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.

Monday, 19 February 2024

Advanced Django: Advanced Django Rest Framework

 


What you'll learn

Optimize the Django Rest Framework

Integrate with ReactJS

Join Free: Advanced Django: Advanced Django Rest Framework

There are 4 modules in this course

Code and run Django websites without installing anything!

This course is designed for learners who are familiar with Python and basic Django skills (similar to those covered in the Django for Everybody specialization). The modules in this course cover testing, performance considerations such as caching and throttling, use of 3rd party libraries, and integrating frontends within the context of the Django REST framework.

To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to slowly building features, resulting in large coding projects at the end of the course.

Course Learning Objectives: 

Write and run tests on Django applications
Optimize code performance using caching, throttling, and filtering
Use a 3rd Party library
Integrate with common Frontends

Select Topics in Python Specialization

 


What you'll learn

Create websites with Django

Create charts and plots with Matplotlib and Jupyter notebooks

Create a chatbot with the NLTK library

Join Free: Select Topics in Python Specialization

Specialization - 4 course series

This specialization is intended for people who are interested in furthering their Python skills. It is assumed that students are familiar with Python and have taken the Programming in Python: A Hands-On Tutorial.

These four courses cover a wide range of topics. Learn how to create and manage Python package. Use Jupyter notebooks to visualize data with Matplotlib. The third course focuses on the basics of the Django web framework. Finally, learn how to leverage Python for natural langauge processing.

Applied Learning Project

Learners create a variety of projects from their own Python packages, as well as use third-party package management tools. They also transform data into different charts and plots. In the Django course, learners build three simple websites. Finally, natural language processing powers a chatbot that learners build.

Web Applications and Command-Line Tools for Data Engineering

 


What you'll learn

Construct Python Microservices with FastAPI

Build a Command-Line Tool in Python using Click

Compare multiple ways to set up and use a Jupyter notebook

Join Free: Web Applications and Command-Line Tools for Data Engineering

There are 4 modules in this course

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry.

Database Engineer Capstone

 


What you'll learn

Build a MySQL database solution.

Deploy level-up ideas to enhance the scope of a database project.

Join Free: Database Engineer Capstone

There are 4 modules in this course

In this course you’ll complete a capstone project in which you’ll create a database and client for Little Lemon restaurant.

To complete this course, you will need database engineering experience.  

The Capstone project enables you to demonstrate multiple skills from the Certificate by solving an authentic real-world problem. Each module includes a brief recap of, and links to, content that you have covered in previous courses in this program. 

In this course, you will demonstrate your new skillset by designing and composing a database solution, combining all the skills and technologies you've learned throughout this program to solve the problem at hand. 

By the end of this course, you’ll have proven your ability to:

-Set up a database project,
-Add sales reports,
-Create a table booking system,
-Work with data analytics and visualization,
-And create a database client.

You’ll also demonstrate your ability with the following tools and software:

-Git,
-MySQL Workbench,
-Tableau,
-And Python.

Web Application Technologies and Django

 


What you'll learn

Explain the basics of HTTP and how the request-response cycle works

Install and deploy a simple DJango application

Build simple web pages in HTML and style them using CSS

Explain the basic operations in SQL

Join Free: Web Application Technologies and Django

There are 5 modules in this course

In this course, you'll explore the basic structure of a web application, and how a web browser interacts with a web server. You'll be introduced to the Hypertext Transfer Protocol (HTTP) request/response cycle, including GET/POST/Redirect. You'll also gain an introductory understanding of Hypertext Markup Language (HTML), as well as the overall structure of a Django application.  We will explore the Model-View-Controller (MVC) pattern for web applications and how it relates to Django.  You will learn how to deploy a Django application using a service like PythonAnywhere so that it is available over the Internet. 

This is the first course in the Django for Everybody specialization. It is recommended that you complete the Python for Everybody specialization or an equivalent learning experience before beginning this series.

Python Coding challenge - Day 133 | What is the output of the following Python Code?

 

The above code assigns the string '2\t4' to the variable x and then prints the value of x. The string '2\t4' contains the characters '2', '\', 't', and '4'.

When you print the value of x, it will display:

2\t4

The '\t' in the string represents the escape sequence for a tab character, so when you print it, you'll see a tab between '2' and '4' in the output.

Fundamentals of Machine Learning in Finance

 


Build your subject-matter expertise

This course is part of the Machine Learning and Reinforcement Learning in Finance 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: Fundamentals of Machine Learning in Finance

There are 4 modules in this course

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.  

A learner with some or no previous knowledge of Machine Learning (ML)  will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.
Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The course is designed for three categories of students:
Practitioners working at financial institutions such as banks, asset management firms or hedge funds
Individuals interested in applications of ML for personal day trading
Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance  

Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

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