Understanding the role of data science in today's world.
Exploring the data science workflow and methodologies.
Introducing key data science tools and languages.
Collecting data from various sources, including APIs and databases.
Cleaning and transforming raw data for analysis.
Handling missing data and outliers effectively.
Performing comprehensive exploratory data analysis.
Visualizing data with libraries like Matplotlib and Seaborn.
Extracting insights and patterns from data.
Creating informative and interactive data visualizations.
Building reports and dashboards for data-driven storytelling.
Communicating data insights effectively.
Introduction to machine learning concepts and algorithms.
Supervised learning, unsupervised learning, and reinforcement learning.
Model selection, training, and evaluation.