IBM Data Science Professional Certificate

This repository serves as a portfolio of data science projects that I used to complete the IBM Data Science Professional Certificate offered by Coursera.

Courses 📔

The program consists of 9 online courses and 1 capstone project that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.

✅ Course 1: What is Data Science?

Coursera Certificate
Digital Badge

This course served as a brief introduction of the tools, history, and careers in data science. The lectures provided advise on how to move forward in a data-driven world, as well as the next steps for data scientists as we progress further not just in technology but also society as a whole.

✅ Course 2: Tools for Data Science

Coursera Certificate
Digital Badge

In this course, I learned about both commercial and open-source tools for data science that are available for each stage of development such as Data Management, Data Integration and Transformation, Data Visualization, Model Building, Model Deployment, Model Assessment, and many more. I utilized IBM's Watson Studio, Jupyter Notebooks, and RStudio to complete this part of the program.

⬜️ Course 3: Data Science Methodology

✅ Course 4: Python for Data Science, AI, and Development

Coursera Certificate
Digital Badge

I already had prior experience in Python before I took this course, so I treated it more as a review. In this course, the curriculum includes Python fundamentals, data science libraries, and an introduction to APIs.

✅ Course 5: Python Project for Data Science

Coursera Certificate
Digital Badge

Project Overview

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.

Topics Covered: Python, Webscraping, pandas, yfinance, IBM Cloud, Dashboard Analytics, Watson Studio

⬜️ Course 6: Databases and SQL for Data Science with Python

⬜️ Course 7: Data Analysis with Python

⬜️ Course 8: Data Visualization with Python

⬜️ Course 9: Machine Learning with Python

⬜️ Applied Data Science Capstone