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import requests
import json
import os

api_key = os.environ["api_key"]
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

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1. Classes: Classes are blueprints for creating objects. They define the properties (attributes) and behaviors (methods) that objects of that class will have. You define a class using the class keyword.

class MyClass:
    # Constructor method
    def __init__(self, attribute1, attribute2):
        self.attribute1 = attribute1
        self.attribute2 = attribute2

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S3 Bucket Setup, IAM Role Creation, and Lambda Function Development

1. S3 Bucket Setup:

• Bucket Name: my-example-bucket
• Logging Configuration: Enabled logging to track access requests.

2. IAM Role Creation:

• Role Name: LambdaS3Role
• Permissions Attached:
  • AmazonS3FullAccess

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Data Processsing Trechniques

ETL or Extract, Transform, and Load processes are used for cases where flexibility, speed, and scalability of data are important. You will explore some key differences been similar processes, ETL and ELT, which include the place of transformation, flexibility, Big Data support, and time-to-insight. You will learn that there is an increasing demand for access to raw data that drives the evolution from ETL to ELT. Data extraction involves advanced technologies including database querying, web scraping, and APIs. You will also learn that data transformation is about formatting data to suit the application and that data is loaded in batches or streamed continuously.

Learning Objectives

• Describe what an ETL process is.
• Explain what data loading means.
• Describe the trending shift from ETL to ELT.
• Summarize data extraction techniques.
• Name data transformation techniques.

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import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt

# Set a random seed for reproducibility
np.random.seed(42)

# Simulating a normal distribution
mu = 0  # Mean
sigma = 1  # Standard Deviation

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Statistics plays a crucial role in data science and machine learning. It provides the foundational concepts and techniques for analyzing data, making predictions, and evaluating the performance of machine learning models. Here are some key statistical concepts and techniques that are important for data science and machine learning:

1. Descriptive Statistics:

   • Mean: The average of a set of data points.
   • Median: The middle value of a dataset when arranged in ascending order.
   • Mode: The most frequently occurring value in a dataset.
   • Variance: A measure of how data points deviate from the mean.
   • Standard Deviation: The square root of the variance, representing the spread of data.

2. Probability:

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import pandas as pd
import numpy as np

# Load the Titanic dataset
df = pd.read_csv('train.csv')

# Check for missing values
df.isnull().sum()

# Handle missing values in the 'Age' column

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