Introduction to Applied Data Science with Python

You will gain a foundational understanding of data science, its key concepts including AI and ML, and the role of Python in data science. You'll also learn to write simple Python scripts and understand basic programming concepts​​.

You will learn techniques for data manipulation, cleaning, and preparation. This includes understanding the importance of data quality and how to perform data analysis using Python's powerful libraries, Pandas and NumPy​​.

You will understand the importance and principles of effective data visualizations and how they can drive business value. You'll gain hands-on experience in creating visualizations, customizing plots, and generating insights from visual data using Matplotlib and Seaborn libraries

You will demystify the world of machine learning, learning about its lifecycle, various types (supervised, unsupervised, reinforcement learning), and key concepts necessary for developing machine learning models​​.

You will apply your learning to build and evaluate a basic machine learning model using the Scikit-Learn library. This includes understanding model selection, overfitting, and applying linear regression on a dataset​​.

You will understand the capability of ChatGPT and how to effectively use it for simple data science queries. This includes best practices in prompt engineering and responsible use of AI.

You will have the opportunity to apply all the skills learned throughout the course in a Capstone Project. This project will allow you to demonstrate your ability to analyze, visualize, and model data to predict outcomes, simulating real-world data science tasks

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