Free Courses from Cisco
1. Cybersecurity Operations Fundamentals Specialization
2. Introduction to Network Automation
3. Security Operations Center (SOC)
4. Network Automation Engineering Fundamentals Specialization
6. DevOps for Network Automation (NetDevOps)
7. Ansible for Network Automation
8. Using APIs for Network Automation
10. Introducing Model-Driven Programmability
Describe ML, explain its role in data engineering, summarize generative AI, discuss Spark's uses, and analyze ML pipelines and model persistence.
Evaluate ML models, distinguish between regression, classification, and clustering models, and compare data engineering pipelines with ML pipelines.
Construct the data analysis processes using Spark SQL, and perform regression, classification, and clustering using SparkML.
Demonstrate connecting to Spark clusters, build ML pipelines, perform feature extraction and transformation, and model persistence.
Explore the exciting world of machine learning with this IBM course.
Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos.
Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML.
In practical labs, you'll utilize SparkML for regression, classification, and clustering, enabling you to construct prediction and classification models. Connect to Spark clusters, analyze SparkSQL datasets, perform ETL activities, and create ML models using Spark ML and sci-kit learn. Finally, demonstrate your acquired skills through a final assignment.
This intermediate course is suitable for aspiring and experienced data engineers, as well as working professionals in data analysis and machine learning. Prior knowledge in Big Data, Hadoop, Spark, Python, and ETL is highly recommended for this course.
Load, clean, analyze, process, and visualize data using Python and Jupyter Notebooks
Produce an end-to-end machine learning prediction model using Python and Jupyter Notebooks
Data Science
Data Analysis
Python Programming
Machine Learning
In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model.
Important Information:
How to register?
To participate, you’ll need to complete simple steps. First, click the “Start Project” button to register.
Next, you’ll need to create a Coursera Skills Profile, which only takes a few minutes. We’ll send you a profile link the week of the challenge.
When does the challenge start?
The coding challenge begins Tuesday, August 29th, at 8 AM (PST) and closes Thursday, August 31st, at 11:59 PM (PST). If you’re registered, you’ll receive a reminder email on the challenge start date.
Please note this is a timed competition. Once the challenge is unlocked, you’ll have 72 hours to complete it. You can submit as many times as you would like within this timeframe.
What will the winners receive?
Participants will be evaluated based on their model’s prediction accuracy. The top 20% of participants will receive an achievement badge on their Coursera Skills Profile, highlighting their performance to recruiters. The top 100 performers will get complimentary access to select Data Science courses.
All participants can showcase their projects to potential employers on their Coursera Skills Profile.
Winners will be notified by email the week of September 10th.
Good luck, and have fun!
This project requires you to independently complete the following steps:
•Importing and preprocessing data
•Analyze the data
•Build machine learning models
•Evaluate machine learning models
Describe the various types of Machine Learning algorithms and when to use them
Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression
Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees
Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics
Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.
This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.
You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN.
With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.
By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.
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a == c: This checks if the values of a and c are equal. The values of both lists are [1, 2, 3], so a == c is True.
a is c: This checks if a and c refer to the exact same object. As mentioned before, although the values of the lists are the same, a and c are two different list objects. Therefore, a is c is False.
So, the output of this code will be: True, False
The correct explanation is that in Python, -0.0 and 0.0 are considered equal, and the max() function does not distinguish between them based on sign. When you use max(-0.0, 0.0), the result will be the number with the higher magnitude, regardless of its sign. In this case, both -0.0 and 0.0 have the same magnitude, so the result will be the one that appears first in the arguments, which is -0.0:
result = max(-0.0, 0.0)
print(result)
Output:
-0.0
So, max(-0.0, 0.0) returns -0.0 due to the way Python handles the comparison of floating-point numbers.
The round() function is used to round a number to the nearest integer. Let's calculate the results:
round(3 / 2) is equal to round(1.5), and when rounded to the nearest integer, it becomes 2.
round(5 / 2) is equal to round(2.5), and when rounded to the nearest integer, it becomes 2.
So, the results are:
round(3 / 2) equals 2.
round(5 / 2) equals 2.
Why does round(5 / 2) return 2 instead of 3? The issue here is that Python’s round method implements banker’s rounding, where all half values will be rounded to the closest even number.
lists, tuples, sets, and dictionaries in Python based on various characteristics:
Lists: Mutable. You can modify, add, or remove elements after creation.
Tuples: Immutable. Once created, elements cannot be changed.
Sets: Mutable. You can add or remove elements, but each element must be unique.
Dictionaries: Mutable. You can add, modify, or remove key-value pairs.
Lists: Ordered. Elements are stored in a specific order and can be accessed by index.
Tuples: Ordered. Similar to lists, elements have a specific order.
Sets: Unordered. Elements have no specific order, and you cannot access them by index.
Dictionaries: Prior to Python 3.7, dictionaries were unordered. From Python 3.7 onwards, dictionaries maintain insertion order.
Lists: Can contain duplicate elements.
Tuples: Can contain duplicate elements.
Sets: Cannot contain duplicate elements.
Dictionaries: Keys must be unique.
Lists: Defined using square brackets [].
Tuples: Defined using parentheses ().
Sets: Defined using curly braces {}.
Dictionaries: Defined using curly braces {} with key-value pairs separated by colons.
Lists: Use when you need a mutable, ordered collection with the possibility of duplicate elements.
Tuples: Use when you need an immutable, ordered collection. Suitable for situations where data should not be changed.
Sets: Use when you need an unordered collection of unique elements and order doesn't matter.
Dictionaries: Use when you need a mutable collection of key-value pairs, providing fast lookup based on keys.
my_list = [1, 2, 3]
my_tuple = (4, 5, 6)
my_set = {7, 8, 9}
my_dict = {'a': 10, 'b': 11, 'c': 12}
Prepare for a career as a full stack developer. Gain the in-demand skills and hands-on experience to get job-ready in less than 4 months. No prior experience required.
Master the most up-to-date practical skills and tools that full stack developers use in their daily roles
Learn how to deploy and scale applications using Cloud Native methodologies and tools such as Containers, Kubernetes, Microservices, and Serverless
Develop software with front-end development languages and tools such as HTML, CSS, JavaScript, React, and Bootstrap
Build your GitHub portfolio by applying your skills to multiple labs and hands-on projects, including a capstone
Prepare for a career in the high-growth field of software development. In this program, you’ll learn in-demand skills and tools used by professionals for front-end, back-end, and cloud native application development to get job-ready in less than 4 months, with no prior experience needed.
Full stack refers to the end-to-end computer system application, including the front end and back end coding. This Professional Certificate covers development for both of these scenarios. Cloud native development refers to developing a program designed to work on cloud architecture. The flexibility and adaptability that full stack and cloud native developers provide make them highly sought after in this digital world.
You’ll learn how to build, deploy, test, run, and manage full stack cloud native applications. Technologies covered includes Cloud foundations, GitHub, Node.js, React, CI/CD, Containers, Docker, Kubernetes, OpenShift, Istio, Databases, NoSQL, Django ORM, Bootstrap, Application Security, Microservices, Serverless computing, and more.
After completing the program you will have developed several applications using front-end and back-end technologies and deployed them on a cloud platform using Cloud Native methodologies. You will publish these projects through your GitHub repository to share your portfolio with your peers and prospective employers.
This program is ACE® recommended—when you complete, you can earn up to 18 college credits.
Applied Learning Project
Throughout the courses in the Professional Certificate, you will develop a portfolio of hands-on projects involving various popular technologies and programming languages in Full Stack Cloud Application Development. These projects include creating:
HTML pages on Cloud Object Storage
An interest rate calculator using HTML, CSS, and JavaScript
An AI program deployed on Cloud Foundry using DevOps principles and CI/CD toolchains with a NoSQL database
A Node.js back-end application and a React front-end application
A containerized guestbook app packaged with Docker deployed with Kubernetes and managed with OpenShift
A Python app bundled as a package
A database-powered application using Django ORM and Bootstrap
An app built using Microservices & Serverless
A scalable, Cloud Native Full Stack application using the technologies learned in previous courses
You will publish these projects through your GitHub repository to share your skills with your peers and prospective employers.
How to architect larger programs using object-oriented principles
Re-use parts of classes using inheritance
Encapsulate relevant information and methods in a class
Code and run your first python program in minutes without installing anything!
This course is designed for learners with limited coding experience, providing a solid foundation of not just python, but core Computer Science topics that can be transferred to other languages. The modules in this course cover inheritance, encapsulation, polymorphism, and other object-related topics. Completion of the prior 3 courses in this specialization is recommended.
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 small, approachable coding exercises that take minutes instead of hours.
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The above code of the list my_list using slicing and assigns a new set of values [7, 8, 9] to the sliced portion. Here's a step-by-step breakdown:
my_list = [1, 2, 3, 4, 5]
This initializes a list with the values [1, 2, 3, 4, 5].
my_list[1:3] = [7, 8, 9]
This slices the elements at index 1 and 2 (exclusive) of my_list and replaces them with the values [7, 8, 9]. After this line executes, my_list becomes [1, 7, 8, 9, 4, 5].
print(my_list)
This prints the modified list:
[1, 7, 8, 9, 4, 5]
So, the final output is [1, 7, 8, 9, 4, 5].
.png)
The expression 3 * 2 ** 3 involves exponentiation and multiplication. The exponentiation operator ** has higher precedence than multiplication.
Let's break it down step by step:
3. 2∗∗32∗∗3 is 2×2×22×2×2, which equals 88.
4. 3×83×8 is 2424.
So, the output of the code: print(3 * 2 ** 3)
The all function returns True if all elements of the iterable are true (or if the iterable is empty). If any element is false, it returns False.
In the case of an empty iterable, such as an empty list ([]), the all function returns True because there are no elements that are false.
So, when you execute print(all([])), it will output: True
def is_prime(number):
# Check if the number is less than 2
if number < 2:
return False
# Check for factors from 2 to the square root of the number
for i in range(2, int(number**0.5) + 1):
if number % i == 0:
# If a factor is found, the number is not prime
return False
# If no factors are found, the number is prime
return True
# Example usage
num = int(input("Enter a number: "))
if is_prime(num):
print(f"{num} is a prime number.")
else:
print(f"{num} is not a prime number.")
import os
# Path IN which we have to count files and directories
PATH = 'E:\elements' # Give your path here
fileCount = 0
dirCount = 0
for root, dirs, files in os.walk(PATH):
print('Looking in:',root)
for directories in dirs:
dirCount += 1
for Files in files:
fileCount += 1
#clcoding.com
print('Number of files',fileCount)
print('Number of Directories',dirCount)
print('Total:',(dirCount + fileCount))
Uses of Python
Python variables and input
Python Decisions and Looping
Learning Python gives the programmer a wide variety of career paths to choose from. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. Learning Python allows the programmer to focus on solving problems, rather than focusing on syntax. Its relative size and simplified syntax give it an edge over languages like Java and C++, yet the abundance of libraries gives it the power needed to accomplish great things.
In this tutorial you will create a guessing game application that pits the computer against the user. You will create variables, decision constructs, and loops in python to create the game.
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Identify and use correct syntax when writing SQL retrieval queries.
Receive training from industry experts
Gain hands-on experience solving real-world job tasks
Build confidence using the latest tools and technologies
In this project you will learn to identify and use correct syntax when writing SQL retrieval queries. Through hands-on activities in SQLiteStudio, you will gain experience with the SQL syntax used to display specific columns, filter for specific rows, and determine the sequence of those columns and rows in query output. Familiarity with SQL syntax is a marketable skill for both Information Technology professionals and non-IT super users.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
•Describe basic SQL syntax rules along with requirements for the names of tables, and the naming and data type requirements for table columns (fields).
•Use the SELECT command and FROM clause to control the columns that are displayed and the order in which they display in query output.
•Limit the rows retrieved by an SQL query by applying the WHERE clause with one or more conditions.
•Use the ORDER BY clause to modify the sort order of the rows returned from an SQL retrieval query.
•Code an SQL retrieval query that uses the JOIN command to retrieve data rows from tables that are related via a common column.
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In Python, the True Boolean value is equivalent to the integer 1, and False is equivalent to 0. Therefore, when you multiply True by any number, it's like multiplying 1 by that number.
In this code: print(True * 10)
QuantStart was founded by Michael Halls-Moore, in 2010, to help junior quantitative analysts
(QAs) find jobs in the tough economic climate. Since then the site has evolved to become a
substantial resource for quantitative finance. The site now concentrates on algorithmic trading,
but also discusses quantitative development, in both Python and C++.
Since March 2010, QuantStart has helped over 200,000 visitors improve their quantitative
finance skills. You can always contact QuantStart by sending an email to mike@quantstart.com.
Successful Algorithmic Trading has been written to teach retail discretionary traders and trading
professionals, with basic programming skills, how to create fully automated profitable and robust
algorithmic trading systems using the Python programming language. The book describes the
nature of an algorithmic trading system, how to obtain and organise financial data, the concept of backtesting and how to implement an execution system. The book is designed to be
extremely practical, with liberal examples of Python code throughout the book to demonstrate
the principles and practice of algorithmic trading.
This book has been written for both retail traders and professional quants who have some basic
exposure to programming and wish to learn how to apply modern languages and libraries to
algorithmic trading. It is designed for those who enjoy self-study and can learn by example. The
book is aimed at individuals interested in actual programming and implementation, as I believe
that real success in algorithmic trading comes from fully understanding the implementation
details.
Professional quantitative traders will also find the content useful. Exposure to new libraries
and implementation methods may lead to more optimal execution or more accurate backtesting.
The book is relatively self-contained, but does assume a familiarity with the basics of trading in
a discretionary setting. The book does not require an extensive programming background, but
basic familiarity with a programming language is assumed. You should be aware of elementary
programming concepts such as variable declaration, flow-control (if-else) and looping (for/while).
Some of the trading strategies make use of statistical machine learning techniques. In addition, the portfolio/strategy optimisation sections make extensive use of search and optimization.
PDF Link - successful algorithmic trading halls-moore
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.
Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
Short, focused chapters progress in complexity, easing students into difficult concepts
Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
Streamlined presentation separates critical ideas from background context and extraneous detail
Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible
Programming exercises offered in accompanying Python Notebooks
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
Buy - Understanding Machine Learning: From Theory to Algorithms
PDF Link - Understanding Machine Learning: From Theory to Algorithms
import turtle
s = turtle.Screen()
t = turtle.Turtle()
def move_to(x,y):
t.penup()
t.goto(x,y)
t.pendown()
def draw_rectangle(a,b):
t.begin_fill()
t.forward(a)
t.left(90)
t.forward(b)
t.left(90)
t.forward(a)
t.left(90)
t.forward(b)
t.end_fill()
t.speed(10)
t.color("red")
move_to(-500,200)
draw_rectangle(10,100)
move_to(-490,250)
t.left(90)
draw_rectangle(80,10)
move_to(-410,200)
t.left(90)
draw_rectangle(10,100)
move_to(-380,200)
t.left(60)
t.color("yellow")
draw_rectangle(10,122)
move_to(-275,198)
t.left(145)
draw_rectangle(10,110)
move_to(-350,230)
t.left(335)
draw_rectangle(10,63)
move_to(-240,198)
t.left(270)
t.color("green")
draw_rectangle(100,10)
move_to(-240,288)
draw_rectangle(50,10)
move_to(-190,288)
draw_rectangle(40,10)
move_to(-190,248)
draw_rectangle(50,10)
move_to(-160,198)
t.color("violet")
draw_rectangle(100,10)
move_to(-160,288)
draw_rectangle(50,10)
move_to(-110,288)
draw_rectangle(40,10)
move_to(-110,248)
draw_rectangle(50,10)
move_to(-80,198)
t.left(320)
t.color("orange")
draw_rectangle(120,10)
move_to(-100,295)
draw_rectangle(68,10)
move_to(-500,80)
t.left(40)
t.color("blue")
draw_rectangle(100,10)
t.left(180)
move_to(-490,70)
draw_rectangle(50,10)
t.left(90)
t.fd(50)
t.right(90)
draw_rectangle(80,10)
t.left(90)
t.fd(80)
t.left(270)
draw_rectangle(50,10)
move_to(-400,80)
t.left(180)
t.color("pink")
draw_rectangle(100,10)
move_to(-220,70)
t.left(60)
t.color("brown")
draw_rectangle(100,10)
t.left(300)
move_to(-370,70)
t.right(152)
draw_rectangle(100,10)
t.left(152)
move_to(-290,10)
t.right(45)
draw_rectangle(40,10)
t.right(3)
draw_rectangle(40,10)
move_to(-200,-15)
t.left(200)
t.color("darkgreen")
draw_rectangle(10,110)
move_to(-95,-20)
t.left(145)
draw_rectangle(10,114)
move_to(-175,25)
t.left(333)
draw_rectangle(10,63)
move_to(-70,79)
t.left(90)
t.color("skyblue")
draw_rectangle(101,10)
move_to(-60,-22)
t.left(180)
draw_rectangle(80,10)
move_to(50,-22)
t.left(180)
t.color("black")
draw_rectangle(100,10)
move_to(300,150)
t.color("red")
angle=0
for i in range(20):
t.fd(50)
move_to(300,150)
angle+=18
t.left(angle)
move_to(450,150)
t.color("blue")
angle=0
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(450,150)
move_to(375,300)
t.color("green")
angle=0
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(375,300)
move_to(375,-300)
t.color("black")
angle=0
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(375,-300)
move_to(150,-150)
t.color("violet")
angle=0
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(150,-150)
move_to(450,-150)
t.color("brown")
angle=0
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(450,-150)
move_to(-200,-300)
t.color("green")
angle=0
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(-200,-300)
move_to(125,0)
t.color("yellow")
angle=0
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(125,0)
t.color("pink")
angle=0
move_to(-100,-200)
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(-100,-200)
t.color("lightgreen")
angle=0
move_to(300,0)
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(300,0)
t.color("skyblue")
angle=0
move_to(-500,-240)
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(-500,-240)
t.color("orange")
angle=0
move_to(-350,-170)
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(-350,-170)
t.color("black")
angle=0
move_to(500,20)
for i in range(20):
t.fd(50)
angle+=18
t.left(angle)
move_to(500,20)
#clcoding.com
Master data analysis and manipulation in Pandas and Python
Define and manipulate Pandas Series
Master Pandas Attributes, methods and math operations
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In Python, when you multiply a boolean value by an integer, the boolean value is implicitly converted to an integer. In this case, False is equivalent to 0, so False * 10 will result in 0.
If you run the following code: print(False * 10)
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.
Use Scikit-learn to track an example ML project end to end
Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
PDF Link -
How to solve a partial differential equation using the finite-difference, the pseudospectral, or the linear (spectral) finite-element method.
Understanding the limits of explicit space-time simulations due to the stability criterion and spatial and temporal sampling requirements.
Strategies how to plan and setup sophisticated simulation tasks.
Strategies how to avoid errors in simulation results.
Interested in learning how to solve partial differential equations with numerical methods and how to turn them into python codes? This course provides you with a basic introduction how to apply methods like the finite-difference method, the pseudospectral method, the linear and spectral element method to the 1D (or 2D) scalar wave equation. The mathematical derivation of the computational algorithm is accompanied by python codes embedded in Jupyter notebooks. In a unique setup you can see how the mathematical equations are transformed to a computer code and the results visualized. The emphasis is on illustrating the fundamental mathematical ingredients of the various numerical methods (e.g., Taylor series, Fourier series, differentiation, function interpolation, numerical integration) and how they compare. You will be provided with strategies how to ensure your solutions are correct, for example benchmarking with analytical solutions or convergence tests. The mathematical aspects are complemented by a basic introduction to wave physics, discretization, meshes, parallel programming, computing models.
The course targets anyone who aims at developing or using numerical methods applied to partial differential equations and is seeking a practical introduction at a basic level. The methodologies discussed are widely used in natural sciences, engineering, as well as economics and other fields.
#!/usr/bin/env python
# coding: utf-8
# # 20 extremely useful single-line Python codes
# # 1. Swap Variables:
# In[ ]:
a, b = b, a
# # 2. Find Maximum Element in a List:
# In[ ]:
max_element = max(lst)
# # 3. Find Minimum Element in a List:
# In[ ]:
min_element = min(lst)
# # 4. List Comprehension:
# In[ ]:
squared_numbers = [x**2 for x in range(10)]
# # 5. Filter List Elements:
# In[ ]:
even_numbers = list(filter(lambda x: x % 2 == 0, lst))
# # 6. Map Function:
# In[ ]:
doubled_numbers = list(map(lambda x: x * 2, lst))
# # 7. Sum of List Elements:
# In[ ]:
total = sum(lst)
# # 8. Check if All Elements in a List are True:
# In[ ]:
all_true = all(lst)
# # 9. Check if Any Element in a List is True:
# In[ ]:
any_true = any(lst)
# # 10. Count Occurrences of an Element in a List:
# In[ ]:
count = lst.count(element)
# # 11. Reverse a String:
# In[ ]:
reversed_str = my_str[::-1]
# # 12. Read a File into a List of Lines:
# In[ ]:
lines = [line.strip() for line in open('file.txt')]
# # 13. Inline If-Else:
# In[ ]:
result = "even" if x % 2 == 0 else "odd"
# # 14. Flatten a Nested List:
# In[ ]:
flat_list = [item for sublist in nested_list for item in sublist]
# # 15. Find the Factorial of a Number:
# In[ ]:
factorial = 1 if n == 0 else functools.reduce(lambda x, y: x * y, range(1, n+1))
# # 16. List Unique Elements:
# In[ ]:
unique_elements = list(set(lst))
# # 17. Execute a Function for Each Element in a List:
# In[ ]:
result = list(map(lambda x: my_function(x), lst))
# # 18. Calculate the Average of a List:
# In[ ]:
average = sum(lst) / len(lst) if len(lst) > 0 else 0
# # 19. Convert a String to a List of Characters:
# In[ ]:
char_list = list("hello")
# # 20. Find Common Elements Between Two Lists:
# In[ ]:
common_elements = list(set(lst1) & set(lst2))
# In[ ]:
Stacks and queues are fundamental data structures used in computer science to manage and organize data. Let's get started with stacks and queues in Python.
A stack is a Last In, First Out (LIFO) data structure, where the last element added is the first one to be removed. Think of it like a stack of plates - you can only take the top plate off.
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Install Python and write your first program
Describe the basics of the Python programming language
Use variables to store, retrieve and calculate information
Utilize core programming tools such as functions and loops
This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.
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