Welcome to this lesson that aims to introduce Hash Tables and Hash Maps, a fundamental concept in data structures and algorithms. Hash tables, also known as Hash Maps in Python, are extremely useful constructs that can drastically reduce time complexity while solving certain types of algorithmic problems. They provide an efficient way to maintain a collection of key-value pairs and allow quick access, insertion, and removal operations, making them highly effective in situations where quick lookups are necessary.

In a Hash Map, data is stored based on a generated hash value from a unique key; that way, we can access data quickly by merely knowing the key. For example, if we have a list of integers and a target number, finding two numbers in the list that sum to the target using a brute force method would require us to compare each number with all the other numbers — a process that would have a quadratic time complexity. However, by using a hash map, we can bypass this by storing each number with its index as it arrives and simultaneously checking if the complement (target minus the current number) is already in the hash map. This method drastically reduces computational overhead, making it much faster.

Here is what the solution might look like:

Python
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1def two_sum(nums, target):
2    hash_map = {}
3    for i, num in enumerate(nums):
4        if target - num in hash_map:
5            return [hash_map[target - num], i]
6        hash_map[num] = i
7    return []
8
9# Test
10print(two_sum([2, 7, 11, 15], 9))  # Output: [0, 1]

Now that we've sketched a basic understanding of Hash Tables/Maps, in the upcoming exercises, we'll dive deeper into the topic. We will practice implementing Hash Maps/Tables in Python and solving complex problems faster with this highly efficient data structure. It's quite a powerful tool to have in your algorithmic toolkit, and mastering it will significantly improve your problem-solving skills.