C / C++ & systems
Make sense of pointers, memory, compiler errors, and the behavior of your code.
Level & focusIntroductory programming through advanced university systems courses
Topics & lesson help
Topics covered
- Pointers & references
- Stack & heap
- Memory management
- Classes & inheritance
- Templates & STL
- GDB & Valgrind
- Operating systems
- Threads & synchronization
How we can work on it
Bring a lab or a reproducible bug. Trace the program and memory, review your approach, and test the fix with edge cases.
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Algorithms & data structures
Choose an approach, explain why it works, and analyze its cost.
Level & focusCore CS through upper-division algorithms and technical interviews
Topics & lesson help
Topics covered
- Arrays, linked lists, stacks & queues
- Recursion, induction & correctness proofs
- Big O, Theta, recurrences & runtime analysis
- Sorting: bubble, insertion, merge, quick & heap sort
- Counting, bucket & radix sort
- Heaps, priority queues & balanced search trees
- Hash tables, collisions & load factors
- Union–Find, tries & spatial data structures
- String search: KMP & Boyer–Moore; Huffman coding
- Graphs: BFS, DFS & topological sorting
- Shortest paths: Dijkstra & Bellman–Ford
- Minimum spanning trees: Prim & Kruskal
- Greedy algorithms & dynamic programming
- Automata & complexity
How we can work on it
Trace a small example, implement the approach, then justify its correctness and runtime. Build a plan that connects proof methods and prerequisite mathematics with the data structures you are using.
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Python
Follow the data, understand the error, and build a program you can explain.
Level & focusIntroductory programming, university coursework, and applied projects
Topics & lesson help
Topics covered
- Python fundamentals & debugging
- Functions, data structures & program flow
- APIs, JSON & working with structured data
- Data handling & practical scripts
- Testing & interpreting errors
How we can work on it
Build a small script around one clear objective. Trace the data, debug the result, and repeat with a new example. Work with sample data for research, business tasks, or a new technical direction.
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R & RStudio
Understand your R code, work with data, and explain the analysis.
Level & focusIntroductory R, university coursework, and applied data analysis
Topics & lesson help
Topics covered
- R / RStudio & ggplot2
- Data analysis
- Debugging & testing
How we can work on it
Bring your R script, the analysis instructions, and the point where the output stops making sense. Review the code and interpret the result.
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SQL
Understand how your query transforms the data and why the result is wrong.
Level & focusIntroductory SQL, university coursework, and applied queries
Topics & lesson help
Topics covered
- SQL joins & queries
- Data analysis
- Debugging & testing
How we can work on it
Use sample tables to trace joins, filtering, and aggregation. Compare the output with the question you want to answer, whether for a class, a research project, or a business task.
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Swift & SwiftUI
Connect Swift syntax and application state to what your program does.
Level & focusIntroductory Swift, university coursework, and application projects
Topics & lesson help
Topics covered
- Swift & SwiftUI
- Object-oriented programming
- Debugging & testing
How we can work on it
Bring your assignment or a focused application problem. Discuss the structure, trace the behavior, and identify the next development step.
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Bash & shell scripting
Build and debug scripts that carry out a clear sequence of tasks.
Level & focusIntroductory scripting, university coursework, and automation projects
Topics & lesson help
Topics covered
- Bash scripting
- Debugging & testing
How we can work on it
Bring the script, its inputs, and the intended output. Trace commands and control flow, then check expected and unexpected inputs.
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Data science & machine learning
Understand the mathematics and code behind an analysis or learning method.
Level & focusUniversity and graduate coursework; confirm your current syllabus
Topics & lesson help
Topics covered
- Statistics for computing and data science
- Statistical machine learning
- Natural language processing
- Interpreting models and evaluating results
How we can work on it
Share a non-sensitive course or project outline. We can review the mathematics, trace a focused code example, and identify the prerequisite concepts to revisit.
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