Harvard University · edX
Data Science: R Basics
4.4273 ratingsBuild a foundation in R and learn how to wrangle, analyze, and visualize data.
Overview
Prepare for technical interviews focused on software design, architecture, performance, security, networking, and development practices. Practice explaining core concepts and making tradeoffs in realistic interview scenarios.
What you'll learn
- Explain system performance concepts such as load balancing, monitoring, optimization, and high availability
- Discuss security practices and modern threats in the context of software systems
- Compare network protocols, database types, concurrency approaches, and data integrity strategies
- Evaluate cloud computing, containerization, infrastructure, scalability, and architecture patterns
- Apply software design principles, design patterns, Agile methods, CI/CD, and version control concepts
- Practice structured responses to technical interview prompts involving tradeoffs and implementation decisions
Skills you'll practise
Advantages and disadvantages
Advantages
- University courses you can audit for free, with lectures, readings and practice quizzes.
- A verified certificate from the university if you pay for it.
- Self-paced: start any time.
Disadvantages
- Graded assignments and the certificate need the paid track.
- Audit access can expire a few weeks after the course ends.
- Learning is free, but the certificate costs money.
- Some parts (graded work, certificate) are paid.
Some points apply to every course of this kind; see how we rank.
Free to audit
- Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
- Paid: Graded assignments and the verified certificate (Certificate $55). Audit access may end after the course closes.
Before you start
Beginner: Expanding Breadth of Learning Intermediate: Verbal Communication
Similar courses
Harvard University · edX
CS50's Web Programming with Python and JavaScript
This course picks up where CS50 leaves off, diving more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.
Harvard University · edX
Principles, Statistical and Computational Tools for Reproducible Data Science
4.314 ratingsLearn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.
Harvard University · edX
Data Science: Productivity Tools
4.345 ratingsKeep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.
ETH Zurich · edX
Agile Software Development
Learn fundamental agile concepts to enhance your software development skills.
