Basics of Data Science

Course Description

"Basics of Data Science" is a comprehensive and engaging intermediate-level course designed to provide students with a solid foundation in the exciting world of data science. This course covers a wide range of fundamental concepts, challenges, and tools essential for anyone looking to dive into the field of data science.

What Students Will Learn

• A comprehensive overview of data science concepts and techniques
• Understanding of data science's role in society and business
• Ability to conceptualize and perform basic data analysis
• Skills to evaluate and interpret data analysis outcomes
• Knowledge of responsible data science practices
• Understanding of machine learning, data mining, and AI limitations
• Proficiency in writing Python programs and using popular libraries
• Application of various data analysis techniques
• Handling of data quality issues and preprocessing
• Implementing data science techniques while maintaining confidentiality and fairness

Prerequisites

While this course is open to individuals from all disciplines with an interest in data science, prior knowledge in mathematics (mathematical notations, linear algebra, stochastics, and statistics) is advantageous but not mandatory.

Course Content

• Data science infrastructure and big data concepts
• Data exploration and visualization
• Supervised learning techniques (decision trees, regression, SVMs, neural networks)
• Unsupervised learning and clustering
• Pattern mining and process mining
• Text mining and natural language processing
• Data preprocessing and quality management
• Evaluation of machine learning models
• Ethical considerations in data science
• Responsible data science practices (fairness, accuracy, confidentiality, transparency)
• Hands-on exercises using Python and Jupyter notebooks

Who This Course Is For

• Students from various disciplines interested in data science
• Professionals looking to transition into data science roles
• Business analysts seeking to enhance their data analysis skills
• Researchers wanting to incorporate data science techniques into their work
• Anyone curious about the power and potential of data science in today's world

Real-World Applications

• Analyzing business data to make informed decisions
• Developing predictive models for various industries
• Improving process efficiency through data-driven insights
• Extracting valuable information from text data
• Implementing responsible data practices in organizations
• Enhancing product development through data analysis
• Conducting market research and consumer behavior analysis
• Optimizing operations and supply chain management
• Contributing to scientific research across various fields
• Developing data-driven solutions for societal challenges

Syllabus