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Data Science

Foundations of Data Science

Cover the fundamental concepts, terminology, and principles of data science. Explore the data science workflow, including data collection, cleaning, analysis, and visualization.

Statistical Analysis and ML

Introduce statistical methods and machine learning algorithms. Cover regression, classification, clustering, and model evaluation. Explore practical applications in real-world datasets.

Programming for Data Science

Teach essential programming languages like Python and R for data manipulation and analysis. Focus on libraries and frameworks commonly used in data science, such as NumPy, pandas, and scikit-learn.

Data Visualization and Storytelling

Teach data visualization techniques using tools like Matplotlib, Seaborn, and Tableau. Emphasize the importance of conveying insights through compelling data stories.

Data Manipulation and Cleaning

Dive into data preprocessing techniques, including data cleaning, handling missing values, and transforming data for analysis. Emphasize data quality and integrity

Big Data and Advanced Topics

Explore big data technologies like Hadoop and Spark. Discuss advanced topics such as natural language processing (NLP), deep learning, and time series analysis. Prepare students to work with large-scale datasets and complex data science projects.

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