nsk6704/python_project_for_data_science

Final Project: Analyzing Stock Performance and Building a Dashboard

Python-Project-for-Data-Science---IBM-Data-Science

💼 Final Project : Analyzing Stock Performance and Building a Dashboard

📋 Project Overview

For this project, I 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. My 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.

In the Python for Data Science, AI and Development course you utilized Skills Network Labs for hands-on labs.

For this project I 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 my projects.

📈 Stock shares

A company's stock share is a piece of the company; more precisely:

A stock (also known as equity) is a security that represents the ownership of a fraction of a corporation. This entitles the owner of the stock to a proportion of the corporation's assets and profits equal to how much stock they own. Units of stock are called "shares." [1]

An investor can buy a stock and sell it later. If the stock price increases, the investor profits, If it decreases, the investor with incur a loss. Determining the stock price is complex; it depends on the number of outstanding shares, the size of the company's future profits, and much more. People trade stocks throughout the day. The stock ticker is a report of the price of a certain stock, updated continuously throughout the trading session by the various stock market exchanges. In this lab, you will use the y-finance API to obtain the stock ticker and extract information about the stock. You will then be asked questions about your results.

📄 Summary

This course is intended to demonstrate foundational Python skills for working with data. The completion of this course involves working on a hands-on project developing a dashboard using Python.

📑 Main Topics

• Web Scraping Lab

• Extracting Stock Data Using a Python Library

• Extracting Stock Data Using a Web Scraping

• Final Project - Extracting and Visualizing Stock Data

🔑 Key Skills Learned

• Data extraction using the yfinance library
• Webscraping using the BeautifulSoup library
• Data Analysis
• Dashboard creation

🛠️ Tools

The following tools were used to complete this certification:

(Python, Jupyter, GitHub, IBM Watson Studio, IBM Cloud Pak)

📖 Libraries

The following Python libraries were used throughout the certification:

🏆 Certificates

To verify the certificates, click the images to follow the links.

Python Project for Data Science Issued by Coursera Authorized by IBM This badge earner demonstrates foundational Python skills for working with data. This includes: understanding the role of a Data Scientist / Data Analyst; applying Python fundamentals, working with Python data structures, and working with data in Python; and how to build a dashboard using Python and popular Python libraries using Jupyter notebook. This badge earner demonstrates foundational Python skills for working with data. This includes: understanding the role of a Data Scientist / Data Analyst; applying Python fundamentals, working with Python data structures, and working with data in Python; and how to build a dashboard using Python and popular Python libraries using Jupyter notebook.