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1. P_01: Introduction to Python Programming | Python for BeginnersJenny's Lectures CS IT

Introduction to Python Programming Language

Python is currently the most popular programming language and also the fastest growing one. The concept of Python was introduced in 1989 and it is being used widely in machine learning, data science, and artificial intelligence.

Main Areas of PythonMachine learningData scienceArtificial intelligence

Python is used by big companies like Facebook and Twitter for managing and processing large amounts of data.

The language is easy to learn and has a wide range of applications. It supports multiple paradigms including object-oriented, procedural, and functional programming.

Python has been used in almost every area of programming, including by big companies like Google and YouTube.

The language is open source and has a huge community with many inbuilt packages, modules, and functions that can be used in programs without having to write them from scratch.

Python has many discussion forums where users can post queries and get answers. Frameworks like Django and Flask are available for web development.

Career Opportunities in Python

Python offers numerous career opportunities with an average salary ranging from 8 to 12 per nm. It's no wonder why this language is so popular. In the upcoming video, we will first explore the history of Python. As the saying goes, "know your enemy before going to battle." We will be using the latest version of Python 3.x, as Python 2.x is now obsolete and no longer supported since January 2020. So, let's focus on the latest version and explore the many career opportunities that Python has to offer.

Features and Application Areas of Python

If you are already familiar with Python and its features and application areas, please feel free to take notes. In the next video, we will dive deeper into the topic.

See you in the next video!

Bye for now!

The Lyft rates from the Downtown UCF/Valencia Campus (500 W Livingston St) to Cocoa Beach on Tuesday afternoon at 3:00 pm are seen in the screenshot below. It's a roughly 61-mile journey. How does the predicted cost using your linear regression equation from section 2b compare to the usual Lyft prices? Does the trip's real cost match or exceed the estimate? Why are the costs not precisely the same, in your opinion?

Here is the data 

Current location → Cocoa Beach

(Nearby drivers are busy)

RECOMMENDED

Lyft for 4 people -with the standard options you Get there by 4:23 PM

Option 1 (Selected one)Standard price price $130.99(limited availability)

Standard price other options: 

-wait & save (limited availability) Price $99.99

-pick up in 45-60 min (save when you wait longer) price is $70.82. 

Preferred price 

For 4 people the price is $150.96 and it arrives at 4:20 pm 

The last option is Lyft Xl for 6 people and its price is $143.13 it arrives at 4:17 pm 

Here is the linear regression

y=4.2663x+2.3664

r^2=0.7965

r- n8025

and here is the scatter plot that The data above was transferred to it 

What is the role of fungus in the food web shown?

◯ A. To gather green light in order to carry out photosynthesis.

◯ B. To break down organic matter into carbon dioxide and water.

◯ C. To convert light energy into chemical energy organisms can use.

◯ D. To break down food molecules in the absence of oxygen.

What roles did Assunta Gusapari play in the music of heart?