For this project, you will assume the role of a Data Scientist / Data Analyst working for a new startup investment firm that helps customers invest their money in stocks. Your job is to extract financial data like historical share price and quarterly revenue reportings from various sources using Python libraries and webscraping on popular stocks. After collecting this data you will visualize it in a dashboard to identify patterns or trends. The stocks we will work with are Tesla, Amazon, AMD, and GameStop. Dashboard Analytics Displayed A dashboard often provides a view of key performance indicators in a clear way. Analyzing a data set and extracting key performance indicators will be practiced. Prompts will be used to support learning in accessing and displaying data in dashboards. Learning how to display key performance indicators on a dashboard will be included in this assignment. We will be using Plotly in this course for data visualization and is not a requirement to take this course. Watson Studio In the Python for Data Science, AI and Development course you utilized Skills Network Labs for hands-on labs. For this project you will use Skills Network Labs and Watson Studio. Skills Network Labs is a sandbox environment for learning and completing labs in courses. Whereas Watson Studio, a component of IBM Cloud Pak for Data, is a suite of tools and a collaborative environment for data scientists, data analysts, AI and machine learning engineers and domain experts to develop and deploy your projects. Review criteria There are two hands-on labs on Extracting Stock Data and one assignment to complete. You will be judged by completing two quizzes and one peer review assignment. The quizzes will test you based on the output of the hands-on labs. In the peer review assignment you will share and take screen shots of the outcomes of your assignment.

The IBM Python for Data Science, AI & Development , this course provides a solid foundation in Python programming while emphasizing its applications in data analysis, AI model development, and software engineering.

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Python jupiter notebook files for the course - Python for data science, AI and development offered by IBM on Coursera

Python for Data Science, AI and Development - IBM course

Python basics and tools for Data Science, AI, and development — IBM course content.

Python for Data Science, AI and Development course offered by IBM on Coursera.

Documenting my progress in Python for Data Science, AI, and Development course by IBM

Exercises and projects from the course Python for Data Science, AI & Development (IBM/Coursera)

IBM Python for Data Science, AI and Development course completion, curated labs, and concise concept notes.

IBM Python for Data Science, AI and Development course completion, curated labs, and concise concept notes.

Contains the projects and lessons included in the IBM course of Python for Data Science, AI and Development

Comprehensive repository featuring course notes and hands-on lab exercises from IBM's Python for Data Science and AI Development course.

This repository contains materials from the IBM Data Science course offered by IBM on Coursera. This is the final assignment of the Python for Data Science and AI Development course

This repo contains hands on of the course Python for Data science, AI and Development from the IBM Data science Certificate on Coursera

Hands-on labs, practice notebooks, and solutions from IBM’s Python for Data Science, AI & Development course on Coursera.

Welcome to the repository for my comprehensive notes on Python Data Science, Artificial Intelligence (AI), and Development. These notes were created during my completion of the Python Data Science, AI, and Development course offered by IBM on Coursera.

this is a repository that houses my early learning in python through the "Python for Data Science, AI and Development" course offered by IBM through Coursera

In this repository, we are going to put into practice all the Python knowledge oriented to data science that we have learned from the course IBM Python for Data Science, AI and development.

This repository contains notes, code examples, and exercises from the Coursera course Python for Data Science, AI & Development offered by IBM. The course provides a comprehensive introduction to Python programming with a focus on applications in data science, artificial intelligence, and software development.

This is a repository containing hands-on labs and exercises from ( Python for Data Science, AI & Development ) course, a part of Coursera IBM Data Engineering Professional Certificate..

The GitHub repository showcases completion of IBM courses on AI and Python. Topics cover AI fundamentals, generative AI, chatbot development, Python for data science, and AI application with Watson APIs, detailing key lessons and practical projects.

This course is a part of IBM Generative AI engineer an other, where the purpose is to understand the basic to advanced of python programmation for AI, Data Science and Development.

A comparative analysis of historical stock prices and revenue data for Tesla and GameStop using web scraping and data visualization.This Analysis was performed by the guidance of Coursera course : Python for Data Science, AI & Development Made by IBM

Successfully completed Python for Data Science, AI & Development by IBM on Coursera. This course deepened my expertise in Python programming, covering data manipulation with Pandas, numerical computing with NumPy, and data visualization techniques. It also introduced foundational AI concepts and Jupyter Notebook for interactive computing. E

This project replicates the final assignment from IBM’s Python for Data Science, AI & Development course on Coursera. The goal is to extract, clean, and visualize real financial data from Yahoo Finance using yfinance, pandas, and matplotlib.

This jupyter noteboook was given as an assignment in the fifth course of the IBM's Python for Data Science, AI & Development Professional certification course. The assignment is as : Peer-graded Assignment: Analyzing Historical Stock/Revenue Data and Building a Dashboard