Introduction to Data Structures

Welcome to the course

You already know basic Python loops and containers. This lesson turns that knowledge into an interview skill: choosing the right way to organize data.

In finance-style interview problems, the story might be about prices, trades, orders, symbols, or schedules. The hidden question is often:

What structure makes the important operation simple?

This first lesson gives you a mental map. Later lessons will zoom into specific structures and analyze their tradeoffs more formally.

A data structure is a decision

A data structure is a way to store data so certain actions become easier.

Common actions include:

• Remember order: What happened first, second, third?
• Find quickly: Have we seen this symbol before?
• Connect keys to values: What is the current position for AAPL?
• Group related items: Which trades belong to each account?
• Process in a rule-based order: What should be handled next?

In interviews, confusion often starts when you think, “I know Python, but which container do I use?” This course is about building that instinct.

Lists and sets answer different questions

Suppose you receive a stream of ticker symbols.

A list is useful when the order matters.

A set is useful when you care about uniqueness or asking “have I seen this before?”

Same raw data. Different structure. Different strength.

```python
watchlist = ["AAPL", "MSFT", "AAPL", "NVDA", "MSFT"]

symbols_in_order = []
unique_symbols = set()

for symbol in watchlist:
    symbols_in_order.append(symbol)
    unique_symbols.add(symbol)

print("Original order:", symbols_in_order)
print("Unique symbols:", sorted(unique_symbols))
print("Have we seen AAPL?", "AAPL" in unique_symbols)
```

You are tracking which trader IDs are currently active. You do not need duplicates, and you frequently ask: “Is this trader active right now?”

Which Python container is the best first choice?

Look for the shape

Before coding, ask what shape the data has.

Sequence:       trade1 -> trade2 -> trade3
Key-value:      "AAPL" -> 150 shares
Hierarchy:      portfolio -> sector -> stock
Network:        account A <-> account B <-> account C

The shape hints at the structure.

For now, you do not need to master every structure. You need the habit of asking:

What relationships does my data naturally have?

Upcoming lessons will focus on these shapes one at a time.

True or false: In an interview, you should choose a data structure only because it makes the code look short.

A finance-flavored example

Imagine each trade updates your position in a symbol.

Raw trades arrive as a sequence. But the question “what is my current position per symbol?” is a key-value question.

That points us toward a dict: symbol as the key, current position as the value.

```python
trades = [
    ("AAPL", 10),
    ("MSFT", 5),
    ("AAPL", -3),
    ("NVDA", 8),
]

positions = {}

for symbol, quantity in trades:
    positions[symbol] = positions.get(symbol, 0) + quantity

print("AAPL position:", positions["AAPL"])
print("All positions:", positions)
```

In Python, the container commonly used to map a key like a ticker symbol to a value like its current position is called a ____.

A simple loop for problem solving

When you see a new interview problem, pause before coding.

Use this loop:

1. Identify the data: What are the items? Trades? Prices? Characters? Nodes?
2. Identify the operations: Search? Count? Group? Update? Remove? Process next?
3. Choose a structure: Which container makes those operations natural?
4. Write clear code: Keep the structure visible in your solution.
5. Explain the tradeoff: Say why you chose it.

Interviewers care about your reasoning, not just the final answer.

Which mindset is most useful when learning data structures for interviews?

You now have the map

Data structures are not random containers. They are tools for making certain operations easier to express and reason about.

Today’s key idea:

First understand the job your data must do. Then choose the structure.

Next, we will learn how to describe efficiency using time and space complexity. That will give you a more precise way to compare choices in interviews.