Friday, 23 February 2024

Lists data structures in Python

 


Example 1: Creating a List

In [2]:
[1, 2, 3, 4, 5]
['apple', 'banana', 'orange']
[1, 'hello', 3.14, True]
[]

Example 2: Accessing Elements in a List

In [3]:
apple
5
[2, 3, 4]
['apple', 'banana']

Example 3: Modifying Elements in a List

In [4]:
['apple', 'grape', 'orange']
['apple', 'grape', 'orange', 'kiwi']
['apple', 'grape', 'orange', 'kiwi', 'mango']

Example 4: Removing Elements from a List

In [4]:
['apple', 'grape', 'kiwi', 'mango', 'pineapple']
Popped fruit: grape
['apple', 'kiwi', 'mango', 'pineapple']

Example 5: List Operations

In [5]:
4
True
[1, 2, 3, 4, 5, 6, 7, 8]

Example 6: List Iteration

In [6]:
Fruit: apple
Fruit: kiwi
Fruit: mango
Fruit: pineapple
Index: 0, Fruit: apple
Index: 1, Fruit: kiwi
Index: 2, Fruit: mango
Index: 3, Fruit: pineapple

Financial Machine Learning (Foundations and Trends(r) in Finance)

  


Financial Machine Learning surveys the nascent literature on machine learning in the study of financial markets. The authors highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

This survey is organized as follows. Section 2 analyzes the theoretical benefits of highly parameterized machine learning models in financial economics. Section 3 surveys the variety of machine learning methods employed in the empirical analysis of asset return predictability. Section 4 focuses on machine learning analyses of factor pricing models and the resulting empirical conclusions for risk-return tradeoffs. Section 5 presents the role of machine learning in identifying optimal portfolios and stochastic discount factors. Section 6 offers brief conclusions and directions for future work.

PDF: Financial Machine Learning (Foundations and Trends(r) in Finance)


Hard Copy: Financial Machine Learning (Foundations and Trends(r) in Finance)


Free Courses Machine learning for Finance 

Fundamentals of Machine Learning in Finance https://www.clcoding.com/2024/02/fundamentals-of-machine-learning-in.html

Python and Machine Learning for Asset Management 

https://www.clcoding.com/2024/02/python-and-machine-learning-for-asset_19.html

Guided Tour of Machine Learning in Finance https://www.clcoding.com/2024/02/guided-tour-of-machine-learning-in.html

Python and Machine-Learning for Asset Management with Alternative Data Sets https://www.clcoding.com/2024/02/python-and-machine-learning-for-asset.html

Python for Finance: Beta and Capital Asset Pricing Model https://www.clcoding.com/2024/02/python-for-finance-beta-and-capital.html



Python Coding challenge - Day 136 | What is the output of the following Python Code?

 



The code creates a list data with elements [1, 2, 3, 4] and then creates a copy of this list called backup_data using the copy() method. After that, it modifies the fourth element of the original data list by setting it to 7. Finally, it prints the backup_data list.

Let's analyze the code step by step:

data = [1, 2, 3, 4]: Initializes a list named data with elements [1, 2, 3, 4].

backup_data = data.copy(): Creates a shallow copy of the data list and assigns it to backup_data. Both lists will initially contain the same elements.

data[3] = 7: Modifies the fourth element of the data list, changing it from 4 to 7.

print(backup_data): Prints the backup_data list. Since it's a copy made before the modification, it will not reflect the change made to the data list.

So, when you run this code, the output will be:

[1, 2, 3, 4]

This is because the modification of the data list does not affect the backup_data list, as it was created as a separate copy.

Thursday, 22 February 2024

10-question multiple-choice quiz on Pandas


 1. What is Pandas?

a. A data visualization library

b. A web development framework

c. A data manipulation library

d. A machine learning framework


2.  What is the primary data structure in Pandas for one-dimensional labeled data?

a. Series

b. DataFrame

c. Array

d. List


3. How do you read a CSV file into a Pandas DataFrame?

a. pd.load_csv()

b. pd.read_csv()

c. pd.read_data()

d. pd.import_csv()


4. How do you select a specific column in a Pandas DataFrame?

a. df.column('ColumnName')

b. df.select('ColumnName')

c. df['ColumnName']

d. df.get('ColumnName')


5. What is the purpose of the head() method in Pandas?

a. It gives the first few rows of the DataFrame

b. It returns the last rows of the DataFrame

c. It displays a summary statistics of the DataFrame

d. It provides information about the columns in the DataFrame


6. How do you handle missing values in a Pandas DataFrame?

a. Use the fillna() method

b. Use the remove_na() method

c. Use the drop_na() method

d. Pandas automatically handles missing values


7. What function is used to group data in Pandas based on one or more columns?

a. groupby()

b. aggregate()

c. sort()

d. combine()


8. How do you merge two DataFrames in Pandas based on a common column?

a. df.merge()

b. df.join()

c. df.concat()

d. df.combine()


9. What does the describe() method in Pandas provide?

a. Descriptive statistics of the DataFrame

b. A list of unique values in each column

c. Information about data types in the DataFrame

d. A summary of missing values in the DataFrame


10. What is the purpose of the to_csv() method in Pandas?

a. It saves the DataFrame to a CSV file

b. It converts the DataFrame to a Series

c. It exports the DataFrame to an Excel file

d. It prints the DataFrame to the console


Answer:

1. c, 

2. a, 

3. b, 

4. c, 

5. a, 

6. a, 

7. a, 

8. a, 

9. a, 

10. a

Wednesday, 21 February 2024

Python Coding challenge - Day 135 | What is the output of the following Python Code?

 


Let's break down the code:

from random import *

x = [0, 2, 4]

print(sample(x, 2))

from random import *: This line imports all functions from the random module. This means you can use functions from the random module without prefixing them with random..

x = [0, 2, 4]: A list named x is defined with elements 0, 2, and 4.

sample(x, 2): The sample function is called with two arguments - the population (which is the list x), and the number of elements to be randomly chosen (2 in this case). The sample function returns a new list containing unique elements randomly chosen from the population.

print(...): The result of the sample function is printed.

So, when you run this code, it will output a list containing 2 randomly selected elements from the list x. The output will vary each time you run the code due to the random selection. For example, it might output [2, 4] or [0, 4], etc.


Word cloud using Python Libraries

 



from wordcloud import WordCloud

import matplotlib.pyplot as plt

# Read text from a file
with open('cl.txt', 'r', encoding='utf-8') as file:
    text = file.read()

# Generate word cloud
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)

# Display the generated word cloud using matplotlib
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

#clcoding.com

Python Coding challenge - Day 134 | What is the output of the following Python Code?

 


The above code is a list comprehension in Python. It creates a new list where each element is the cube of the corresponding element in the original list a.

Here's the breakdown of the code:

a = [-2, -1, 0, 1, 2]: Initializes a list a with the values -2, -1, 0, 1, and 2.

print([i**3 for i in a]): Uses a list comprehension to create a new list by cubing each element in the original list a. The expression i**3 calculates the cube of each element i. The resulting list is then printed.

Output:

[-8, -1, 0, 1, 8]

So, the printed list is [-8, -1, 0, 1, 8], which represents the cubes of the elements in the original list a.

Tuesday, 20 February 2024

Cybersecurity Attack and Defense Fundamentals Specialization

 


What you'll learn

Information security threats, vulnerabilities, and attacks.

Network security assessment techniques and tools.

Computer forensics fundaments, digital evidence, and forensic investigation phases.

Join Free: Cybersecurity Attack and Defense Fundamentals Specialization

Specialization - 3 course series

This Specialization can be taken by students, IT professionals, IT managers, career changers, and anyone who seeks a cybersecurity career or aspires to advance their current role. This course is ideal for those entering the cybersecurity workforce, providing foundational, hands-on skills to solve the most common security issues organizations face today.


This 3-course Specialization will help you gain core cybersecurity skills needed to protect critical data, networks, and digital assets. You will learn to build the foundation that enables individuals to grow their skills in specialized domains like penetration testing, security consulting, auditing, and system and network administration. 

Applied Learning Project

Learn to troubleshoots  network security problems, monitor alerts, and follow policies, procedures, and standards to protect information assets. You will gain practical skills cybersecurity professionals need in Information Security, Network Security, Computer Forensics, Risk Management, Incident Handling, and the industry best practices.

Cybersecurity: Developing a Program for Your Business Specialization

 


Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from University System of Georgia

Join Free: Cybersecurity: Developing a Program for Your Business Specialization

Specialization - 4 course series

Cybersecurity is an essential business skill for the evolving workplace. For-profit companies, government agencies, and not-for-profit organizations all need technologically proficient, business-savvy information technology security professionals. In this Specialization, you will learn about  a variety of processes for protecting business assets through policy, education and training, and technology best practices. You’ll develop an awareness of the risks and cyber threats or attacks associated with modern information usage, and explore key technical and managerial topics required for a balanced approach to information protection. Topics will include mobility, the Internet of Things, the human factor,  governance and management practices.

Enterprise and Infrastructure Security

 


Build your subject-matter expertise

This course is part of the Introduction to Cyber Security 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: Enterprise and Infrastructure Security

There are 4 modules in this course

This course introduces a series of advanced and current topics in cyber security, many of which are especially relevant in modern enterprise and infrastructure settings. The basics of enterprise compliance frameworks are provided with introduction to NIST and PCI. Hybrid cloud architectures are shown to provide an opportunity to fix many of the security weaknesses in modern perimeter local area networks.

Emerging security issues in blockchain, blinding algorithms, Internet of Things (IoT), and critical infrastructure protection are also described for learners in the context of cyber risk. Mobile security and cloud security hyper-resilience approaches are also introduced. The course completes with some practical advice for learners on how to plan careers in cyber security.

Introduction to Python for Cybersecurity

 


Build your subject-matter expertise

This course is part of the Python for Cybersecurity 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: Introduction to Python for Cybersecurity

There are 3 modules in this course

This course it the first part of the Python for Cybersecurity Specialization. Learners will get an introduction and overview of the course format and learning objectives.

Security Analyst Fundamentals Specialization

 


What you'll learn

Develop knowledge in digital forensics, incident response and penetration testing.

Advance your knowledge of cybersecurity analyst tools including data and endpoint protection; SIEM; and systems and network fundamentals.  

Get hands-on experience to develop skills  via industry specific and open source Security tools.

Apply your skills to investigate a real-world security breach identifying the attack, vulnerabilities, costs and prevention recommendations.

Join Free: Security Analyst Fundamentals Specialization

Specialization - 3 course series

There are a growing number of exciting, well-paying jobs in today’s security industry that do not require a traditional college degree. Forbes estimates that there will be as many as 3.5 million unfilled positions in the industry worldwide by 2021! One position with a severe shortage of skills is as a cybersecurity analyst.

Throughout this specialization, you will learn concepts around digital forensics, penetration testing and incident response.  You will learn about threat intelligence and tools to gather data to prevent an attack or in the event your organization is attacked.  You will have the opportunity to review some of the largest breach cases and try your hand at reporting on a real world breach.  

The content creators and instructors are architects , Security Operation Center (SOC) analysts, and distinguished engineers who work with cybersecurity in their day to day lives at IBM with a worldwide perspective. They will share their skills which they need to secure IBM and its clients security systems.

The completion of this specialization also makes you eligible to earn the System Analyst Fundamentals IBM digital badge. More information about the badge can be found here:

https://www.youracclaim.com/org/ibm/badge/security-analyst-fundamentals         

Applied Learning Project

Throughout the program, you will use virtual labs and internet sites that will provide you with practical skills with applicability to real jobs that employers value, including:

Tools: e.g. Wireshark, IBM QRadar, IBM MaaS360, IBM Guardium, IBM Resilient, i2 Enterprise Insight 

 Labs: SecurityLearningAcademy.com

Libraries: Python

Projects: Investigate a real-world security breach identifying the attack, vulnerabilities, costs and prevention recommendations.

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