Python for Data Science Cognitive Class Exam Answers 2024

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Enroll Here: Python for Data Science Cognitive Class Exam Answers 2024

Course Syllabus:

Module 1 – Python Basics

• Your first program
• Types
• Expressions and Variables
• String Operations

Module 2 – Python Data Structures

• Lists and Tuples
• Sets
• Dictionaries

Module 3 – Python Programming Fundamentals

• Conditions and Branching
• Loops
• Functions
• Objects and Classes

Module 4 – Working with Data in Python

• Reading files with open
• Writing files with open
• Loading data with Pandas
• Working with and Saving data with Pandas

Module 5 – Working with Numpy Arrays and Simple APIs

• Numpy 1D Arrays
• Numpy 2D Arrays
• Simple APIs
• API Setup

General Information:

• This course is self-paced.
• It can be taken at any time.
• It can be audited as many times as you wish.
• There is only ONE chance to pass the course, but multiple attempts per question

RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE: Basic Math

Module 1: Python Basics

Question 1: What is the result of the following operation in Python:

• 3 + 2 * 2
• 10
• 7
• 9
• 12

Question 2: In Python, if you executed name = ‘Lizz’, what would be the output of print(name[0:2])?

• Lizz
• L
• Liz
• Li

Question 3: In Python, if you executed var = ‘01234567’, what would be the result of print(var[::2])?

• 0246
• 1357
• 1234567
• 8903

Question 4: In Python, what is the result of the following operation ‘1’+’2′

• ‘2’
• ‘3’
• ’12’
• 3

Question 5: Given myvar = ‘hello’, how would you convert myvar into uppercase?

• len(myvar)
• myvar.find(‘hello’)
• myvar.upper()
• myvar.sum()

Module 2: Python Data Structures

Question 1: What is the syntax to obtain the first element of the tuple:

• A=(‘a’,’b’,’c’)
• A[1]
• A[0] correct
• A[:]

Question 2: After applying the following method,L.append([‘a’,’b’]), the following list will only be one element longer.

• True
• False

Question 3: How many duplicate elements can you have in a set?

• 1
• 0, you can only have one unique element in a set
• 100
• depends on the number of elements in your set.

Question 4: >>>>Consider the following Python Dictionary:

Dict={“A”:1,”B”:”2″,”C”:[3,3,3],”D”:(4,4,4),’E’:5,’F’:6}, what is the result of the following operation: Dict[“D”]

• 4
• 3
• [3,3,3]
• (4, 4, 4) correct
• error

Question 5: What is an important difference between lists and tuples?

• Lists can’t contain a string
• Tuples can only have integers
• Lists and tuples are the same.
• Lists are mutable tuples are not
• There is no zeros in lists

Module 3: Python Programming Fundamentals

Question 1: What is the output of the following lines of code:

x=1

if(x!=1):

print(‘Hello’)

else:

print(‘Hi’)

print(‘Mike’)

• Hi Mike
• Mike
• Hello Mike
• The Mike

Question 2: What is the output of the following few lines of code?

A=[‘1′,’2′,’3’]

for a in A:

print(2*a)

• 2 4 6
• ‘2’ ‘4’ ‘6’
• ’11’ ’22’ ’33’
• A B C

Question 3: Consider the function Delta, when will the function return a value of 1

def Delta(x):

if x==0:

y=1;

else:

y=0;

return(y)

• When the input is anything but 0
• When the input in 1
• Never
• When the input is 0

Question 4: What is the correct way to sort the list ‘B’ using a method, the result should not return a new list, just change the list ‘B’.

• B.sort()
• sort(B)
• sorted(B)
• B.sorted()

Question 5: What are the keys of the of the following {‘a’:1,’b’:2}

• 1,2
• ;,:
• a,b

Module 4: Working with Data in Python

Question 1: What do the following lines of code do?

with open(“Example1.txt”,”r”) as file1:

FileContent=file1.readlines()

print(FileContent)

• Read the file “Example1.txt” correct
• Write to the file “Example1.txt”
• Append the file “Example1.txt”

Question 2: What do the following lines of code do?

with open(“Example2.txt”,”w”) as writefile:

writefile.write(“This is line A\n”)

writefile.write(“This is line B\n”)

• Read the file “Example2.txt”
• Write to the file “Example2.txt”
• Append the file “Example2.txt”

Question 3: What do the following lines of code do?

with open(“Example3.txt”,”a”) as file1:

file1.write(“This is line C\n”)

• Read the file “Example3.txt”
• Write to the file “Example3.txt”
• Append the file “Example3.txt”

Question 4: What is the result of applying the following method df.head(), to the dataframe df

• prints the first row of the dataframe
• prints the first column of the dataframe
• prints the first 5 rows of the dataframe
• prints the dateframe out

Module 5: Working with Numpy Arrays

Question 1: What is the result of the following lines of code:

a=np.array([0,1,0,1,0])

b=np.array([1,0,1,0,1])

a*b

• 0
• array([1, 1, 1, 1, 1])
• array([0, 0, 0, 0, 0])

Question 2: What is the result of the following lines of code:

a=np.array([0,1])

b=np.array([1,0])

np.dot(a,b)

• 1
• array([1,1])
• 0
• array([0,0])

Question 3: What is the result of the following lines of code:

a=np.array([1,1,1,1,1])

a+10

• array([10,10,10,10,10])
• array([11, 11, 11, 11, 11])
• array([1,1,1,1,1])

Question 4: What is the correct code to perform matrix multiplication on the matrix A and B

• np.dot(A,B)
• A*B
• AxB

Python for Data Science Final Exam Answers

Question 1: What is the result of the following operation 3+2*2?

• 3
• 12
• 9
• 7

Question 2: What is the type of the following variable: a=True?

• int
• bool
• str
• list

Question 3: What is the result of the following operation int(3.2)?

• 3.2
• 3
• 4
• ‘3.2’

Question 4: Consider the string A=’1234567′, what is the result of the following operation: A[1::2]

• ‘1234567’
• ‘246’
• ‘1357’
• error

Question 5: Consider the string Name=”Michael Jackson” , what is the result of the following operation Name.find(‘el’)

• 5
• 4
• 5,6
• -1

Question 6: The variables A=’1′ and B=’2′ ,what is the result of the operation A+B?

you cant add two strings

• 3
• ‘3’
• ’12’

Question 7: Consider the variable F=”You are wrong”, Convert the values in the variable F to uppercase?

• F.up()
• F.upper
• F.upper()

Question 8: Consider the tuple tuple1=(“A”,”B”,”C” ), what is the result of the following operation tuple1[-1]?

• “A”
• “B”
• “C”

Question 9: Consider the tuple A=((11,12),[21,22]), that contains a tuple and list. What is the result of the following operation A[1]:

• ((11,12),[21,22])
• (11,12)
• (21,22)
• [21,22]

Question 10: Consider the tuple A=((11,12),[21,22]), that contains a tuple and list. What is the result of the following operation A[0][1]:

• 12
• 11
• 22
• 21

Question 11: What is the result of the following operation ‘1,2,3,4’.split(‘,’)

• ‘1’,’2′,’3′,’4′
• (‘1′,’2′,’3′,’4’)
• [‘1′,’2′,’3′,’4’]
• ‘1234’

Question 12: Concatenate the following lists A=[1,’a’] and B=[2,1,’d’]:

• A+B
• A-B
• A*B
• A/B

Question 13: How do you cast the list ‘A’ to the set ‘a’?

• a.set()
• a=A.append()
• a=A.dict()
• a=set(A)

Question 14: Consider the Set: V={‘A’,’B’}, what is the result of V.add(‘C’)?

• {‘A’,’B’}
• {‘A’,’B’,’C’}
• {‘AC’,’BC’}
• error

Question 15: Consider the Set: V={‘A’,’B’,’C’ }, what is the result of V.add(‘C’)?

• {‘A’,’B’}
• {‘A’,’B’,’C’}
• {‘A’,’B’,’C’,’C’}
• error

Question 16: What is the output of the following lines of code:

x=”Go”

if(x!=”Go”):

print(‘Stop’)

else:

print(‘Go ‘)

print(‘Mike’)

• Go Mike
• Mike
• Stop Mike
• The Mike

Question 17: What is the output of the following lines of code:

x=”Go”

if(x==”Go”):

print(‘Go ‘)

else:

print(‘Stop’)

print(‘Mike’)

• Go Mike
• Mike
• Stop Mike
• The Mike

Question 18: How many iterations are performed in the following loop?

for n in range(3):

print(n)

• 1
• 2
• 3
• 4

Question 19: What does the following loop print?

for n in range(3):

print(n+1)

• 0 1 2
• 1 2 3
• 3 2 1
• 2 1 0

Question 20: What is the output of the following few lines of code ?

A=[‘1′,’2′,’3’]

for a in A:

print(2*a)

• 2 4 6
• ‘2’ ‘4’ ‘6’
• ’11’ ’22’ ’33’
• A B C

Question 21: Consider the function add, what is the result of calling the following Add(‘1′,’1’) (look closely at the return statement)

def Add(x,y):

z=y+x

return(y)

• error
• ‘2’
• ’11’
• ‘1’

Question 22: Consider the class Points, what are the data attributes:

class Points(object):

def __init__(self,x,y):

self.x=x

self.y=y

def print_point(self):

print(‘x=’,self.x,’y=’,self.y)

• __init__
• self.x self.y
• print_point

Question 23: What is the result of running the following lines of code ?

class Points(object):

def __init__(self,x,y):

self.x=x

self.y=y

def print_point(self):

print(‘x=’,self.x,’ y=’,self.y)

p1=Points(1,2)

p1.print_point()

• x=1
• y=2
• x=1 y=2

Question 24: What is the result of running the following lines of code?

class Points(object):

def __init__(self,x,y):

self.x=x

self.y=y

def print_point(self):

print(‘x=’,self.x,’ y=’,self.y)

p2=Points(1,2)

p2.x=2

p2.print_point()

• x=1
• y=2
• x=1 y=2
• x=2 y=2

Question 25: Consider the following line of code: with open(example1,”r”) as file1:

What mode is the file object in?

• read
• write
• append

Introduction to Python for Data Science

Python is widely recognized as one of the most versatile and powerful programming languages, particularly in the realm of data science. Its popularity stems from its simplicity and readability, making it accessible for beginners while offering robust capabilities for professionals.

Why Python for Data Science?

1. Ease of Learning: Python’s syntax is straightforward and easy to understand, even for those new to programming.
2. Extensive Libraries: Python boasts a rich ecosystem of libraries and tools specifically designed for data manipulation, analysis, and visualization. Key libraries include:
   • NumPy: Fundamental package for numerical computing with arrays.
   • Pandas: Data manipulation and analysis tool.
   • Matplotlib and Seaborn: Libraries for data visualization.
   • Scikit-learn: Simple and efficient tools for data mining and data analysis.
   • TensorFlow and PyTorch: Libraries for machine learning and deep learning.
3. Community and Support: Python has a vast and active community of developers and data scientists who contribute to its growth and provide support through forums, tutorials, and open-source contributions.

Getting Started with Python for Data Science:

1. Installation: Start by installing Python and the necessary libraries. Anaconda distribution is popular for data science as it includes many of the essential libraries pre-installed.
2. Basic Python Concepts: Familiarize yourself with Python basics such as variables, data types (lists, tuples, dictionaries), control structures (if statements, loops), functions, and object-oriented programming if needed.
3. Working with Libraries:
   • NumPy: Learn to create arrays, perform operations on them, and understand broadcasting and vectorization.
   • Pandas: Explore data structures like Series and DataFrame, learn data manipulation techniques such as filtering, sorting, grouping, and merging.
   • Matplotlib/Seaborn: Plotting histograms, scatter plots, line plots, and customizing plots for effective data visualization.
4. Data Analysis and Visualization: Practice loading datasets, performing exploratory data analysis (EDA), cleaning data (handling missing values, outliers), and visualizing data to extract insights.
5. Introduction to Machine Learning: Once comfortable with data manipulation and visualization, delve into basic machine learning concepts using libraries like Scikit-learn. Start with simple models like linear regression, decision trees, or k-nearest neighbors.
6. Advanced Topics: As you progress, explore more advanced topics such as deep learning using TensorFlow or PyTorch, natural language processing (NLP), time series analysis, or big data processing with tools like Spark.

Resources for Learning:

• Online Courses: Platforms like Coursera, edX, and Udemy offer courses specifically tailored to learning Python for data science.
• Books: Recommended titles include “Python for Data Analysis” by Wes McKinney, and “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron.
• Documentation and Tutorials: Official documentation for Python and its libraries, along with community-contributed tutorials and blogs, are valuable resources.

Conclusion:

Python’s versatility and the vast array of libraries make it an ideal choice for data science projects, from data cleaning and analysis to machine learning and deep learning applications. By mastering Python fundamentals and its specialized libraries, you’ll be well-equipped to tackle real-world data challenges and advance your career in data science.