Introduction to key concepts and principles of data science, including data exploration, visualization, and statistical analysis.

Learn programming languages such as Python and R for data manipulation, analysis, and building machine learning models.

Master statistical techniques for modeling and analyzing data, covering regression, hypothesis testing, and probability distributions.

Explore machine learning algorithms and techniques for predictive modeling, classification, and clustering.

Develop skills in creating compelling and informative data visualizations using tools like Matplotlib, Seaborn, and Tableau.

Apply data science skills to real-world projects, solving complex problems and showcasing practical abilities to potential employers.

Build a professional portfolio featuring completed data science projects and demonstrating expertise in various data science domains.

Prepare for data science internships by honing practical skills and gaining insights into industry practices and expectations.