What experience level is required for UCSanDiegoX: Data Structures: An Active Learning Approach?

This course is aimed at the Intermediate level. Having some basic understanding of the topic will help you get the most out of the curriculum.

How long does it take to complete UCSanDiegoX: Data Structures: An Active Learning Approach?

The total duration of the course is approximately 10 weeks. Since it is generally self-paced, completion time depends entirely on your learning schedule and availability.

Is this course more theory-focused or project-focused?

This course provides a strong mix of conceptual theory and practical application to ensure a comprehensive understanding of the subject matter.

Do I get a certificate after completing UCSanDiegoX: Data Structures: An Active Learning Approach?

Yes, a certificate of completion is included, which you can add to your professional profile or resume to showcase your newly acquired skills.

What careers is this course relevant for?

The skills taught in this course are highly relevant for professionals pursuing roles within Computer Science and adjacent technical fields looking to upskill.

Computer Science

UCSanDiegoX: Data Structures: An Active Learning Approach

Name: UCSanDiegoX: Data Structures: An Active Learning Approach
Price: 4150 INR
Availability: InStock

Learn about high-performance data structures and supporting algorithms, as well as the fundamentals of theoretical time complexity analysis through an interactive online text.

₹4150

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Course Insight

Suitable for intermediate learners. Works well as a continuation after mastering Computer Science fundamentals. It bridges the gap toward advanced, production-level engineering.

Intermediate FriendlyCertification IncludedSelf-Paced Learning

SKILLS TO
MASTER

Computer Science Basics

Fundamental principles and concepts

Practical Application

Real-world project implementation

Best Practices

Industry standard workflows and guidelines

Problem Solving

Core Concepts

Implementation

Workflow Integration

Optimization

Careers:Data Scientist, Data Analyst, Machine Learning Engineer.

Quick Facts

10 weeks

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What You’ll Learn

This interactive text used in this course was written with the intention of teaching Computer Science students about various data structures as well as the applications in which each data structure would be appropriate to use. It is currently beingtaught at the University of California, San Diego (UCSD), the University of San Diego (USD), and the University of Puerto Rico (UPR).

Thiscoursework utilizes the Active Learning approach to instruction, meaning it has various activities embedded throughout to help stimulate your learning and improve your understanding of the materials we will cover. You will encounter "STOP and Think" questions that will help you reflect on the material, "Exercise Breaks" that will test your knowledge and understanding of the concepts discussed, and "Code Challenges" that will allow you to actually implement some of the algorithms we will cover.

Currently, all code challenges are in C++ or Python, but the vast majority of the content is language-agnostic theory of complexity and algorithm analysis. In other words, even without C++ or Python knowledge, the key takeaways can still be obtained.

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Outcomes

The algorithms behind fundamental data structures (dynamic arrays, linked structures, (un)balanced trees/tries, graph algorithms, hash tables/functions).
How to reason about appropriate data structures to solve problems, including their strengths and weaknesses.
How to analyze algorithms theoretically (worst-case, average-case, and amortized).
The key distinctions and relations between "Abstract Data Types" and "Data Structures".
Basic information theory and data compression utilizing the data structures covered.

See side-by-side differences in learning outcomes