IBM Data science

Description

This educational program provides a comprehensive foundation in data science, equipping participants with the technical and analytical skills required to extract insights from large datasets. The curriculum focuses on key data science concepts, tools, and methodologies to prepare students for roles as data analysts, data scientists, and machine learning engineers. The program begins with What is Data Science?, introducing students to the role of data science in business and technology. This module establishes the foundational concepts, industry use cases, and career paths in data science. Students then move on to Python for Data Science, AI & Development, where they learn the basics of Python programming. This course focuses on essential Python concepts, including data structures, loops, and functions, which serve as a foundation for more advanced data science skills. The next module, Tools for Data Science, introduces students to the key tools used by data scientists, such as Jupyter Notebooks, GitHub, RStudio, and SQL interfaces. This module ensures that students have the technical proficiency to work in modern data science environments. Data Science Methodology focuses on the lifecycle of a data science project, from data collection and preparation to model deployment. Students learn best practices for handling data, defining project goals, and selecting appropriate models for analysis. Python Project for Data Science allows students to put their Python skills to practical use. This project-based module reinforces Python programming concepts and challenges students to solve real-world data problems. In Databases and SQL for Data Science with Python, students learn how to query, manipulate, and analyze data stored in relational databases. Students use SQL to extract insights from large datasets and understand how to integrate SQL with Python scripts for advanced data analysis. Data Analysis with Python builds on earlier Python knowledge to focus on analytical techniques like descriptive statistics, data visualization, and exploratory data analysis (EDA). Students work with libraries like Pandas, NumPy, and Matplotlib to visualize trends and patterns in data. Data Visualization with Python focuses on storytelling with data. Students learn how to create compelling data visualizations using Python libraries like Matplotlib and Seaborn. They design clear, impactful charts and dashboards to support business decision-making. The Machine Learning with Python module introduces students to machine learning concepts, including supervised and unsupervised learning, classification, regression, and clustering. Students work with machine learning libraries like Scikit-learn to train predictive models and evaluate their accuracy. Finally, students complete the Applied Data Science Capstone, a hands-on project where they apply all their learned skills. In this capstone, students work on a complete data science project, from data acquisition and cleaning to analysis and presentation. This project simulates a real-world data science challenge and demonstrates their ability to create actionable insights from data. By the end of the program, participants are equipped with the technical skills, analytical mindset, and project experience necessary for roles in data science, machine learning, and artificial intelligence.