Click Grades and complete the modules, no need to watch videos
Readiness Quiz
cubes = []
for number in lst:
cube = number*number*number
cubes.append(cube)
return cubes
squares=[]
for num in lst:
if (num % 2) == 0:
square = num*num
squares.append(square)
return squares
Heap & Varchar
4.
def make_dict(lst1,lst2):
list = {}
for key, value in zip(lst1, lst2):
list[key] = value
return list
5.
Set & list
6.
Base Python automatically parallelizes processing across cores when multiple cores are available
7.
def count_characters(string):
all_freq = {}
for i in string:
if i in all_freq:
all_freq[i] += 1
else:
all_freq[i] = 1
return all_freq
8.
import numpy as np
def calculate_l1_norm(v):
v = np.array(v)
l1_norm = np.sum(np.abs(v))
return float(l1_norm)
9.
import numpy as np
def get_vector_sum(vectorLower, vectorUpper):
vector = np.arange(vectorLower, vectorUpper)
matrix = np.reshape(vector, (10, 15))
row_sums = np.sum(matrix, axis=1)
return row_sums.tolist()
10.
Negative numbers and positive numbers less than 25
Numbers between 100-200 and numbers between 201-300
11.
import scipy.stats as stats
def geometric_distribution(p,k):
probability = (1 - p) ** (k - 1) * p
return probability
12.
import scipy.stats as stats
def poisson_distribution(mu,k):
probability = stats.poisson.pmf(k, mu)
return probability
13.
def gaussian_distribution(loc_val, scale_val, cdf_val):
probability = 1 - stats.norm.cdf(cdf_val, loc=loc_val, scale=scale_val)
return probability
14.
def matrix_multiplication(A,B):
n = len(A)
result = [[0 for _ in range(n)] for _ in range(n)]
for i in range(n):
for j in range(n):
for k in range(n):
result[i][j] += A[i][k] * B[k][j]
return result
Process Models, Design Thinking, and Introduction: End of Module Quiz
1. Which types of programming tasks best describes what you are expected to already have some familiarity with before beginning this course?
numeric computing, data munging, data visualization and data modeling
2. Though the emphasis may change, which two elements are both essential and common to all three process models we talked about?
resolve the business question, feedback loops
3. Is the following statement True/False? To succeed in this course you are expected to be proficient in any one of the following: R, Python or Java.
FALSE
4. Which of the following is the least accurate statement about the advantages of using process models in data science? Process models generally help by...
speeding up the process of getting through the workflow
5. Is the following statement True/False?
Design thinking is applied in other domains which helps make the task of communicating the AI workflow to those outside of data science easier.
TRUE
Data Collection: End of Module Quiz
1.
Create a service that improves on existing audio recognition systems as a richer interface to mobile devices
2.
Use customer segmentation and/or market analysis to help marketing with new customers
3.
Which members of leadership support this decision?
4.
TRUE
5.
Berkeley DB --> bsddb
Data Ingestion: End of Module Quiz
1.
word counts, user-item matrix for recommendations
2.
FALSE
3.
Implement several checks for quality assurance
4.
data engineer