Data Science 101
This self-paced course offers an immersive introduction to data science, combining programming, data analysis, and network architecture skills tailored for beginners. Using carefully selected textbooks, participants will gain hands-on experience in C, Python, and R programming languages, build foundational skills in data manipulation and visualization, and explore basic principles of network architecture. The course guides learners through structured modules covering essential programming constructs, advanced data analysis techniques, and introductory network data collection and analysis methods. By the end of the course, students will have the confidence to analyze data, automate processes, and understand network data flows, equipping them with practical skills that are in high demand in the data science field.
Course Objectives:
- Master C Programming Basics - Start with foundational programming skills in C, covering data structures, memory management, and file handling for data science applications.
- Transition to Python for Data Analysis - Explore Python’s data analysis capabilities, focusing on data manipulation with libraries like Pandas, and data visualization techniques to uncover insights.
- Learn Data Analysis in R - Use R for statistical analysis and visualization, leveraging ggplot2 for effective data representation.
- Understand Network Architecture - Develop a beginner’s understanding of network essentials and data collection methods, with an emphasis on data flows in networked environments.
Course Structure:
- Duration: 6 weeks (recommended pace)
- Format: Self-paced, hands-on with assignments and projects
- Level: Beginner to Intermediate
- Outcome: Gain a broad skillset that combines programming, data science, and networking fundamentals, readying participants for a variety of roles in data-driven fields.
This course is ideal for anyone new to data science, aspiring to acquire practical skills that bridge programming and data analysis with an understanding of network architecture.