Computers And Technology College

Unic Research and Find out two ways each in which
Programming language and used Form
& Scientific Application
A
Buisness
8
Application

Answers

Answer 1

The Scientific Applications are :

Data AnalysisSimulation and Modeling

Business Applications:

Web DevelopmentData Management and Analysis

What is the Programming language?

Data Analysis: Python, R, and MATLAB are used in research for statistical analysis, visualization, and modeling. Scientists use programming languages for data processing, analysis, calculations, insights, simulations, and modeling.

Web Development: HTML, CSS, and JavaScript are used to create websites and web apps. HTML provides structure, CSS styles, and JavaScript adds interaction.

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Related Questions

Which of the following is a positional argument?
a) ABOVE
b) MIN
c) AVERAGE
d) MAX

Answers

D) MAX is a positional argument

Problem: Prime Number Generator

Write a Python function that generates prime numbers up to a given limit. The function should take a single parameter limit, which represents the maximum value of the prime numbers to be generated. The function should return a list of all prime numbers up to the limit.

A prime number is a positive integer greater than 1 that has no positive divisors other than 1 and itself. For example, the first few prime numbers are 2, 3, 5, 7, 11, and so on.

Your task is to implement the generate_primes(limit) function that returns a list of all prime numbers up to the given limit.

Example usage:
"""
primes = generate_primes(20)
print(primes)
"""

Output:
"""
[2, 3, 5, 7, 11, 13, 17, 19]
"""

Note:
You can assume that the limit parameter will be a positive integer greater than 1.
Your implementation should use an efficient algorithm to generate prime numbers.

Answers

Here's a possible implementation of the generate_primes(limit) function using the Sieve of Eratosthenes algorithm:

```python
def generate_primes(limit):
# Create a boolean array "is_prime[0..limit]" and initialize
# all entries as true. A value in is_prime[i] will finally be
# false if i is Not a prime, else true.
is_prime = [True] * (limit + 1)
# 0 and 1 are not prime
is_prime[0] = is_prime[1] = False

# Iterate over the numbers from 2 to sqrt(limit)
for i in range(2, int(limit**0.5) + 1):
if is_prime[i]:
# Mark all multiples of i as composite numbers
for j in range(i**2, limit + 1, i):
is_prime[j] = False

# Generate the list of prime numbers
primes = [i for i in range(2, limit + 1) if is_prime[i]]
return primes
```

The function first initializes a boolean array `is_prime` of size `limit + 1` with all entries set to `True`, except for the first two entries which represent 0 and 1, which are not prime. Then, the function iterates over the numbers from 2 to the square root of `limit`, checking ifeach number is prime and marking its multiples as composite numbers in the `is_prime` array. Finally, the function generates a list of prime numbers based on the `is_prime` array and returns it.

To use the function, simply call it with a limit value and assign the returned list of primes to a variable, like this:

```python
primes = generate_primes(20)
print(primes)
```

This will generate a list of all prime numbers up to 20 and print it:

```
[2, 3, 5, 7, 11, 13, 17, 19]
```

Rank the following functions from lowest to highest asymptotic growth rate.

Answers

To rank the functions from lowest to highest asymptotic growth rate, we need to compare their growth rates as the input size tends to infinity.

Here's the ranking from lowest to highest:

O(1): This represents constant time complexity. It means that the algorithm's runtime remains constant, regardless of the input size. It has the lowest growth rate.

O(log n): This represents logarithmic time complexity. It means that the algorithm's runtime grows logarithmically with the input size. Logarithmic growth is slower than linear growth but faster than polynomial growth.

O(n): This represents linear time complexity. It means that the algorithm's runtime grows linearly with the input size. As the input size increases, the runtime increases proportionally. Linear growth is slower than logarithmic growth but faster than polynomial growth.

O(n log n): This represents linearithmic time complexity. It means that the algorithm's runtime grows in proportion to n multiplied by the logarithm of n. Linearithmic growth is slower than linear growth but faster than polynomial growth.

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