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 IBM
Python Coding March 06, 2024 AI, Data Science, IBM No comments
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 IBM
Python Coding March 06, 2024 Data Science, IBM No comments
Understand the value of data and how the rapid growth of technologies such as artificial intelligence and cloud computing are transforming business.
Python Coding March 06, 2024 Data Science, IBM, Python, SQL No comments
Working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio
Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy
Statistical Analysis techniques including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression
Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables
Python Coding March 06, 2024 Books, Coursera, Data Science No comments
Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists
Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio
Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems
Write SQL statements and query Cloud databases using Python from Jupyter notebooks
Python Coding March 06, 2024 Python Coding Challenge No comments
This code defines a function named g1 that takes two parameters: x and d with a default value of an empty dictionary {}. The function updates the dictionary d by setting the key x to the value x and then returns the updated dictionary.
Here's a step-by-step explanation of the code:
def g1(x, d={}):: This line defines a function g1 with two parameters, x and d. The parameter d has a default value of an empty dictionary {}.
d[x] = x: This line updates the dictionary d by assigning the value of x to the key x. This essentially adds or updates the key-value pair in the dictionary.
return d: The function returns the updated dictionary d.
print(g1(5)): This line calls the function g1 with the argument 5. Since no value is provided for the d parameter, it uses the default empty dictionary {}. The dictionary is then updated to include the key-value pair 5: 5. The function returns the updated dictionary, and it is printed.
The output of print(g1(5)) will be:
{5: 5}
It's important to note that the default dictionary is shared across multiple calls to the function if no explicit dictionary is provided. This can lead to unexpected behavior, so caution should be exercised when using mutable default values in function parameters.
Python Coding March 06, 2024 Books, Machine Learning No comments
Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems
Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain
The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.
The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift.
Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.
With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you’re not a developer but want to manage or understand the product lifecycle of these systems, you’ll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.
Python Coding March 06, 2024 Python No comments
1. Precision Issues:
x = 1.0
y = 1e-16
result = x + y
print(result)
#clcoding.com
1.0
2. Comparing Floating-Point Numbers:
a = 0.1 + 0.2
b = 0.3
print(a == b)
#clcoding.com
False
3. NaN (Not a Number) and Inf (Infinity):
result = float('inf') / float('inf')
print(result)
#clcoding.com
nan
4. Large Integers:
result = 2 ** 1000
print(result)
#clcoding.com
10715086071862673209484250490600018105614048117055336074437503883703510511249361224931983788156958581275946729175531468251871452856923140435984577574698574803934567774824230985421074605062371141877954182153046474983581941267398767559165543946077062914571196477686542167660429831652624386837205668069376
5. Round-off Errors:
result = 0.1 + 0.2 - 0.3
print(result)
#clcoding.com
5.551115123125783e-17
Python Coding March 05, 2024 Python Coding Challenge No comments
In Python, the expressions within curly braces {} are evaluated as set literals. However, the expressions 1 and 2 and 1 or 3 are not directly used to create sets. Instead, these expressions are evaluated as boolean logic expressions and the final results are used to create sets.
Let's break down the expressions:
s1 = {1 and 2}
s2 = {1 or 3}
result = s1 ^ s2
print(result)
1 and 2: This expression evaluates to 2 because and returns the last truthy value (2 is the last truthy value in the expression).
1 or 3: This expression evaluates to 1 because or returns the first truthy value (1 is the first truthy value in the expression).
Therefore, your sets become:
s1 = {2}
s2 = {1}
result = s1 ^ s2
print(result)
Output:
{1, 2}
In this example, the ^ (symmetric difference) operator results in a set containing elements that are unique to each set ({1, 2}).
Python Coding March 05, 2024 Books, Python, SQL No comments
Are you poised to elevate your technical expertise and stay ahead in the rapidly evolving world of data and programming?
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Our 5 Books Series is meticulously crafted to guide you from the basics to the most advanced concepts in Python and SQL, making it a must-have for database enthusiasts, aspiring data scientists, and seasoned coders alike.
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Synergizing Code and Data: Explore the synergy between Python and SQL Server Development, mastering techniques from executing SQL queries through Python to advanced data manipulation.
Python and SQL for Data Solutions: Uncover the powerful combination of Python and SQL for data analysis, reporting, and integration, including ETL processes and machine learning applications.
Advanced Data Solutions: Delve into integrating Python and SQL for data retrieval, manipulation, and performance optimization.
Integrating Python and SQL: Master database manipulation, focusing on crafting SQL queries in Python and implementing security best practices.
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Embark on this comprehensive journey to mastering Python and SQL. With our series, you'll transform your career, opening doors to new opportunities and achieving data excellence.
Python Coding March 05, 2024 Books, data management, Machine Learning, Python No comments
"Finance with Rust" is a pioneering guide that introduces financial professionals and software developers to the transformative power of Rust in the financial industry. With its emphasis on speed, safety, and concurrency, Rust presents an unprecedented opportunity to enhance financial systems and applications.
Written by an accomplished software developer and entrepreneur, this book bridges the gap between complex financial processes and cutting-edge technology. It offers a comprehensive exploration of Rust's application in finance, from developing faster algorithms to ensuring data security and system reliability.
An introduction to Rust for those new to the language, focusing on its relevance and benefits in financial applications.
Step-by-step guides on using Rust to build scalable and secure financial models, algorithms, and infrastructure.
Case studies demonstrating the successful integration of Rust in financial systems, highlighting its impact on performance and security.
Practical insights into leveraging Rust for financial innovation, including blockchain technology, cryptocurrency platforms, and more.
"Finance with Rust" empowers you to stay ahead in the fast-evolving world of financial technology. Whether you're aiming to optimize financial operations, develop high-performance trading systems, or innovate with blockchain and crypto technologies, this book is your essential roadmap to success.
Python Coding March 05, 2024 Books, Python No comments
Learn Python Programming Fast - A Beginner's Guide to Mastering Python from Home
Grab the Bonus Chapter Inside with 50 Coding Journal
Python is the most in-demand programming language in 2024. As a beginner, learning Python can open up high-paying remote and freelance job opportunities in fields like data science, web development, AI, and more.
This hands-on Python Programming is designed specifically for beginners with no prior coding experience. It provides a foundations-first introduction to Python programming concepts using simplified explanations, practical examples, and step-by-step tutorials.
Programming is best learned by doing, and thus, this book incorporates numerous practical exercises and real-world projects.
This is not Hype; you will learn something new in this Python Programming for Beginners.
What You Will Learn in this Python Programming for Beginners Book:
Python Installation - How to download Python and set up your coding environment
Python Syntax - Key programming constructs like variables, data types, functions, conditionals and loops
Core Programming Techniques - Best practices for writing clean, efficient Python code
Built-in Data Structures - Hands-on projects using Python lists, tuples, dictionaries and more
Object-Oriented Programming - How to work with classes, objects and inheritance in Python
Python for Web Development - Build a web app and API with Python frameworks like Django and Flask
Python for Data Analysis - Use Python for data science and work with Jupyter Notebooks
Python for Machine Learning - Implement machine learning algorithms for prediction and classification
Bonus: Python Coding Interview Questions - Practice questions and answers to prepare for the interview
This beginner-friendly guide will give you a solid foundation in Python to build real-world apps and land your first Python developer job.
Python Coding March 05, 2024 Books, Data Science, Python No comments
In the rapidly evolving landscape of economics, "Econometric Python" emerges as a groundbreaking guide, perfectly blending the intricate world of econometrics with the dynamic capabilities of Python. This book is crafted for economists, data scientists, researchers, and students who aspire to revolutionize their approach to economic data analysis.
At its center, "Econometric Python" serves as a beacon for those navigating the complexities of econometric models, offering a unique perspective on applying Python's powerful data science tools in economic research. The book starts with a fundamental introduction to Python, focusing on aspects most relevant to econometric analysis. This makes it an invaluable resource for both Python novices and seasoned programmers.
As the narrative unfolds, readers are led through a series of progressively complex econometric techniques, all demonstrated with Python's state-of-the-art libraries such as pandas, NumPy, and statsmodels. Each chapter is meticulously designed to balance theory and practice, providing in-depth explanations of econometric concepts, followed by practical coding examples.
Key features of "Econometric Python" include:
Comprehensive Coverage: From basic economic concepts to advanced econometric models, the book covers a wide array of topics, ensuring a thorough understanding of both theoretical and practical aspects.
Hands-On Approach: With real-world datasets and step-by-step coding tutorials, readers gain hands-on experience in applying econometric theories using Python.
Latest Trends and Techniques: Stay abreast of the latest developments in both econometrics and Python programming, including machine learning applications in economic data analysis.
Expert Insights: The authors, renowned in the fields of economics and data science, provide valuable insights and tips, enhancing the learning experience.
"Econometric Python" is more than just a textbook; it's a journey into the future of economic analysis. By the end of this book, readers will not only be proficient in using Python for econometric analysis but will also be equipped with the skills to contribute innovatively to the field of economics. Whether it's for academic purposes, professional development, or personal interest, this book is an indispensable asset for anyone looking to merge the power of data science with economic analysis.
Python Coding March 05, 2024 Books, Data Science, Machine Learning, Python No comments
Uncover the energy of records in 30 days with Python Data Science 2024!
Are you searching for a hands-on strategy to study Python coding and Python for Data Analysis fast?
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On the program:
Deep mastering
Neural Networks and Deep Learning
Deep Learning Parameters and Hyper-parameters
Deep Neural Networks Layers
Deep Learning Activation Functions
Convolutional Neural Network
Python Data Structures
Best practices in Python and Zen of Python
Installing Python
Python
These are some of the subjects included in this book:
Fundamentals of deep learning
Fundamentals of probability
Fundamentals of statistics
Fundamentals of linear algebra
Introduction to desktop gaining knowledge of and deep learning
Fundamentals of computer learning
Deep gaining knowledge of parameters and hyper-parameters
Deep neural networks layers
Deep getting to know activation functions
Convolutional neural network
Deep mastering in exercise (in jupyter notebooks)
Python information structures
Best practices in python and zen of Python
Installing Python
At the cease of this course, you may be in a position to:
Confidently deal with real-world datasets.
Wrangle, analyze, and visualize facts the usage of Python.
Turn records into actionable insights and knowledgeable decisions.
Speak the language of data-driven professionals.
Lay the basis for in addition studying in statistics science and computing device learning.
Python Coding March 03, 2024 Python Coding Challenge No comments
Example 1: Slicing a List
# Slicing a list
numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# Get elements from index 2 to 5 (exclusive)
subset = numbers[2:5]
print(subset) # Output: [2, 3, 4]
#clcoding.com
[2, 3, 4]
Example 2: Omitting Start and End Indices
# Omitting start and end indices
subset = numbers[:7] # From the beginning to index 6
print(subset) # Output: [0, 1, 2, 3, 4, 5, 6]
subset = numbers[3:] # From index 3 to the end
print(subset) # Output: [3, 4, 5, 6, 7, 8, 9]
#clcoding.com
[0, 1, 2, 3, 4, 5, 6]
[3, 4, 5, 6, 7, 8, 9]
Example 3: Using Negative Indices
# Using negative indices
subset = numbers[-4:-1]
print(subset)
#clcoding.com
[6, 7, 8]
Example 4: Slicing a String
# Slicing a string
text = "Hello, Python!"
# Get the substring "Python"
substring = text[7:13]
print(substring) # Output: Python
#clcoding.com
Python
Example 5: Step in Slicing
# Step in slicing
even_numbers = numbers[2:10:2]
print(even_numbers)
#clcoding.com
[2, 4, 6, 8]
Example 6: Slicing with Stride
# Slicing with stride
reverse_numbers = numbers[::-1]
print(reverse_numbers)
#clcoding.com
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
Example 7: Slicing a Tuple
# Slicing a tuple
my_tuple = (1, 2, 3, 4, 5)
# Get a sub-tuple from index 1 to 3
subset_tuple = my_tuple[1:4]
print(subset_tuple) # Output: (2, 3, 4)
#clcoding.com
(2, 3, 4)
Example 8: Modifying a List with Slicing
# Modifying a list with slicing
letters = ['a', 'b', 'c', 'd', 'e']
# Replace elements from index 1 to 3
letters[1:4] = ['x', 'y', 'z']
print(letters)
#clcoding.com
['a', 'x', 'y', 'z', 'e']
Python Coding March 02, 2024 Python Coding Challenge No comments
The above code defines a string variable my_string with the value "hello, world!" and then extracts a substring from index 2 to 6 (7 is exclusive) using slicing. Finally, it prints the extracted substring. Here's the breakdown:
my_string = "hello, world!"
substring = my_string[2:7]
print(substring)
Output:
llo,
In Python, string indexing starts from 0, so my_string[2] is the third character, which is "l", and my_string[7] is the eighth character, which is the space after the comma. Therefore, the substring "llo, " is extracted and printed.
Python Coding March 02, 2024 Coursera, IBM, Python No comments
Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data
Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy
Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making
Python Coding March 02, 2024 Coursera, Python No comments
Explain how Python is used by data professionals
Explore basic Python building blocks, including syntax and semantics
Understand loops, control statements, and string manipulation
Use data structures to store and organize data
There are 5 modules in this course
This is the second of seven courses in the Google Advanced Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
By the end of this course, you will:
-Define what a programming language is and why Python is used by data scientists
-Create Python scripts to display data and perform operations
-Control the flow of programs using conditions and functions
-Utilize different types of loops when performing repeated operations
-Identify data types such as integers, floats, strings, and booleans
-Manipulate data structures such as , lists, tuples, dictionaries, and sets
-Import and use Python libraries such as NumPy and pandas
Python Coding March 02, 2024 Python Coding Challenge No comments
Example 1: Basic Usage of zip
# Basic usage of zip
names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
# Combining lists using zip
combined_data = zip(names, ages)
# Displaying the result
for name, age in combined_data:
print(f"Name: {name}, Age: {age}")
#clcoding.com
Name: Alice, Age: 25
Name: Bob, Age: 30
Name: Charlie, Age: 35
Example 2: Different Lengths in Input Iterables
# Zip with different lengths in input iterables
names = ["Alice", "Bob", "Charlie"]
ages = [25, 30]
# Using zip with different lengths will stop at the shortest iterable
combined_data = zip(names, ages)
# Displaying the result
for name, age in combined_data:
print(f"Name: {name}, Age: {age}")
#clcoding.com
Name: Alice, Age: 25
Name: Bob, Age: 30
Example 3: Unzipping with zip
# Unzipping with zip
names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
# Combining lists using zip
combined_data = zip(names, ages)
# Unzipping the result
unzipped_names, unzipped_ages = zip(*combined_data)
# Displaying the unzipped data
print("Unzipped Names:", unzipped_names)
print("Unzipped Ages:", unzipped_ages)
#clcoding.com
Unzipped Names: ('Alice', 'Bob', 'Charlie')
Unzipped Ages: (25, 30, 35)
Example 4: Using zip with Dictionaries
# Using zip with dictionaries
keys = ["name", "age", "city"]
values = ["Alice", 25, "New York"]
# Creating a dictionary using zip
person_dict = dict(zip(keys, values))
# Displaying the dictionary
print(person_dict)
#clcoding.com
{'name': 'Alice', 'age': 25, 'city': 'New York'}
Example 5: Transposing a Matrix with zip
# Transposing a matrix using zip
matrix = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
# Using zip to transpose the matrix
transposed_matrix = list(zip(*matrix))
# Displaying the transposed matrix
for row in transposed_matrix:
print(row)
#clcoding.com
(1, 4, 7)
(2, 5, 8)
(3, 6, 9)
Example 6: Using zip with enumerate
# Using zip with enumerate
names = ["Alice", "Bob", "Charlie"]
# Combining with enumerate to get both index and value
indexed_names = list(zip(range(len(names)), names))
# Displaying the result
for index, name in indexed_names:
print(f"Index: {index}, Name: {name}")
#clcoding.com
Index: 0, Name: Alice
Index: 1, Name: Bob
Index: 2, Name: Charlie
Python Coding March 02, 2024 Python Coding Challenge No comments
Let's break down the code:
x = 5
y = 2
x *= -y
print(x, y)
Here's what happens step by step:
x is initially assigned the value 5.
y is initially assigned the value 2.
x *= -y is equivalent to x = x * -y, which means multiplying the current value of x by -y and assigning the result back to x.
Therefore, x becomes 5 * -2, resulting in x being updated to -10.
The print(x, y) statement prints the current values of x and y.
So, the output of this code will be:
-10 2
Python Coding February 29, 2024 Python Coding Challenge No comments
Let's break down the code step by step:
Function Definition:
def custom_function(b):
This line defines a function named custom_function that takes a parameter b.
Conditional Statements:
if b < 0:
return 20
This block checks if the value of b is less than 0. If it is, the function returns the integer 20.
if b == 0:
return 20.0
This block checks if the value of b is equal to 0. If it is, the function returns the floating-point number 20.0.
if b > 0:
return '20'
This block checks if the value of b is greater than 0. If it is, the function returns the string '20'.
Function Call:
print(custom_function(-3))
This line calls the custom_function with the argument -3 and prints the result.
Output Explanation:
The argument is -3, which is less than 0. Therefore, the first condition is true.
The function returns the integer 20.
The print statement then outputs 20.
So, the output of the provided code will be:
20
This is because the function returns the integer 20 when the input is less than 0.
Python Coding February 29, 2024 Books, Machine Learning, Python No comments
This course is part of the Probabilistic Graphical Models 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
Python Coding February 29, 2024 Books, Python No comments
This course is part of the Probabilistic Graphical Models 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
Python Coding February 29, 2024 Books, Coursera, Python No comments
This course is part of the Probabilistic Graphical Models 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
Python Coding February 29, 2024 AI, Books, Coursera, Python No comments
Principles and practical considerations for integrating AI into clinical workflows
Best practices of AI applications to promote fair and equitable healthcare solutions
Challenges of regulation of AI applications and which components of a model can be regulated
What standard evaluation metrics do and do not provide
Python Coding February 29, 2024 Books, Machine Learning, Python No comments
Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.
Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.
Learn important approaches for leveraging data to train, validate, and test machine learning models.
Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.
Python Coding February 27, 2024 Books, Data Science, Python No comments
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
IPython and Jupyter provide computational environments for scientists using Python
NumPy includes the ndarray for efficient storage and manipulation of dense data arrays
Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data
Matplotlib includes capabilities for a flexible range of data visualizations
Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Python Coding February 27, 2024 Books, Data Science, Python No comments
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.
This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.
Applies a modern, computational approach to working with data
Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues
Teaches the fundamentals of some of the most important tools in the Python data-science stack
Provides a basic, but rigorous, introduction to Probability and its application to Statistics
Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
Python Coding February 27, 2024 Books, Data Science, Python No comments
Unlock the power of Python to analyze data, uncover insights, and drive decision-making with "Python for Data Analysis: From Basics to Advanced Data Science Techniques" Whether you're new to data analysis or looking to enhance your skills, this book offers a comprehensive journey through the tools, techniques, and concepts that make Python the go-to choice for data professionals.
Foundational Python: Start from the basics of Python programming, including setting up your environment, understanding Python syntax, and exploring core concepts.
Mastering Pandas for Data Manipulation: Dive deep into Pandas for data cleaning, preparation, and manipulation, empowering you to handle and explore real-world datasets with ease.
Data Visualization Techniques: Learn to communicate your findings visually with Matplotlib and Seaborn, creating compelling and informative plots that bring your data to life.
Machine Learning Integration: Step into the world of machine learning with Scikit-Learn to apply predictive models to your data, from basic classification to complex regression tasks.
Advanced Data Analysis: Explore advanced topics, including working with big data using Dask, natural language processing (NLP), and an introduction to deep learning with TensorFlow and Keras.
Practical Projects and Case Studies: Apply what you've learned with hands-on projects and case studies that simulate real-world data analysis scenarios, enhancing your problem-solving skills and practical knowledge.
Future of Data Analysis: Look ahead to the emerging trends in data analysis and the ethical considerations of working with data, preparing you for the future of the field.
"Python for Data Analysis: From Basics to Advanced Data Science Techniques" is more than just a book; it's a comprehensive guide to becoming proficient in data analysis using Python. With clear explanations, practical examples, and step-by-step instructions, this book will equip you with the knowledge and skills you need to navigate the data landscape confidently and become an invaluable asset in your organization or field.
Python Coding February 27, 2024 Books, Data Science, Python No comments
A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You’ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.
You will discover Python’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
Python Coding February 27, 2024 aws, book, Data Science No comments
Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines
Stay up to date with a comprehensive revised chapter on Data Governance
Build modern data platforms with a new section covering transactional data lakes and data mesh
This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.
You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS.
By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!
Seamlessly ingest streaming data with Amazon Kinesis Data Firehose
Optimize, denormalize, and join datasets with AWS Glue Studio
Use Amazon S3 events to trigger a Lambda process to transform a file
Load data into a Redshift data warehouse and run queries with ease
Visualize and explore data using Amazon QuickSight
Extract sentiment data from a dataset using Amazon Comprehend
Build transactional data lakes using Apache Iceberg with Amazon Athena
Learn how a data mesh approach can be implemented on AWS
This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.
An Introduction to Data Engineering
Data Management Architectures for Analytics
The AWS Data Engineer’s Toolkit
Data Governance, Security, and Cataloging
Architecting Data Engineering Pipelines
Ingesting Batch and Streaming Data
Transforming Data to Optimize for Analytics
Identifying and Enabling Data Consumers
A Deeper Dive into Data Marts and Amazon Redshift
Orchestrating the Data Pipeline
Python Coding February 26, 2024 Python No comments
The above code deletes elements from index 2 to index 3 (not including index 4) in the list num and then prints the updated list. Let's break it down:
num = [10, 20, 30, 40, 50]
This line initializes a list named num with the elements 10, 20, 30, 40, and 50.
del(num[2:4])
This line uses the del statement to delete elements from index 2 up to (but not including) index 4 in the list. So, it removes the elements at index 2 and 3 (30 and 40) from the list.
After this operation, the list num becomes [10, 20, 50].
print(num)
Finally, the code prints the updated list, which is [10, 20, 50].
So, the output of the code will be:
[10, 20, 50]
Python Coding February 26, 2024 Coursera, Data Science, Excel, IBM No comments
Master the most up-to-date practical skills and knowledge data analysts use in their daily roles
Learn how to perform data analysis, including data preparation, statistical analysis, and predictive modeling using R, R Studio, and Jupyter
Utilize Excel spreadsheets to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, & creating charts
Communicate your data findings using various data visualization techniques including, charts, plots & interactive dashboards with Cognos and R Shiny
Python Coding February 26, 2024 Coursera, Data Science No comments
Build simple linear regression models in Python
Apply scikit-learn and statsmodels to regression problems
Employ explorartory data analysis (EDA) with seaborn and pandas
Explain linear regression to both technical and non-technical audiences
Python Coding February 26, 2024 AI, Coursera, Data Science, IBM No comments
Describe how you can use Generative AI tools and techniques in the context of data analytics across industries
Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools
Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights
Analyze the ethical considerations and challenges associated with using Generative AI in data analytics
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