Course Introduction:

The Data Science track is designed to empower women to enter the dynamic field of data science. The program is structured around different modules that cover key concepts and skills needed to excel in the field. You will be introduced to data science, focusing on essential areas such as data analysis and visualization techniques, advanced plotting and dashboarding, exploratory data analysis, and statistical methods. You will also explore foundational mathematics for machine learning, key machine learning and data concepts, as well as both supervised and unsupervised machine learning algorithms. As the program progresses, you will gain hands-on experience with model deployment, and be introduced to cutting-edge areas such as computer vision and natural language processing. Throughout the program, you will work on practical exercises and projects, receiving guidance and mentorship to apply your knowledge in real-world contexts.

Learning Objectives:

• Comprehend the basic concepts and applications of data science.
• Recognize various career paths and roles within data science.
• Master the basics of Python syntax, data structures, and functions.
• Utilize Python libraries such as NumPy and pandas for data manipulation and analysis.
• Perform data analysis and derive insights from data.
• Understand and apply fundamental statistical concepts and techniques.
• Create and interpret various types of data visualizations using Matplotlib and Plotly.
• Develop interactive dashboards using Dash.
• Implement data cleaning, normalization, binning, and encoding techniques.
• Perform exploratory data analysis using descriptive statistics, group-by operations, and correlation analysis.
• Understand and implement key machine learning concepts and algorithms.
• Develop and evaluate supervised and unsupervised learning models.
• Implement advanced algorithms such as SVM, decision trees, random forests, and gradient boosting.
• Build and evaluate neural network models.