The Principles of Data Science course is designed to equip students with an in-depth understanding of the core principles, methodologies, and practical applications of data science. This comprehensive course consists of five modules, each focusing on specific facets of data science, from foundational concepts to advanced tools and techniques. Students will explore the intricacies of data analysis, modeling, recommendation systems, anomaly detection, and the practical utilization of data science tools. Through a combination of theoretical knowledge and hands-on experience, this course empowers students to navigate complex data scenarios and make data-driven decisions effectively.
Course Objectives:
Upon successful completion of this course, students will:
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Comprehend Data Science Fundamentals: Develop a deep understanding of the fundamental concepts of data science, its significance, and its diverse applications in real-world scenarios.
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Master Data Analysis Techniques: Acquire proficiency in exploratory data analysis (EDA), statistical analysis, and data visualization techniques to uncover patterns and trends in data.
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Navigate the Data Science Process: Understand the systematic data science process, from problem formulation and data collection to model building, evaluation, and results communication.
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Apply Machine Learning Principles: Gain practical knowledge of machine learning algorithms and their applications for classification, regression, clustering, and recommendation systems.
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Harness Tools and Technologies: Explore a wide range of data science tools and technologies, including SAS, Apache Spark, RapidMiner, and TensorFlow, to manipulate, analyze, and visualize data effectively.
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Design Recommendation Systems: Create personalized recommendation engines and forecasting models for various domains, including e-commerce, entertainment, and finance.
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Detect Anomalies and Outliers: Develop expertise in anomaly detection techniques to identify outliers and irregularities in data, critical for fraud detection and quality control.
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Implement Ethical Data Practices: Understand the ethical considerations of data science, including privacy, bias, and responsible data handling.
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Tackle Real-World Challenges: Apply data science techniques to real-world challenges, such as social media sentiment analysis, sales forecasting, and health monitoring.
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Become Proficient Data Scientists: By the end of the course, students will be well-prepared to excel in data science roles across various industries, armed with the knowledge and skills to extract meaningful insights from complex datasets, make informed decisions, and drive innovation through data-driven approaches.
- Teacher: Dr Muthamizh Selvan A.