideal choice for both beginners and experienced programmers. Google Colab provides a convenient platform for writing and executing Python code without the need for local installations. With features like code cells, text cells, and built-in library support, Colab offers an interactive environment for learning and experimenting with Python. Chapter 2 - Print Statement and Comments The print statement in Python is used to

  Chapter 5: Probability and Statistics for Data Science Probability and statistics serve as the backbone of data science, enabling data scientists to comprehend data, make predictions, and assess model performance. In this chapter, we delve into two pivotal areas: Descriptive Statistics: Descriptive statistics furnish a quantitative summary of datasets, facilitating comprehension and visualization. Key metrics include: Mean: Average value of a dataset. Median: Middle value in sorted data. Mode: Most frequent value in a dataset. Range: Difference between the highest and lowest values. Variance: Average squared difference from the mean. Standard Deviation: Square root of variance, indicating data dispersion. For instance, considering a group's heights, mean height gives the average, range signifies the tallest-shortest difference, and standard deviation reflects height dispersion. Python offers straightforward implementations: python import numpy as np heights = [ 167 , 1

  Chapter 3 of the book focuses on handling and cleaning data using Python libraries, primarily Pandas and NumPy. These libraries provide powerful tools for importing, manipulating, and cleaning data, which are essential steps in any data analysis project. Below is a summary and discussion of the key points covered in the chapter. Introduction to Pandas: Pandas is introduced as an open-source library that offers high-performance data structures and tools for data analysis in Python. It provides flexibility in handling large datasets and offers various data manipulation capabilities. Using Pandas: To use Pandas in Python code, the library is imported using the standard convention import pandas as pd . Key Data Structures in Pandas: Pandas has two primary data structures: Series and DataFrame. Series is a one-dimensional labeled array, while DataFrame is a two-dimensional labeled data structure resembling a table with columns of potentially different types. Importing Data with Pandas:

  Chapter 1 of this book provides an introduction to Python for Data Science. Python's popularity in this field is attributed to its readability, versatility, rich library ecosystem, and active community. The chapter begins by discussing the reasons why Python is preferred for data science, emphasizing its readability, versatility, rich library ecosystem, and active community support. It then delves into Python basics, covering syntax, variables, and data types. Python's syntax is described as clean and easy to understand, with indentation used to define code blocks. Variables are introduced as containers for storing values, and built-in data types such as integers, floats, strings, lists, and dictionaries are explained. The chapter also introduces Google Colab, a free cloud-based service for creating and sharing Jupyter Notebooks. It provides a step-by-step guide on setting up a Google Colab notebook, including creating a new notebook, writing Python code, and running simple e