Random Models, Nested and Split-plot Designs

 

What you'll learn

Design and analyze experiments where some of the factors are random

Design and analyze experiments where there are nested factors or hard-to-change factors

Analyze experiments with covariates

Design and analyze experiments with nonnormal response distributions

Join Free: Random Models, Nested and Split-plot Designs

There are 3 modules in this course

Many experiments involve factors whose levels are chosen at random. A well-know situation is the study of measurement systems to determine their capability.  This course presents the design and analysis of these types of experiments, including modern methods for estimating the components of variability in these systems. The course also covers experiments with nested factors, and experiments with hard-to-change factors that require split-plot designs. We also provide an overview of designs for experiments with response distributions from nonnormal response distributions and experiments with covariates.

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Response Surfaces, Mixtures, and Model Building

 

What you'll learn

Conduct experiments w/computer models and understand how least squares regression is used to build an empirical model from experimental design data

Understand the response surface methodology strategy to conduct experiments where system optimization is the objective

Recognize how the response surface approach can be used for experiments where the factors are the components of a mixture

Recognize where the objective of the experiment is to minimize the variability transmitted into the response from uncontrollable factors

Join Free: Response Surfaces, Mixtures, and Model Building

There are 4 modules in this course

Factorial experiments are often used in factor screening.; that is, identify the subset of factors in a process or system that are of primary important to the response. Once the set of important factors are identified interest then usually turns to optimization; that is, what levels of the important factors produce the best values of the response.  This course provides design and optimization tools to answer that questions using the response surface framework.  Other related topics include design and analysis of computer experiments, experiments with mixtures, and experimental strategies to reduce the effect of uncontrollable factors on unwanted variability in the response.

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Factorial and Fractional Factorial Designs

 

What you'll learn

Conduct a factorial experiment in blocks and construct and analyze a fractional factorial design

Apply the factorial concept to experiments with several factors

Use the analysis of variance for factorial designs

Use the 2^k system of factorial designs

Join Free: Factorial and Fractional Factorial Designs

There are 4 modules in this course

Many experiments in engineering, science and business involve several factors.  This course is an introduction to these types of multifactor experiments.  The appropriate experimental strategy for these situations is based on the factorial design, a type of experiment where factors are varied together.  This course focuses on designing these types of experiments and on using the ANOVA for analyzing the resulting data.  These types of experiments often include nuisance factors, and  the blocking principle can be used in factorial designs to handle these situations.  As the number of factors of interest grows full factorials become too expensive and fractional versions of the factorial design are useful.  This course will  cover the benefits of fractional factorials, along with methods for constructing and analyzing the data from these experiments.

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Experimental Design Basics

 

What you'll learn

By the end of this course, you will be able to:

Approach complex industrial and business research problems and address them through a rigorous, statistically sound experimental strategy

Use modern software to effectively plan experiments

Analyze the resulting data of an experiment, and communicate the results effectively to decision-makers.

Join Free: Experimental Design Basics

There are 5 modules in this course

This is a basic course in designing experiments and analyzing the resulting data. The course objective is to learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. Opportunities to use the principles taught in the course arise in all aspects of today’s industrial and business environment. Applications from various fields will be illustrated throughout the course.  Computer software packages (JMP, Design-Expert, Minitab) will be used to implement the methods presented and will be illustrated extensively. 

All experiments are designed experiments; some of them are poorly designed, and others are well-designed. Well-designed experiments allow you to obtain reliable, valid results faster, easier, and with fewer resources than with poorly-designed experiments. You will learn how to plan, conduct and analyze experiments efficiently in this course.

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Design of Experiments Specialization

 

What you'll learn

Plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain valid objective conclusions.

Use response surface methods for system optimization as a follow-up to successful screening.

Use experimental design tools for computer experiments, both deterministic and stochastic computer models.

Use software tools to create custom designs based on optimal design methodology for situations where standard designs are not easily applicable.

Join Free: Design of Experiments Specialization

Specialization - 4 course series

Learn modern experimental strategy, including factorial and fractional factorial experimental designs, designs for screening many factors, designs for optimization experiments, and designs for complex experiments such as those with hard-to-change factors and unusual responses. There is thorough coverage of modern data analysis techniques for experimental design, including software.  Applications include electronics and semiconductors, automotive and aerospace, chemical and process industries, pharmaceutical and bio-pharm, medical devices, and many others.

You can see an overview of the specialization from Dr. Montgomery here.

Applied Learning Project

Participants will complete a project that is typically based around their own work environment, and can use this to effectively demonstrate the application of experimental design methodology. The structure of the course and the step-by-stem process taught in the course is designed to ensure participant success.

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Learn to Program: The Fundamentals

 

There are 7 modules in this course

Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.

Join free : Learn to Program: The Fundamentals

Skills you'll gain

• Python Syntax And Semantics

• Computer Programming

• Python Programming

• Idle (Python)

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Cheat sheet for Python list

List Basics:

Creating a List:

my_list = [1, 2, 3, 'four', 5.0]

Accessing Elements:

first_element = my_list[0]

last_element = my_list[-1]

Slicing:

sliced_list = my_list[1:4]  # Returns elements at index 1, 2, and 3

List Operations:

Appending and Extending:

my_list.append(6)        # Adds 6 to the end

my_list.extend([7, 8])   # Extends with elements 7 and 8

Inserting at a Specific Position:

my_list.insert(2, 'inserted')  # Inserts 'inserted' at index 2

Removing Elements:

my_list.remove('four')  # Removes the first occurrence of 'four'

popped_element = my_list.pop(2)  # Removes and returns element at index 2

Sorting:

my_list.sort()   # Sorts the list in ascending order

my_list.reverse()  # Reverses the order of elements

List Functions:

Length and Count:

length = len(my_list)          # Returns the number of elements

count_of_element = my_list.count(2)  # Returns the count of occurrences of 2

Index and In:

index_of_element = my_list.index('four')  # Returns the index of 'four'

is_present = 5 in my_list       # Returns True if 5 is in the list, False otherwise

List Comprehensions:

Creating a New List:

squared_numbers = [x**2 for x in range(5)]

Conditional List Comprehension:

even_numbers = [x for x in range(10) if x % 2 == 0]

Miscellaneous:

Copying a List:

copied_list = my_list.copy()

Clearing a List:

my_list.clear()  # Removes all elements from the list

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Python Coding challenge - Day 117 | What is the output of the following Python Code?

 

The above code uses the symmetric difference operator (^) between two sets. The symmetric difference of two sets is the set of elements that are in either of the sets, but not in both.

Here's the output of the given code:

set1 = {1, 1, 2}

set2 = {2, 3, 4}

result = set1 ^ set2

print(result)

Output:

{1, 3, 4}

In the result set, you can see that it contains elements 1, 3, and 4, which are present in either set1 or set2 but not in both. Additionally, duplicate elements are automatically removed in a set, so even though set1 contains two occurrences of the element 1, it appears only once in the result set.

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Python Quick Interview Guide: Top Expert-Led Coding Interview Question Bank for Python Aspirants

 

Quick solutions to frequently asked algorithm and data structure questions.

Key Features

● Learn how to crack the Data structure and Algorithms Code test using the top 75 questions/solutions discussed in the book.

● Refresher on Python data structures and writing clean, actionable python codes.

● Simplified solutions on translating business problems into executable programs and applications.

Description

Python is the most popular programming language, and hence, there is a huge demand for Python programmers. Even if you have learnt Python or have done projects on AI, you cannot enter the top companies unless you have cleared the Algorithms and data Structure coding test.

This book presents 75 most frequently asked coding questions by top companies of the world. It not only focuses on the solution strategy, but also provides you with the working code. This book will equip you with the skills required for developing and analyzing algorithms for various situations. This book teaches you how to measure Time Complexity, it then provides solutions to questions on the Linked list, Stack, Hash table, and Math. Then you can review questions and solutions based on graph theory and application techniques. Towards the end, you will come across coding questions on advanced topics such as Backtracking, Greedy, Divide and Conquer, and Dynamic Programming.

After reading this book, you will successfully pass the python interview with high confidence and passion for exploring python in future.

What you will learn

● Design an efficient algorithm to solve the problem.

● Learn to use python tricks to make your program competitive.

● Learn to understand and measure time and space complexity.

● Get solutions to questions based on Searching, Sorting, Graphs, DFS, BFS, Backtracking, Dynamic programming.

Who this book is for

This book will help professionals and beginners clear the Data structures and Algorithms coding test. Basic knowledge of Python and Data Structures is a must.

Table of Contents

1. Lists, binary search and strings

2. Linked lists and stacks

3. Hash table and maths

4. Trees and graphs

5. Depth first search

6. Breadth first search

7. Backtracking

8. Greedy and divide and conquer algorithms

9. Dynamic programming

About the Author

Professor Shyamkant Limaye spent 18 years in the computer industry and 30 years in teaching electronics engineering students. His experience includes a two-year stint as a system analyst in the USA. In 1971, he graduated from Visvesvaraya National Institute of Technology in Electrical Engineering with a gold medal. He did masters from IIT Kanpur and Doctorate in electronics from RTM Nagpur University. He has guided ten students for PhD. He published a text book on VHDL programming in 2007. He has also published a thriller novel titled “Dual reality” in 2011. Currently, he is a Professor in the Electronics and Telecomm Department at St. Vincent Pallotti College of Engineering and Technology, Nagpur.

Hard Copy : Python Quick Interview Guide: Top Expert-Led Coding Interview Question Bank for Python Aspirants (English Edition)

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Jai Shree Ram using Python Code

from turtle import *

title('Jai Shree Ram')

bgcolor('black')

pensize(5)

pencolor("ORANGE")

Ram_naam = ["जय श्री राम"] * 50

angle = 360 / 50

penup()

sety(-1)

for _ in range(51):

    left(angle)

    forward(260)

    if Ram_naam:

        write(Ram_naam.pop(), align="right", font=('Arial', 12, "bold"))

    backward(260)

penup()    

goto(-40, -20)

pendown()

write("|| जय श्री राम ||", font=("Arial", 50, "normal"), align="center")

hideturtle()

done()

#clcoding.com

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Top 20 Python Dictionary Questions with answer

 

Question 1:

What is a dictionary in Python?

a) A collection of ordered elements

b) A collection of unordered elements

c) A single element

d) A data type

Question 2:

How do you create an empty dictionary in Python?

a) empty_dict = {}

b) empty_dict = dict()

c) empty_dict = new dict()

d) Both a and b

Question 3:

How do you access the value associated with a specific key in a dictionary?

a) dictionary.value(key)

b) dictionary[key]

c) dictionary.get(key)

d) dictionary.retrieve(key)

Question 4:

What is the purpose of the len() function when used with a dictionary?

a) It returns the total number of key-value pairs in the dictionary

b) It returns the last key in the dictionary

c) It returns the length of each value in the dictionary

d) It returns the sum of all values in the dictionary

Question 5:

How do you add a new key-value pair to a dictionary?

a) dictionary.add(key, value)

b) dictionary[key] = value

c) dictionary.insert(key, value)

d) dictionary.append(key, value)

Question 6:

What is the key difference between a dictionary and a list in Python?

a) Dictionaries are ordered, while lists are unordered

b) Dictionaries are mutable, while lists are immutable

c) Dictionaries can contain only numeric elements

d) Dictionaries are unordered and do not allow duplicate keys

Question 7:

How do you check if a key is present in a dictionary?

a) key in dictionary

b) dictionary.contains(key)

c) dictionary.exists(key)

d) key.exists(dictionary)

Question 8:

What does the dictionary.keys() method return?

a) The values of the dictionary

b) The keys of the dictionary

c) The key-value pairs of the dictionary

d) The length of the dictionary

Question 9:

How do you remove a key-value pair from a dictionary?

a) dictionary.remove(key)

b) dictionary.discard(key)

c) dictionary.delete(key)

d) All of the above

Question 10:

Which method is used to retrieve the value associated with a key, and if the key is not present, it returns a default value?

a) dictionary.get(key, default)

b) dictionary.retrieve(key, default)

c) dictionary.value(key, default)

d) dictionary.fetch(key, default)

Question 11:

What is the purpose of the pop() method in Python dictionaries?

a) Adds an element to the dictionary

b) Removes the last element from the dictionary and returns its value

c) Removes the first occurrence of the specified element

d) Removes the key-value pair for a specified key

Question 12:

How do you update the value associated with a key in a dictionary?

a) dictionary.update(key, new_value)

b) dictionary[key] = new_value

c) dictionary.modify(key, new_value)

d) dictionary.change_value(key, new_value)

Question 13:

What is the output of the following code?

my_dict = {"a": 1, "b": 2, "c": 3}

del my_dict["b"]

print(my_dict)

a) {"a": 1, "b": 2, "c": 3}

b) {"a": 1, "c": 3}

c) {"a": 1, "b": 2}

d) Raises an error

Question 14:

How do you iterate over the keys of a dictionary?

a) for key in dictionary.keys():

b) for key in dictionary:

c) for key in dictionary.values():

d) for key in dictionary.items():

Question 15:

What is the purpose of the values() method in Python dictionaries?

a) Returns the keys of the dictionary

b) Returns the values of the dictionary

c) Returns the key-value pairs of the dictionary

d) Returns the length of the dictionary

Question 16:

Which method is used to clear all key-value pairs from a dictionary?

a) dictionary.clear()

b) dictionary.remove_all()

c) dictionary.delete()

d) dictionary.empty()

Question 17:

What is the purpose of the items() method in Python dictionaries?

a) Returns the keys of the dictionary

b) Returns the values of the dictionary

c) Returns the key-value pairs of the dictionary

d) Returns the length of the dictionary

Question 18:

What is the output of the following code?

my_dict = {"apple": 3, "banana": 5, "cherry": 2}

sorted_dict = dict(sorted(my_dict.items()))

print(sorted_dict)

a) {"apple": 3, "banana": 5, "cherry": 2}

b) {"cherry": 2, "apple": 3, "banana": 5}

c) {"banana": 5, "apple": 3, "cherry": 2}

d) Raises an error

Question 19:

How do you create a dictionary with keys as numbers from 1 to 5 and values as their squares?

a) squares = {i: i ** 2 for i in range(1, 6)}

b) squares = {i: i * i for i in range(1, 6)}

c) squares = {i: i ** 2 for i in [1, 2, 3, 4, 5]}

d) All of the above

Question 20:

What is the purpose of the copy() method in Python dictionaries?

a) Creates a shallow copy of the dictionary

b) Creates a deep copy of the dictionary

c) Returns the reversed dictionary

d) Appends a copy of the dictionary to itself

Answer Key:

1. b) A collection of unordered elements
2. d) Both a and b
3. b) dictionary[key]
4. a) It returns the total number of key-value pairs in the dictionary
5. b) dictionary[key] = value
6. d) Dictionaries are unordered and do not allow duplicate keys
7. a) key in dictionary
8. b) The keys of the dictionary
9. d) All of the above
10. a) dictionary.get(key, default)
11. b) Removes the last element from the dictionary and returns its value
12. b) dictionary[key] = new_value
13. b) {"a": 1, "c": 3}
14. b) for key in dictionary:
15. b) Returns the values of the dictionary
16. a) dictionary.clear()
17. c) Returns the key-value pairs of the dictionary
18. b) {"cherry": 2, "apple": 3, "banana": 5}
19. a) squares = {i: i ** 2 for i in range(1, 6)}
20. a) Creates a shallow copy of the dictionary

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Top 20 Python Set Questions with answer

Question 1:

What is a set in Python?

a) A collection of ordered elements

b) A collection of unordered elements

c) A single element

d) A data type

Question 2:

How do you create an empty set in Python?

a) set()

b) empty_set = {}

c) empty_set = set()

d) Both b and c

Question 3:

How do you add an element to a set in Python?

a) set.insert(element)

b) set.add(element)

c) set.append(element)

d) set.include(element)

Question 4:

What is the key difference between a set and a list in Python?

a) Sets are ordered, while lists are unordered

b) Sets are mutable, while lists are immutable

c) Sets can contain only numeric elements

d) Sets are unordered and do not allow duplicate elements

Question 5:

How do you check if an element is present in a set?

a) element in set

b) set.contains(element)

c) set.exists(element)

d) element.exists(set)

Question 6:

What happens when you try to add a duplicate element to a set?

a) The element is added successfully

b) Python raises an exception

c) The duplicate element is ignored, and the set remains unchanged

d) The set is automatically sorted

Question 7:

How do you remove an element from a set?

a) set.remove(element)

b) set.delete(element)

c) set.pop(element)

d) set.discard(element)

Question 8:

What is the purpose of the len() function when used with a set?

a) It returns the total number of elements in the set

b) It returns the last element of the set

c) It returns the length of each element in the set

d) It returns the sum of all elements in the set

Question 9:

How do you create a set with elements from 1 to 5 in Python?

a) set = {1, 2, 3, 4, 5}

b) set = range(1, 6)

c) set = set(1, 6)

d) set = {range(1, 6)}

Question 10:

What is the purpose of the pop() method in Python sets?

a) Removes the last element from the set

b) Removes a random element from the set and returns it

c) Removes the first occurrence of the specified element

d) Sorts the elements of the set

Question 11:

What is the difference between a set and a frozenset in Python?

a) Sets are mutable, while frozensets are immutable

b) Sets are unordered, while frozensets are ordered

c) Sets can contain only numeric elements

d) Sets allow duplicate elements, while frozensets do not

Question 12:

Which of the following statements is true regarding the union of two sets?

a) The union operator for sets is +

b) The union of two sets is the intersection of their elements

c) The union of two sets contains all unique elements from both sets

d) The union of two sets results in an empty set

Question 13:

What is the purpose of the clear() method in Python sets?

a) Clears all elements from the set

b) Returns a clear copy of the set

c) Clears only the first element from the set

d) Clears the set if a specific element is provided

Question 14:

Which method is used to find the intersection of two sets in Python?

a) set.intersection(set2)

b) set.intersect(set2)

c) set.common(set2)

d) set.and(set2)

Question 15:

What is the output of the following code?

set1 = {1, 2, 3}

set2 = {3, 4, 5}

result = set1.union(set2)

print(result)

a) {1, 2, 3, 4, 5}

b) {1, 2, 3}

c) {3, 4, 5}

d) {1, 2, 4, 5}

Question 16:

How do you check if a set is a subset of another set?

a) set.is_subset(other_set)

b) set.subset_of(other_set)

c) set.issubset(other_set)

d) set.contains_subset(other_set)

Question 17:

What does the difference() method do when applied to two sets?

a) Returns the union of the two sets

b) Returns the intersection of the two sets

c) Returns the difference between the two sets

d) Returns the symmetric difference between the two sets

Question 18:

What is the purpose of the symmetric_difference() method in Python sets?

a) Returns the union of the two sets

b) Returns the intersection of the two sets

c) Returns the difference between the two sets

d) Returns the symmetric difference between the two sets

Question 19:

What is the output of the following code?

set1 = {1, 2, 3}

set2 = {3, 4, 5}

result = set1.difference(set2)

print(result)

a) {1, 2, 3, 4, 5}

b) {1, 2}

c) {3}

d) {4, 5}

Question 20:

What is the purpose of the issuperset() method in Python sets?

a) Checks if the set is a proper superset of another set

b) Checks if the set is a subset of another set

c) Checks if the set is equal to another set

d) Checks if the set contains all elements of another set

Answer Key:

1. b) A collection of unordered elements
2. c) empty_set = set()
3. b) set.add(element)
4. d) Sets are unordered and do not allow duplicate elements
5. a) element in set
6. c) The duplicate element is ignored, and the set remains unchanged
7. a) set.remove(element)
8. a) It returns the total number of elements in the set
9. a) set = {1, 2, 3, 4, 5}
10. b) Removes a random element from the set and returns it
11. a) Sets are mutable, while frozensets are immutable
12. c) The union of two sets contains all unique elements from both sets
13. a) Clears all elements from the set
14. a) set.intersection(set2)
15. a) {1, 2, 3, 4, 5}
16. c) set.issubset(other_set)
17. d) Returns the symmetric difference between the two sets
18. d) Returns the symmetric difference between the two sets
19. b) {1, 2}
20. a) Checks if the set is a proper superset of another set

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Introducing Microsoft Power BI Alberto Ferrari and Marco Russo (Free PDF)

 

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.

Introducing Microsoft Power BI enables you to evaluate when and how to use Power BI. Get inspired to improve business processes in your company by leveraging the available analytical and collaborative features of this environment.

Be sure to watch for the publication of Alberto Ferrari and Marco Russo's upcoming retail book, Analyzing Data with Power BI and Power Pivot for Excel (ISBN 9781509302765).

Free PDF Download: Introducing Microsoft Power BI Alberto Ferrari and Marco Russo

Contents

Introduction ....................................................viii

Downloads.....................................................................xi

Installing the companion content ..................xii

Acknowledgments.....................................................xii

Free ebooks from Microsoft Press .................... xiv

Errata, updates, & book support....................... xiv

We want to hear from you.....................................xv

Stay in touch................................................................xv

Chapter 1: Introducing Power BI .....................1

Getting started with Power BI................................ 4

Uploading data to Power BI .................................10

Introducing natural-language queries .............13

Introducing Quick Insights....................................16

Introduction to reports...........................................22

Introducing Visual Interactions ...........................30

Decorating the report.............................................37

Saving the report......................................................40

Pinning a report.........................................................41

iv Contents

Refreshing the budget workbook ......................43

Filtering a report .......................................................50

Conclusions .................................................................55

Chapter 2: Sharing the dashboard.................57

Inviting a user to see a dashboard ....................58

Inviting users outside your organization....66

Creating a group workspace in Power BI........71

Turning on sharing with Microsoft OneDrive

for Business .................................................................76

Viewing reports and dashboards on mobile

devices...........................................................................94

Conclusions .............................................................. 101

Chapter 3: Understanding data refresh ..... 103

Introducing data refresh ..................................... 105

Introducing the Power BI refresh

architecture .............................................................. 107

Introducing Power BI Desktop.......................... 111

Publishing to Power BI......................................... 117

Installing the Power BI Personal Gateway.... 120

Configuring automatic refresh ......................... 128

Conclusions .............................................................. 130

Chapter 4: Using Power BI Desktop ........... 132

v Contents

Connecting to a database .................................. 134

Loading from multiple sources ........................ 141

Using Query Editor................................................ 145

Hiding or removing tables ................................. 159

Handling seasonality and sorting months... 163

Conclusions .............................................................. 179

Chapter 5: Getting data from services

and content packs ........................................ 181

Consuming a service content pack................. 183

Creating a custom dataset from a service ... 197

Creating a content pack for your

organization............................................................. 211

Consuming an organizational content

pack............................................................................. 216

Updating an organizational content pack ... 223

Conclusions .............................................................. 227

Chapter 6: Building a data model............... 230

Loading individual tables.................................... 232

Implementing measures ..................................... 236

Creating calculated columns............................. 239

Improving the report by using measures..... 242

Integrating budget information....................... 244

vi Contents

Reallocating the budget...................................... 256

Conclusions .............................................................. 262

Chapter 7: Improving Power BI reports ..... 264

Choosing the right visualizations .................... 267

Choosing between standard visuals.......... 274

Using custom visualizations .............................. 283

First steps with custom visualizations ....... 284

Improving reports by using custom

visualizations....................................................... 291

Identifying conditions when custom

visualizations are required............................. 299

Using DAX in data models ................................. 303

Creating high-density reports .......................... 311

Conclusions .............................................................. 320

Chapter 8: Using Microsoft Power BI

in your company........................................... 323

Getting data from existing systems................ 325

Understanding differences between

data refresh and live connections .............. 328

Using relational databases on-premises.. 330

Using relational databases in the cloud... 335

Using live connections to Analysis

Services ................................................................. 338

vii Contents

Integrating Power BI with Office...................... 340

Publish Excel data models in Power BI..... 340

Consume Power BI content from Excel .... 343

Using Power BI Tiles from Office Store .... 350

Managing security to access data................... 360

Using row-level security ................................. 364

Extending and customizing Power BI ............ 370

Creating custom visualizations for

Power BI................................................................ 371

Introducing the Power BI REST API............ 372

Pushing real-time data to Power BI

dashboards .......................................................... 376

Power BI embedded in applications.......... 381

Conclusions .............................................................. 383

About the authors........................................... 386

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Power Bi Report Development Crash Course

 

What you'll learn

Create Relationship in PowerBI

Create Visualization in PowerBI 

Publish a report in powerbi service

Join Free: Power Bi Report Development Crash Course

About this Guided Project

Hello,

In this project, we will see in detail the steps required to create a PowerBI report using PowerBI Desktop and also we will publish this report on PowerBI service in the respective workspace. 

This project is designed for beginners who have no idea about PowerBI so they can quickly get a glimpse of how to create a report in PowerBI.

Pre-requisites:

- PowerBI Account

Here is a brief description of the tasks we are going to perform in this project:

Task1: Get Data

In this task we will first see an overview of PowerBI Desktop and then using PowerBI Desktop we will connect to the source data using the Get Data option. At the end of this task we would have imported all the source data in PowerBI Desktop

Task2: Create Relationship & Calculated Columns

In this task, we will create relationships between different source tables imported in PowerBI. In this process of creating relationship, we will also see how to created a calculated column using DAX query language.

Task3: Customize field level properties

In this task, we will explore and customize different field level properties like datatype, summarization, hidden etc.

Task4: Create visualizations in report

In this task, we will see how to create different visualizations in the report. We will also see how to add filters or slicers. We will explore visualizations like line chart, pie chart, table etc.

Task5: Publish report to PowerBI service

In all previous tasks, we have developed the report in PowerBI Desktop which is the local application installed on our desktop. In this task, we will see how to publish the task in power bi service. 

All The Best !!

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Prepare, Clean, Transform, and Load Data using Power BI

 

What you'll learn

Prepare and Clean Data using Power BI

Transform and Load Data using Power BI

Join Free: Prepare, Clean, Transform, and Load Data using Power BI

About this Guided Project

Usually, tidy data is a mirage in a real-world setting. Additionally, before quality analysis can be done, data need to be in a proper format. This project-based course, "Prepare, Clean, Transform, and Load Data using Power BI" is for beginner and intermediate Power BI users willing to advance their knowledge and skills. 

In this course, you will learn practical ways for data cleaning and transformation using Power BI. We will talk about different data cleaning and transformation tasks like splitting, renaming, adding, removing columns. By the end of this 2-hour-long project, you will change data types, merge and append data sets. By extension, you will learn how to import data from the web and unpivot data.

This project-based course is a beginner to an intermediate-level course in Power BI. Therefore, to get the most of this project, it is essential to have a basic understanding of using a computer before you take this project.

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Build an Income Statement Dashboard in Power BI

 

What you'll learn

Build an Income statement dashboard in Power BI

Visualize the income statement using cards, table and column charts

Transform & clean data in the Power Query editor

Join Free: Build an Income Statement Dashboard in Power BI

About this Guided Project

In this 1.5 hours long project, we will be creating an income statement dashboard filled with relevant charts and data. Power BI dashboards are an amazing way to visualize data and make them interactive.  We will begin this guided project by importing the data and transforming it in the Power Query editor. We will then visualize the Income Statement using a table, visualize total revenue, operating income and net income using cards and in the final task visualize the year on year growth using clustered column charts. This project is for anyone who is interested in Power BI and data visualization and specially for those who work in accounts and finance departments. By the end of this course, you will be confident in creating financial statement dashboards with many different kinds of visualizations.

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Create a Sales Dashboard using Power BI

 

What you'll learn

Build an attractive and interactive sales dashboard with all the necessary visualizations in a black and blue theme

Visualize sales data using bar charts & pie charts

Create interactive maps to visualize sales data by countries and markets

Join Free: Create a Sales Dashboard using Power BI

About this Guided Project

In this 1 hour long project, you will build an attractive and eye-catching sales dashboard using Power BI in a black and blue theme that will make your audience go "wow". We will begin this guided project by importing data. We will then create bar charts and pie charts to visualize the sales data and then position the graphs on the dashboard. In the final tasks, we will create interactive maps to visualize sales data by countries and markets. By the end of this course, you will be confident in creating beautiful dashboards with many different kinds of visualizations.

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HR Analytics- Build an HR dashboard using Power BI

 

What you'll learn

Build an attractive and eye-catching HR dashboard

Visualize gender & racial diversity using graphs & charts in Power BI

Explore buttons, themes, filters & slicers to make the dashboard interactive & smart

Join Free: HR Analytics- Build an HR dashboard using Power BI

About this Guided Project

In this 1 hour long project, you will build an attractive and eye-catching HR dashboard using Power BI. We will begin this guided project by importing data & creating an employee demographics page that gives us the overall demographic outlook of the organization. We will then create pie charts and doughnut charts to visualize gender & racial diversity. In the final tasks, we will create an employee detail page that will provide you with all the important information about any employee with just a click. We will also explore buttons, themes, slicers & filters to make the dashboard more interactive & useful. By the end of this course, you will be confident in creating beautiful HR dashboards that you can use for your personal or organizational purpose.

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Data-Driven Decisions with Power BI

 

There are 5 modules in this course

New Power BI users will begin the course by gaining a conceptual understanding of the Power BI desktop application and the Power BI service. Learners will explore the Power BI interface while learning how to manage pages and understand the basics of visualizations.  Learners can download a course dataset and engage in numerous hands-on experiences to discover how to import, connect, clean, transform, and model their own data in the Power BI desktop application.

Join Free: Data-Driven Decisions with Power BI

 Learners will investigate reports, learn about workspaces, and practice viewing, creating, and publishing reports to the Power BI service. Finally, learners will become proficient in the creation and utilization dashboards.

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Use Power Bi for Financial Data Analysis

 What you'll learn

Navigate and understand the process of importing data into Power Bi.

Use Power Query to clean data before constructing visuals and reports. Determine relationships between data and use reference tables in Power Bi.

Create and design a reporting dashboard with dynamic features. Publish and share your report

Join Free:Use Power Bi for Financial Data Analysis

About this Guided Project

In this project, learners will have a guided look through Power Bi dynamic reports and visualizations for financial data analysis. As you view, load, and transform your data in Power Bi, you will learn which steps are key to making an effective financial report dashboard and how to connect your report for dynamic visualizations. Data reporting and visualization is the most critical step in a financial, business, or data analyst’s functions. The data is only as effective if it can be communicated effectively to key stakeholders in the organization. Effective communication of data starts here.

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Build Dashboards in Power BI

 

What you'll learn

Build a Dashboard in Power BI by building a report and visuals.

Build a report with visuals.

Create a dashboard and pin visuals.

Join Free: Build Dashboards in Power BI

About this Guided Project

In this project, you will create a Dashboard in Power BI. You will get data to bring into a model, build several reports, generate informative charts from each report, then choose powerful visuals to highlight on a Dashboard. Your new skills will help you efficiently summarize important information on a one-page dashboard with visual data.

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Getting Started with Power BI Desktop

 

What you'll learn

Import and Transform Data with Power BI Desktop

Visualize Data with Power BI Desktop

Join Free: Getting Started with Power BI Desktop

About this Guided Project

In this 2-hour long project-based course, you will learn the basics of using Power BI Desktop software. We will do this by analyzing data on credit card defaults with Power BI Desktop. Power BI Desktop is a free Business Intelligence application from Microsoft that lets you load, transform, and visualize data. You can create interactive reports and dashboards quite easily, and quickly. We will learn some of the basics of Power BI by importing, transforming, and visualizing the data.

This course is aimed at learners who are looking to get started with the Power BI Desktop software. There are no hard prerequisites and any competent computer user should be able to complete the project successfully.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Top 20 Python Tuple Questions

 

What is a tuple in Python?

a) A collection of unordered elements

b) A collection of ordered elements

c) A single element

d) A data type

Question 2:

How do you create an empty tuple in Python?

a) tuple()

b) empty_tuple = ()

c) empty_tuple = tuple()

d) Both b and c

Question 3:

How do you access the first element of a tuple?

a) tuple[0]

b) tuple.first()

c) tuple.first

d) tuple.get(0)

Question 4:

Which of the following statements is used to add an element to a tuple?

a) tuple.insert(0, element)

b) Tuples are immutable, so elements cannot be added once a tuple is created

c) tuple.add(element)

d) tuple.extend(element)

Question 5:

What is the key difference between a tuple and a list in Python?

a) Tuples are mutable, while lists are immutable

b) Tuples are ordered, while lists are unordered

c) Tuples are immutable, while lists are mutable

d) Tuples can contain only numeric elements

Question 6:

How do you check if an element is present in a tuple?

a) element in tuple

b) tuple.contains(element)

c) tuple.exists(element)

d) element.exists(tuple)

Question 7:

What does the tuple.count(element) method do?

a) Counts the total number of elements in the tuple

b) Counts the occurrences of a specific element in the tuple

c) Counts the sum of all elements in the tuple

d) Counts the average value of elements in the tuple

Question 8:

How do you concatenate two tuples in Python?

a) tuple1 + tuple2

b) tuple1.concat(tuple2)

c) concat(tuple1, tuple2)

d) combine(tuple1, tuple2)

Question 9:

How do you create a tuple with a single element?

a) single_tuple = (1)

b) single_tuple = 1,

c) single_tuple = (1,)

d) Both a and b

Question 10:

Which method is used to find the index of the first occurrence of a specified element in a tuple?

a) tuple.index(element)

b) tuple.find(element)

c) tuple.search(element)

d) tuple.loc(element)

Question 11:

What happens when you try to modify an element in a tuple?

a) It is not possible to modify elements in a tuple as they are immutable

b) The element is updated successfully

c) Python raises an exception

d) The element is deleted from the tuple

Question 12:

How do you create a tuple with elements from 1 to 5 in Python?

a) tuple = (1, 2, 3, 4, 5)

b) tuple = range(1, 6)

c) tuple = tuple(1, 6)

d) tuple = (range(1, 6))

Question 13:

What is the purpose of the len() function when used with a tuple?

a) It returns the total number of elements in the tuple

b) It returns the last element of the tuple

c) It returns the length of each element in the tuple

d) It returns the sum of all elements in the tuple

Question 14:

How do you check if two tuples are equal?

a) tuple1.is_equal(tuple2)

b) tuple1 == tuple2

c) tuple1.equals(tuple2)

d) tuple1.equals(tuple2, strict=True)

Question 15:

What is the purpose of the max() function when used with a tuple?

a) It returns the maximum element in the tuple

b) It returns the index of the maximum element in the tuple

c) It returns the sum of all elements in the tuple

d) It returns the average value of elements in the tuple

Question 16:

Which method is used to remove the last element from a tuple?

a) tuple.remove_last()

b) tuple.pop()

c) tuple.delete_last()

d) Tuples are immutable, so elements cannot be removed

Question 17:

How do you convert a list to a tuple in Python?

a) tuple(list)

b) tuple = list

c) tuple.from_list(list)

d) tuple.convert(list)

Question 18:

What is the purpose of the sorted() function when applied to a tuple?

a) Reverses the order of elements in the tuple

b) Sorts the elements of the tuple in ascending order

c) Removes duplicate elements from the tuple

d) Shuffles the elements of the tuple randomly

Question 19:

What does the tuple.index(element) method return if the element is not found in the tuple?

a) None

b) -1

c) 0

d) Raises a ValueError

Question 20:

What is the output of the following code?

my_tuple = (3, 1, 4, 1, 5, 9, 2)

my_tuple.sort()

print(my_tuple)

a) (1, 1, 2, 3, 4, 5, 9)

b) (9, 5, 4, 3, 2, 1, 1)

c) (1, 1, 2, 3, 4, 5, 9, 2)

d) (1, 2, 3, 4, 5, 9)

Answer: 

1. b) A collection of ordered elements
2. d) Both b and c
3. a) tuple[0]
4. b) Tuples are immutable, so elements cannot be added once a tuple is created
5. c) Tuples are immutable, while lists are mutable
6. a) element in tuple
7. b) Counts the occurrences of a specific element in the tuple
8. a) tuple1 + tuple2
9. c) single_tuple = (1,)
10. a) tuple.index(element)
11. a) It is not possible to modify elements in a tuple as they are immutable
12. a) tuple = (1, 2, 3, 4, 5)
13. a) It returns the total number of elements in the tuple
14. b) tuple1 == tuple2
15. a) It returns the maximum element in the tuple
16. d) Tuples are immutable, so elements cannot be removed
17. a) tuple(list)
18. b) Sorts the elements of the tuple in ascending order
19. d) Raises a ValueError
20. d) (1, 2, 3, 4, 5, 9)

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The Big Book of Small Python Projects: 81 Easy Practice Programs

 

Best-selling author Al Sweigart shows you how to easily build over 80 fun programs with minimal code and maximum creativity.

If you’ve mastered basic Python syntax and you’re ready to start writing programs, you’ll find The Big Book of Small Python Projects both enlightening and fun. This collection of 81 Python projects will have you making digital art, games, animations, counting pro- grams, and more right away. Once you see how the code works, you’ll practice re-creating the programs and experiment by adding your own custom touches.

These simple, text-based programs are 256 lines of code or less. And whether it’s a vintage screensaver, a snail-racing game, a clickbait headline generator, or animated strands of DNA, each project is designed to be self-contained so you can easily share it online.

You’ll create:

• Hangman, Blackjack, and other games to play against your friends or the computer

• Simulations of a forest fire, a million dice rolls, and a Japanese abacus

• Animations like a virtual fish tank, a rotating cube, and a bouncing DVD logo screensaver

• A first-person 3D maze game

• Encryption programs that use ciphers like ROT13 and Vigenère to conceal text

If you’re tired of standard step-by-step tutorials, you’ll love the learn-by-doing approach of The Big Book of Small Python Projects. It’s proof that good things come in small programs!

Hard Copy : The Big Book of Small Python Projects: 81 Easy Practice Programs

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Top 20 Python List Questions

Question 1:

What is a list in Python?

a) A collection of unordered elements

b) A collection of ordered elements

c) A single element

d) A data type

Question 2:

How do you create an empty list in Python?

a) list()

b) empty_list = []

c) empty_list = list()

d) Both b and c

Question 3:

How do you access the first element of a list?

a) list[0]

b) list.first()

c) list.first

d) list.get(0)

Question 4:

Which of the following statements is used to add an element to the end of a list?

a) list.insert(0, element)

b) list.add(element)

c) list.append(element)

d) list.extend(element)

Question 5:

What is the purpose of the len() function when used with a list?

a) It returns the total number of elements in the list

b) It returns the last element of the list

c) It returns the length of each element in the list

d) It returns the sum of all elements in the list

Question 6:

How do you check if an element is present in a list?

a) element in list

b) list.contains(element)

c) list.exists(element)

d) element.exists(list)

Question 7:

What does the list.remove(element) function do?

a) Removes the first occurrence of the specified element from the list

b) Removes all occurrences of the specified element from the list

c) Removes the last element from the list

d) Removes the element at the specified index

Question 8:

How do you reverse the order of elements in a list?

a) list.reverse()

b) list.sort(reverse=True)

c) list.reorder()

d) list.flip()

Question 9:

What is the difference between the append() and extend() methods in Python lists?

a) There is no difference, and the terms are interchangeable

b) append() adds a single element, while extend() adds multiple elements

c) extend() adds a single element, while append() adds multiple elements

d) Both methods are used for removing elements from a list

Question 10:

What is the output of the following code?

my_list = [1, 2, 3]

new_list = my_list * 2

print(new_list)

a) [1, 2, 3, 1, 2, 3]

b) [2, 4, 6]

c) [1, 4, 9]

d) [1, 2, 3, 6, 9]

Question 11:

Which method is used to find the index of the first occurrence of a specified element in a list?

a) list.index(element)

b) list.find(element)

c) list.search(element)

d) list.loc(element)

Question 12:

How do you copy the elements of one list to another list in Python?

a) new_list = old_list.copy()

b) new_list = old_list.clone()

c) new_list = copy(old_list)

d) new_list = old_list[:]

Question 13:

What is the purpose of the pop() method in Python lists?

a) Adds an element to the end of the list

b) Removes the last element from the list and returns it

c) Removes the first occurrence of the specified element

d) Sorts the elements of the list

Question 14:

What is the difference between a list and a tuple in Python?

a) Lists are mutable, while tuples are immutable

b) Lists are immutable, while tuples are mutable

c) Both lists and tuples are mutable

d) Both lists and tuples are immutable

Question 15:

How do you insert an element at a specific index in a list?

a) list.add(index, element)

b) list.insert(index, element)

c) list.insert(element, index)

d) list.put(index, element)

Question 16:

Which method is used to clear all elements from a list?

a) list.clear()

b) list.remove_all()

c) list.delete()

d) list.empty()

Question 17:

What does the sorted() function do when applied to a list?

a) Reverses the order of elements in the list

b) Sorts the elements of the list in ascending order

c) Removes duplicate elements from the list

d) Shuffles the elements of the list randomly

Question 18:

What is the purpose of the count() method in Python lists?

a) Counts the total number of elements in the list

b) Counts the occurrences of a specific element in the list

c) Counts the sum of all elements in the list

d) Counts the average value of elements in the list

Question 19:

How do you create a list of numbers from 1 to 5 in Python?

a) list = [1, 2, 3, 4, 5]

b) list = range(1, 6)

c) list = list(1, 6)

d) list = [range(1, 6)]

Question 20:

What is the output of the following code?

my_list = [3, 1, 4, 1, 5, 9, 2]

my_list.sort()

print(my_list)

a) [1, 1, 2, 3, 4, 5, 9]

b) [9, 5, 4, 3, 2, 1, 1]

c) [1, 1, 2, 3, 4, 5, 9, 2]

d) [1, 2, 3, 4, 5, 9]

Answer : 

Question 1: b) A collection of ordered elements

Question 2: d) Both b and c

Question 3: a) list[0]

Question 4: c) list.append(element)

Question 5: a) It returns the total number of elements in the list

Question 6: a) element in list

Question 7: a) Removes the first occurrence of the specified element from the list

Question 8: a) list.reverse()

Question 9: b) append() adds a single element, while extend() adds multiple elements

Question 10: a) [1, 2, 3, 1, 2, 3]

Question 11: a) list.index(element)

Question 12: d) new_list = old_list[:]

Question 13: b) Removes the last element from the list and returns it

Question 14: a) Lists are mutable, while tuples are immutable

Question 15: b) list.insert(index, element)

Question 16: a) list.clear()

Question 17: b) Sorts the elements of the list in ascending order

Question 18: b) Counts the occurrences of a specific element in the list

Question 19: b) list = range(1, 6)

Question 20: a) [1, 1, 2, 3, 4, 5, 9]

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Python Coding challenge - Day 116 | What is the output of the following Python Code?

 

Code:

my_list = [1, 2]

new_list = my_list * 2

print(new_list)

Solution and Explanation: 

The above code creates a new list new_list by repeating the elements of my_list twice using the * operator. Here's the output of the code:

my_list = [1, 2]

new_list = my_list * 2

print(new_list)

Output:

[1, 2, 1, 2]

As you can see, the elements [1, 2] from my_list are repeated, resulting in a new list [1, 2, 1, 2]. The * operator in this context duplicates the elements of the list the specified number of times.

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How much do you know about Pandas library?

Question 1:

What is Pandas?

a) A species of bear

b) A data manipulation and analysis library in Python

c) A programming language

d) A web development framework

Question 2:

Which of the following data structures is used to store one-dimensional labeled data in Pandas?

a) Series

b) DataFrame

c) Array

d) List

Question 3:

How can you import the Pandas library in Python?

a) import panda

b) import pandas as pd

c) from pandas import *

d) import pd

Question 4:

What is the primary purpose of a Pandas DataFrame?

a) Storing only numerical data

b) Storing two-dimensional labeled data

c) Storing images and multimedia files

d) Storing text data

Question 5:

Which Pandas function is used to read a CSV file into a DataFrame?

a) read_csv()

b) load_csv()

c) import_csv()

d) read_file()

Question 6:

How can you access a specific column in a Pandas DataFrame?

a) By using the column's index

b) By using the column's label or name

c) By using the row number

d) By using the DataFrame's index

Question 7:

What does the head() function do in Pandas?

a) Prints the first few rows of a DataFrame

b) Prints the last few rows of a DataFrame

c) Prints a summary statistics of the DataFrame

d) Prints the shape of the DataFrame

Question 8:

Which Pandas method is used to check for missing values in a DataFrame?

a) find_missing()

b) check_missing()

c) missing_values()

d) isnull()

Question 9:

What is the purpose of the iloc method in Pandas?

a) Selects columns based on their labels

b) Selects rows and columns based on their integer positions

c) Performs element-wise operations on a DataFrame

d) Checks for duplicate values in a DataFrame

Question 10:

How can you drop a column named "Column_A" from a Pandas DataFrame called df?

a) df.remove("Column_A")

b) df.drop("Column_A", axis=1)

c) df.delete("Column_A")

d) df.remove_column("Column_A")

Question 11:

Which Pandas function is used to filter rows based on a condition?

a) filter_rows()

b) select_rows()

c) filter()

d) query()

Question 12:

In Pandas, what is the purpose of the groupby() function?

a) Groups data based on unique values in a column

b) Reverses the order of rows in a DataFrame

c) Computes the mean of each column

d) Reshapes a DataFrame into a pivot table

Question 13:

How can you rename a specific column in a Pandas DataFrame?

a) rename_column()

b) change_column_name()

c) df.rename()

d) df.change_name()

Question 14:

What does the merge() function in Pandas do?

a) Merges two DataFrames based on a specified column or index

b) Adds a new column to a DataFrame

c) Sorts the rows of a DataFrame

d) Reshapes a DataFrame into a long format

Question 15:

Which Pandas function is used to pivot a DataFrame?

a) pivot()

b) reshape()

c) pivot_table()

d) transpose()

Question 16:

What is the purpose of the to_csv() function in Pandas?

a) Converts a DataFrame to a CSV file

b) Converts a CSV file to a DataFrame

c) Checks if a CSV file is valid

d) Counts the number of occurrences of each value in a DataFrame

Question 17:

Which method is used to fill missing values in a Pandas DataFrame with a specific value?

a) fillna()

b) fill_missing()

c) replace()

d) impute()

Question 18:

How can you sort a Pandas DataFrame based on a specific column?

a) df.sort_by("column_name")

b) df.sort("column_name")

c) df.sort_values("column_name")

d) df.order_by("column_name")

Question 19:

What is the purpose of the pivot_table() function in Pandas?

a) Pivots a DataFrame from long to wide format

b) Computes the sum of each column in a DataFrame

c) Transposes a DataFrame

d) Aggregates data based on one or more columns

Question 20:

Which Pandas function is used to calculate summary statistics of a DataFrame?

a) describe()

b) summary()

c) stats()

d) analyze()

Answer : 

Question 1: b) A data manipulation and analysis library in Python

Question 2: a) Series

Question 3: b) import pandas as pd

Question 4: b) Storing two-dimensional labeled data

Question 5: a) read_csv()

Question 6: b) By using the column's label or name

Question 7: a) Prints the first few rows of a DataFrame

Question 8: d) isnull()

Question 9: b) Selects rows and columns based on their integer positions

Question 10: b) df.drop("Column_A", axis=1)

Question 11: d) query()

Question 12: a) Groups data based on unique values in a column

Question 13: c) df.rename()

Question 14: a) Merges two DataFrames based on a specified column or index

Question 15: c) pivot_table()

Question 16: a) Converts a DataFrame to a CSV file

Question 17: a) fillna()

Question 18: c) df.sort_values("column_name")

Question 19: d) Aggregates data based on one or more columns

Question 20: a) describe()

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How much do you know about NumPy library?

 

What is NumPy?

a) A programming language

b) A machine learning library

c) A numerical computing library in Python

d) A deep learning framework

Question 2:

Which of the following is the primary purpose of NumPy?

a) Web development

b) Data manipulation and analysis

c) Game development

d) Network programming

Question 3:

What does the term "NumPy" stand for?

a) Numerical Python

b) Nonlinear Python

c) Neural Python

d) Numeral Python

Question 4:

Which of the following is the correct way to import NumPy in Python?

a) import np

b) import numpy as np

c) from numpy import *

d) include numpy

Question 5:

What is the core data structure in NumPy for representing arrays?

a) Lists

b) Tuples

c) Arrays

d) Sets

Question 6:

Which NumPy function is used to create an array with a range of values?

a) numpy.linspace()

b) numpy.range()

c) numpy.aranges()

d) numpy.arange()

Question 7:

What is the result of the following NumPy expression: numpy.zeros(5)?

a) An array with five elements, all set to 1

b) An array with five elements, all set to 0

c) An empty array

d) An array with five elements, all set to 5

Question 8:

Which NumPy function is used to find the mean of an array?

a) numpy.mean()

b) numpy.average()

c) numpy.median()

d) numpy.mean_value()

Question 9:

In NumPy, what does the function numpy.random.rand() do?

a) Generates random integers

b) Generates random floats in the half-open interval [0.0, 1.0)

c) Computes the standard deviation of an array

d) Creates an array with a specified shape and all elements initialized to the same value

Question 10:

What does the term "broadcasting" mean in the context of NumPy?

a) Sending data over a network

b) Extending the dimensions of an array

c) Performing element-wise operations on arrays of different shapes and sizes

d) Converting data types in an array

Question 11:

Which NumPy function is used to perform element-wise multiplication of two arrays?

a) numpy.multiply()

b) numpy.dot()

c) numpy.cross()

d) numpy.product()

Question 12:

How can you reshape a NumPy array with dimensions (4, 5) into a new array with dimensions (2, 10)?

a) array.reshape((2, 10))

b) numpy.resize(array, (2, 10))

c) numpy.reshape(array, (2, 10))

d) array.resize((2, 10))

Question 13:

What is the purpose of the NumPy function numpy.linalg.inv()?

a) Computes the determinant of a matrix

b) Finds the eigenvalues of a matrix

c) Computes the inverse of a matrix

d) Calculates the singular value decomposition of a matrix

Question 14:

Which NumPy function is used to concatenate two or more arrays along a specified axis?

a) numpy.append()

b) numpy.concatenate()

c) numpy.concat()

d) numpy.merge()

Question 15:

How can you find the index of the maximum value in a NumPy array?

a) numpy.max_index()

b) numpy.argmax()

c) numpy.index_max()

d) numpy.maximum_index()

Question 16:

What is the purpose of the NumPy function numpy.fft.fft()?

a) Finds the fast file transfer of an array

b) Computes the fast Fourier transform of an array

c) Calculates the flip-flop transform of an array

d) Performs the fuzzy frequency transformation of an array

Question 17:

Which NumPy function is used to calculate the dot product of two arrays?

a) numpy.dot()

b) numpy.multiply()

c) numpy.cross()

d) numpy.product()

Question 18:

In NumPy, what does the function numpy.where() do?

a) Filters elements of an array based on a condition

b) Finds the index of a specified element in an array

c) Transposes the elements of an array

d) Sorts the elements of an array

Question 19:

What is the purpose of the NumPy function numpy.save()?

a) Saves an array to a text file

b) Saves an array to a binary file in NumPy's .npy format

c) Saves an array to a CSV file

d) Saves an array to an Excel file

Question 20:

How can you find the unique elements in a NumPy array?

a) numpy.unique()

b) numpy.distinct()

c) numpy.uniq()

d) numpy.elements()

Question 1: c) A numerical computing library in Python

Question 2: b) Data manipulation and analysis

Question 3: a) Numerical Python

Question 4: b) import numpy as np

Question 5: c) Arrays

Question 6: d) numpy.arange()

Question 7: b) An array with five elements, all set to 0

Question 8: a) numpy.mean()

Question 9: b) Generates random floats in the half-open interval [0.0, 1.0)

Question 10: c) Performing element-wise operations on arrays of different shapes and sizes

Question 11: a) numpy.multiply()

Question 12: a) array.reshape((2, 10))

Question 13: c) Computes the inverse of a matrix

Question 14: b) numpy.concatenate()

Question 15: b) numpy.argmax()

Question 16: b) Computes the fast Fourier transform of an array

Question 17: a) numpy.dot()

Question 18: a) Filters elements of an array based on a condition

Question 19: b) Saves an array to a binary file in NumPy's .npy format

Question 20: a) numpy.unique()

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Managing Machine Learning Projects

 

Build your subject-matter expertise

This course is part of the AI Product Management Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Managing Machine Learning Projects

There are 5 modules in this course

This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects.  The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems.  Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.

At the conclusion of this course, you should be able to:

1) Identify opportunities to apply ML to solve problems for users

2) Apply the data science process to organize ML projects

3) Evaluate the key technology decisions to make in ML system design

4) Lead ML projects from ideation through production using best practices

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