Friday, 8 March 2024

Lambda Functions in Python

 


Example 1: Basic Syntax

# Regular function

def add(x, y):

    return x + y

# Equivalent lambda function

lambda_add = lambda x, y: x + y

# Using both functions

result_regular = add(3, 5)

result_lambda = lambda_add(3, 5)

print("Result (Regular Function):", result_regular)

print("Result (Lambda Function):", result_lambda)

#clcoding.com

Result (Regular Function): 8

Result (Lambda Function): 8

Example 2: Sorting with Lambda

# List of tuples

students = [("Alice", 25), ("Bob", 30), ("Charlie", 22)]

# Sort by age using a lambda function

sorted_students = sorted(students, key=lambda student: student[1])

print("Sorted Students by Age:", sorted_students)

#clcoding.com

Sorted Students by Age: [('Charlie', 22), ('Alice', 25), ('Bob', 30)]

Example 3: Filtering with Lambda

# List of numbers

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9]

# Filter even numbers using a lambda function

even_numbers = list(filter(lambda x: x % 2 == 0, numbers))

print("Even Numbers:", even_numbers)

#clcoding.com

Even Numbers: [2, 4, 6, 8]

Example 4: Mapping with Lambda

# List of numbers

numbers = [1, 2, 3, 4, 5]

# Square each number using a lambda function

squared_numbers = list(map(lambda x: x**2, numbers))

print("Squared Numbers:", squared_numbers)

#clcoding.com

Squared Numbers: [1, 4, 9, 16, 25]

Example 5: Using Lambda with max function

# List of numbers

numbers = [10, 5, 8, 20, 15]

# Find the maximum number using a lambda function

max_number = max(numbers, key=lambda x: -x)  # Use negation for finding the minimum

print("Maximum Number:", max_number)

#clcoding.com

Maximum Number: 5

Example 6: Using Lambda with sorted and Multiple Criteria

# List of dictionaries representing people with names and ages

people = [{"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}, {"name": "Charlie", "age": 22}]

# Sort by age and then by name using a lambda function

sorted_people = sorted(people, key=lambda person: (person["age"], person["name"]))

print("Sorted People:", sorted_people)

#clcoding.com

Sorted People: [{'name': 'Charlie', 'age': 22}, {'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]

Example 7: Using Lambda with reduce from functools

from functools import reduce

# List of numbers

numbers = [1, 2, 3, 4, 5]

# Calculate the product of all numbers using a lambda function and reduce

product = reduce(lambda x, y: x * y, numbers)

print("Product of Numbers:", product)

#clcoding.com

Product of Numbers: 120

Example 8: Using Lambda with Conditional Expressions

# List of numbers

numbers = [10, 5, 8, 20, 15]

# Use a lambda function with a conditional expression to filter and square even numbers

filtered_and_squared = list(map(lambda x: x**2 if x % 2 == 0 else x, numbers))

print("Filtered and Squared Numbers:", filtered_and_squared)

#clcoding.com

Filtered and Squared Numbers: [100, 5, 64, 400, 15]

Example 9: Using Lambda with key in max and min to Find Extremes

# List of tuples representing products with names and prices

products = [("Laptop", 1200), ("Phone", 800), ("Tablet", 500), ("Smartwatch", 200)]

# Find the most and least expensive products using lambda functions

most_expensive = max(products, key=lambda item: item[1])

least_expensive = min(products, key=lambda item: item[1])

print("Most Expensive Product:", most_expensive)

print("Least Expensive Product:", least_expensive)

#clcoding.com

Most Expensive Product: ('Laptop', 1200)

Least Expensive Product: ('Smartwatch', 200)

Python Coding challenge - Day 144 | What is the output of the following Python Code?

 

The code print(()*3) in Python will print an empty tuple three times.

Let's break down the code:

print(): This is a built-in function in Python used to output messages to the console.

(): This represents an empty tuple. A tuple is an ordered collection of items similar to a list, but unlike lists, tuples are immutable, meaning their elements cannot be changed after creation.

*3: This is the unpacking operator. In this context, it unpacks the empty tuple three times.

Since an empty tuple by itself doesn't contain any elements to print, it essentially prints nothing three times. So the output of this code will be an empty line repeated three times.

Fractal Data Science Professional Certificate

 


What you'll learn

 Apply structured problem-solving techniques to dissect and address complex data-related challenges encountered in real-world scenarios.   

Utilize SQL proficiency to retrieve, manipulate data and employ data visualization skills using Power BI to communicate insights.

Apply Python expertise for data manipulation, analysis and implement machine learning algorithms to create predictive models for applications.

Create compelling data stories to influence your audience and master the art of critically analyzing data while making decisions and recommendations.

Join Free: Fractal Data Science Professional Certificate

Professional Certificate - 8 course series

Data science is projected to create 11.5 1 million global job openings by 2026 and offers many of the remote 2 job opportunities in the industry.

Prepare for a new career in this high-demand field with a Professional Certificate from Fractal Analytics. Whether you're a recent graduate seeking a rewarding career shift or a professional aiming to upskill, this program will equip you with the essential skills demanded by the industry.

This curriculum is designed with a problem-solving approach at the center to equip and enable you with the skills, required to solve data science problems, instead of just focusing on the tools and applications.

Through hands-on courses you'll master Python programming, harness the power of machine learning, cultivate expertise in data manipulation, and build understanding of cognitive factors affecting decisions. You will also learn the direct application of tools like SQL, PowerBI, and Python to real-world scenarios.

Upon completion, you will earn a Professional Certificate, which will help to make your profile standout in your career journey.

Fractal Data Science Professional Certificate is one of the preferred qualifications for entry-level data science jobs at Fractal. Complete this certificate to make your profile standout from other candidates while applying for job openings at Fractal.

Applied Learning Project

Learners will be able to apply structured problem-solving techniques to dissect and address complex data-related challenges encountered in real-world scenarios and utilize SQL proficiency to retrieve and manipulate data and employ data visualization skills using Power BI to communicate insights. Becoming experts at Python programming to manipulate and analyze data. Learners will implement machine learning algorithms to create predictive models for diverse applications. And create compelling data stories to influence and inform your audience and master the art of critically analyzing data while making decisions and recommendations.

CertNexus Certified Data Science Practitioner Professional Certificate

 


Advance your career with in-demand skills

Receive professional-level training from CertNexus

Demonstrate your technical proficiency

Earn an employer-recognized certificate from CertNexus

Prepare for an industry certification exam

Join Free: CertNexus Certified Data Science Practitioner Professional Certificate

Professional Certificate - 5 course series

The field of Data Science has topped the Linked In Emerging Jobs list for the last 3 years with a projected growth of 28% annually and the World Economic Forum lists Data Analytics and Scientists as the top emerging job for 2022. 

Data can reveal insights and inform business—by guiding decisions and influencing day-to-day operations. This specialization will teach learners how to analyze, understand, manipulate, and present data within an effective and repeatable process framework and will enable you to bring value to the business by putting data science concepts into practice. 

This course is designed for business professionals that want to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.

Certified Data Science Practitioner (CDSP)  will prepare learners for the CertNexus CDSP certification exam. 

To complete your journey to the CDSP Certification

Complete the Coursera Certified Data Science Practitioner Professional Certificate.

Review the CDSP Exam Blueprint
.

Purchase your CDSP Exam Voucher

Register for your CDSP Exam.

Applied Learning Project

At the conclusion of each course, learners will have the opportunity to complete a project which can be added to their portfolio of work.  Projects include: 

Address a Business Issue with Data Science 

Extract, Transform, and Load Data

Data Analysis

Training a Machine Learning Model

Presenting a Data Science Project

IBM Data Engineering Professional Certificate

 


What you'll learn

Master the most up-to-date practical skills and knowledge data engineers use in their daily roles

Learn to create, design, & manage relational databases & apply database administration (DBA) concepts to RDBMSs such as MySQL, PostgreSQL, & IBM Db2 

Develop working knowledge of NoSQL & Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, Spark SQL, Spark ML, and Spark Streaming 

Implement ETL & Data Pipelines with Bash, Airflow & Kafka; architect, populate, deploy Data Warehouses; create BI reports & interactive dashboards 

Join Free: IBM Data Engineering Professional Certificate

Professional Certificate - 13 course series

Prepare for a career in the high-growth field of data engineering. In this program, you’ll learn in-demand skills like Python, SQL, and Databases to get job-ready in less than 5 months.

Data engineering is building systems to gather data, process and organize raw data into usable information, and manage data. The work data engineers do provides the foundational information that data scientists and business intelligence (BI) analysts use to make recommendations and decisions.

This program will teach you the foundational data engineering skills employers are seeking for entry level data engineering roles, including Python, one of the most widely used programming languages. You’ll also master SQL, RDBMS, ETL, Data Warehousing, NoSQL, Big Data, and Spark with hands-on labs and projects.

You’ll learn to use Python programming language and Linux/UNIX shell scripts to extract, transform and load (ETL) data. You’ll also work with Relational Databases (RDBMS) and query data using SQL statements and use NoSQL databases as well as unstructured data. 

When you complete the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support. 

This program is ACE® recommended—when you complete, you can earn up to 12 college credits.

Applied Learning Project

Throughout this Professional Certificate, you will complete hands-on labs and projects to help you gain practical experience with Python, SQL, relational databases, NoSQL databases, Apache Spark, building data pipelines, managing databases, and working with data warehouses.

Design a relational database to help a coffee franchise improve operations.

Use SQL to query census, crime, and school demographic data sets.

Write a Bash shell script on Linux that backups changed files.

Set up, test, and optimize a data platform that contains MySQL, PostgreSQL, and IBM Db2 databases.

Analyze road traffic data to perform ETL and create a pipeline using Airflow and Kafka.

Design and implement a data warehouse for a solid-waste management company.

Move, query, and analyze data in MongoDB, Cassandra, and Cloudant NoSQL databases.

Train a machine learning model by creating an Apache Spark application.

This program is FIBAA recommended—when you complete, you can earn up to 8 ECTS credits.

Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate

 


What you'll learn

Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.

Employ BigQuery to carry out interactive data analysis.

Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.

Choose between different data processing products on Google Cloud.

Join Free: Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate 

Professional Certificate - 6 course series

Google Cloud Professional Data Engineer certification was ranked #1 
on Global Knowledge's list of 15 top-paying certifications in 2021
! Enroll now to prepare!

---

87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized
 Google Cloud Professional Data Engineer
 certification.

Here's what you have to do

1) Complete the Coursera Data Engineering Professional Certificate

2) Review other recommended resources for the Google Cloud Professional Data Engineer certification
 exam

3) Review the Professional Data Engineer exam guide

4) Complete Professional Data Engineer sample questions

5)Register for the Google Cloud certification exam (remotely or at a test center)

Applied Learning Project

This professional certificate incorporates hands-on labs using Qwiklabs platform.These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

Applied Learning Project

 This Professional Certificate incorporates hands-on labs using our Qwiklabs platform.

These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google BigQuery, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.

Tableau Business Intelligence Analyst Professional Certificate

 


What you'll learn

Gain the essential skills necessary to excel in an entry-level Business Intelligence Analytics role.

Learn to use Tableau Public to manipulate and prepare data for analysis.

Craft and dissect data visualizations that reveal patterns and drive actionable insights.

Construct captivating narratives through data, enabling stakeholders to explore insights effectively.

Join Free: Tableau Business Intelligence Analyst Professional Certificate

Professional Certificate - 8 course series

Whether you are just starting out or looking for a career change, the Tableau Business Intelligence Analytics Professional Certificate will prepare you for entry-level roles that require fundamental Tableau skills, such as business intelligence analyst roles. If you are detail-oriented and have an interest in looking for trends in data, this program is for you. Through hands-on, real-world scenarios, you learn how to use the Tableau platform to evaluate data to generate and present actionable business insights. Upon completion, you will be prepared to take the 
Tableau Desktop Specialist Exam
.  With this certification, you will be qualified to apply for a position in the business intelligence analyst field.  

In this program, you’ll: 

 Craft problem statements, business requirement documents, and visual models.

 Connect with various data sources and preprocess data in Tableau for enhanced quality and analysis.

 Learn to utilize the benefits of Tableau’s analytics features for efficient reporting.

Learn to create advanced and spatial analytics data visualizations by integrating motion and multi-layer elements to effectively communicate insights to stakeholders.

Employ data storytelling principles and design techniques to craft compelling presentations that empower you to extract meaningful insights.

This program was developed by Tableau experts, designed to prepare you for a career in Business Intelligence Analytics and help you learn the most relevant skills.

Applied Learning Project

 Throughout the program, you’ll engage in applied learning through hands-on activities to help level up your knowledge. Each course contains ungraded quizzes throughout the content, a graded quiz at the end of each module, and a variety of hands-on projects. The program activities will give you the skills to : 

Preprocess, manage, and manipulate data for analysis using Tableau. 

Create and customize Visualizations in Tableau. 

Learn best practices for creating presentations to communicate data analysis insights to stakeholders. 

Thursday, 7 March 2024

Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

 


A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores

Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods

Analyze and extract insights from complex models from CNNs to BERT to time series models

Book Description

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.

Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.

In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.

By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.

What you will learn

Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty

Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers

Use monotonic and interaction constraints to make fairer and safer models

Understand how to mitigate the influence of bias in datasets

Leverage sensitivity analysis factor prioritization and factor fixing for any model

Discover how to make models more reliable with adversarial robustness

Who this book is for

This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.

Table of Contents

Interpretation, Interpretability and Explainability; and why does it all matter?

Key Concepts of Interpretability

Interpretation Challenges

Global Model-agnostic Interpretation Methods

Local Model-agnostic Interpretation Methods

Anchors and Counterfactual Explanations

Visualizing Convolutional Neural Networks

Interpreting NLP Transformers

Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

Feature Selection and Engineering for Interpretability

Bias Mitigation and Causal Inference Methods

Monotonic Constraints and Model Tuning for Interpretability

Adversarial Robustness

What's Next for Machine Learning Interpretability?

Hard Copy: Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

 


Get to grips with the LangChain framework from theory to deployment and develop production-ready applications.

Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments.

Purchase of the print or Kindle book includes a free PDF eBook.

Key Features

Learn how to leverage LLMs' capabilities and work around their inherent weaknesses

Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges

Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality

Book Description

ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications.

Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

What you will learn

Understand LLMs, their strengths and limitations

Grasp generative AI fundamentals and industry trends

Create LLM apps with LangChain like question-answering systems and chatbots

Understand transformer models and attention mechanisms

Automate data analysis and visualization using pandas and Python

Grasp prompt engineering to improve performance

Fine-tune LLMs and get to know the tools to unleash their power

Deploy LLMs as a service with LangChain and apply evaluation strategies

Privately interact with documents using open-source LLMs to prevent data leaks

Who this book is for

The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.

Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.

Table of Contents

What Is Generative AI?

LangChain for LLM Apps

Getting Started with LangChain

Building Capable Assistants

Building a Chatbot like ChatGPT

Developing Software with Generative AI

LLMs for Data Science

Customizing LLMs and Their Output

Generative AI in Production

The Future of Generative Models

Hard Copy: Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster

 

Printed in Color

Develop an array of effective strategies and blueprints to approach any new data analysis on the Kaggle platform and create Notebooks with substance, style and impact

Leverage the power of Generative AI with Kaggle Models

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models

Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound

Improve the style and readability of your Notebooks, making them more impactful and compelling

Book Description

Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques.

For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable.

Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.

What you will learn

Approach a dataset or competition to perform data analysis via a notebook

Learn data ingestion and address issues arising with the ingested data

Structure your code using reusable components

Analyze in depth both small and large datasets of various types

Distinguish yourself from the crowd with the content of your analysis

Enhance your notebook style with a color scheme and other visual effects

Captivate your audience with data and compelling storytelling techniques

Who this book is for

This book is suitable for a wide audience with a keen interest in data science and machine learning, looking to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. This book caters to:

Beginners on Kaggle from any background

Seasoned contributors who want to build various skills like ingestion, preparation, exploration, and visualization

Expert contributors who want to learn from the Grandmasters to rise into the upper Kaggle rankings

Professionals who already use Kaggle for learning and competing

Table of Contents

Introducing Kaggle and Its Basic Functions

Getting Ready for Your Kaggle Environment

Starting Our Travel - Surviving the Titanic Disaster

Take a Break and Have a Beer or Coffee in London

Get Back to Work and Optimize Microloans for Developing Countries

Can You Predict Bee Subspecies?

Text Analysis Is All You Need

Analyzing Acoustic Signals to Predict the Next Simulated Earthquake

Can You Find Out Which Movie Is a Deepfake?

Unleash the Power of Generative AI with Kaggle Models

Closing Our Journey: How to Stay Relevant and on Top

Hard Copy: Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster



Wednesday, 6 March 2024

Data Analysis and Visualization Foundations Specialization

 


What you'll learn

Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools required for successful data analysis

Explain basic functionality of spreadsheets and utilize Excel to perform a variety of data analysis tasks like data wrangling and data mining

List various types of charts and plots and create them in Excel as well as work with Cognos Analytics to generate interactive dashboards

Join Free: Data Analysis and Visualization Foundations Specialization

Specialization - 4 course series

Deriving insights from data and communicating findings has become an increasingly important part of virtually every profession. This Specialization prepares you for this data-driven transformation by teaching you the core principles of data analysis and visualization and by giving you the tools and hands-on practice to communicate the results of your data discoveries effectively.  

You will be introduced to the modern data ecosystem. You will learn the skills required to successfully start data analysis tasks by becoming familiar with spreadsheets like Excel. You will examine different data sets, load them into the spreadsheet, and employ techniques like summarization, sorting, filtering, & creating pivot tables.

Creating stunning visualizations is a critical part of communicating your data analysis results. You will use Excel spreadsheets to create the many different types of data visualizations such as line plots, bar charts, pie charts. You will also create advanced visualizations such as treemaps, scatter charts & map charts. You will then build interactive dashboards. 

This Specialization is designed for learners interested in starting a career in the field of Data or Business Analytics, as well as those in other professions, who need basic data analysis and visualization skills to supplement their primary job tasks.

This program is ACE® recommended—when you complete, you can earn up to 9 college credits.  

Applied Learning Project

Build your data analytics portfolio as you gain practical experience from producing artifacts in the interactive labs and projects throughout this program. Each course has a culminating project to apply your newfound skills:

In the first course, create visualizations to detect fraud by analyzing credit card data.

In the second course, import, clean, and analyze fleet vehicle inventory with Excel pivot tables.

In the third course, use car sales key performance indicator (KPI) data to create an interactive dashboard with stunning visualizations using Excel and IBM Cognos Analytics.

Only a modern web browser is required to complete these practical exercises and projects — no need to download or install anything on your device.

IBM AI Foundations for 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 IBM

Join Free: IBM AI Foundations for Business Specialization

Specialization - 3 course series

This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.  

Applied Learning Project

Each of the courses in this specialization include Checks for Understanding, which are designed to assess each learner’s ability to understand the concepts presented as well as use those concepts in actual practice.  Specifically, those concepts are related to introductory knowledge regarding 1) artificial intelligence; 2) data science, and; 3) the AI Ladder.  

IBM & Darden Digital Strategy Specialization

 


What you'll learn

Understand the value of data and how the rapid growth of technologies such as artificial intelligence and cloud computing are transforming business. 

Join Free: IBM & Darden Digital Strategy Specialization

Specialization - 6 course series

This Specialization was designed to combine the most current business research in digital transformation and strategy with the most up-to-date technical knowledge of the technologies that are changing how we work and do business to enable you to advance your career. By the end of this Specialization, you will have an understanding of the three technologies impacting all businesses: artificial intelligence, cloud computing, and data science. You will also be able to develop or advance a digital transformation strategy for your own business using these technologies. This specialization will help managers understand technology and technical workers to understand strategy, and is ideal for anyone who wants to be able to help lead projects in digital transformation and technical and business strategy.

Applied Learning Project

This Specialization was designed to combine the most current business research in digital transformation and strategy with the most up-to-date technical knowledge of the technologies that are changing how we work and do business to enable you to advance your career. By the end of this Specialization, you will have an understanding of the three technologies impacting all businesses: artificial intelligence, cloud computing, and data science. You will also be able to develop or advance a digital transformation strategy for your own business using these technologies. This specialization will help managers understand technology and technical workers to understand strategy, and is ideal for anyone who wants to be able to help lead projects in digital transformation and technical and business strategy.

Data Science Fundamentals with Python and SQL Specialization

 


What you'll learn

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

Join Free: Data Science Fundamentals with Python and SQL Specialization

Specialization - 5 course series

Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.

The specialization consists of 5 self-paced online courses that will provide you with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. You’ll learn these data science pre-requisites through hands-on practice using real data science tools and real-world data sets.

Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper in Data Science and work on more advanced Data Science projects. 

No prior knowledge of computer science or programming languages required. 

This program is ACE® recommended—when you complete, you can earn up to 8 college credits.  

Applied Learning Project

All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Extracting and graphing financial data with the Pandas data analysis Python library

Generating visualizations and conducting statistical tests to provide insight on housing trends using census data

Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

Introduction to Data Science Specialization

 


What you'll learn

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

Join Free: Introduction to Data Science Specialization

Specialization - 4 course series

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field.  

This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.

You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.

In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations.

This Specialization can also be applied toward the 
IBM Data Science Professional Certificate. 

Applied Learning Project

All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets and tools like Jupyter, GitHub, and R Studio. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Creating and sharing a Jupyter Notebook containing code blocks and markdown

Devising a problem that can be solved by applying the data science methodology and explain how to apply each stage of the methodology to solve it

Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

Python Coding challenge - Day 143 | What is the output of the following Python Code?

 


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.






Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples

 


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

Key Features

  • This second edition delves deeper into key machine learning topics, CI/CD, and system design
  • Explore core MLOps practices, such as model management and performance monitoring
  • Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools

Book Description

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.

Hard Copy : Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples

What you will learn

  • Plan and manage end-to-end ML development projects
  • Explore deep learning, LLMs, and LLMOps to leverage generative AI
  • Use Python to package your ML tools and scale up your solutions
  • Get to grips with Apache Spark, Kubernetes, and Ray
  • Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow
  • Detect drift and build retraining mechanisms into your solutions
  • Improve error handling with control flows and vulnerability scanning
  • Host and build ML microservices and batch processes running on AWS

Who this book is for

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.

Table of Contents

  1. Introduction to ML Engineering
  2. The Machine Learning Development Process
  3. From Model to Model Factory
  4. Packaging Up
  5. Deployment Patterns and Tools
  6. Scaling Up
  7. Deep Learning, Generative AI, and LLMOps
  8. Building an Example ML Microservice
  9. Building an Extract, Transform, Machine Learning Use Case

Where math doesn’t work in Python

 


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

Tuesday, 5 March 2024

Python Coding challenge - Day 142 | What is the output of the following Python Code?

 


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 & SQL Mastery: 5 Books in 1: Your Comprehensive Guide from Novice to Expert (2024 Edition) (Data Dynamics: Python & SQL Mastery)

 


Are you poised to elevate your technical expertise and stay ahead in the rapidly evolving world of data and programming?

Look no further!

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.

Comprehensive Learning Journey:

Mastering SQL: Dive deep into every facet of SQL, from fundamental data retrieval to complex transactions, views, and indexing.

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.

Empower Your Career: Gain the skills that are highly sought after in today's job market. From database management to advanced analytics, this series prepares you for a multitude of roles in tech, data analysis, and beyond.

Practical, Real-World Application: Each book is packed with practical examples, real-world case studies, and hands-on projects. This approach not only reinforces learning but also prepares you to apply your knowledge effectively in professional settings.

Expert Insight and Future Trends: Learn from experts with years of experience in the field. The series not only teaches you current best practices but also explores emerging trends, ensuring you stay at the forefront of technology.

For Beginners and Experts Alike: Whether you're just starting out or looking to deepen your existing knowledge, our series provides a clear, structured path to mastering both Python and SQL.

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.

Hard Copy: Python & SQL Mastery: 5 Books in 1: Your Comprehensive Guide from Novice to Expert (2024 Edition) (Data Dynamics: Python & SQL Mastery)

Finance with Rust: The 2024 Quantitative Finance Guide to - Financial Engineering, Machine Learning, Algorithmic Trading, Data Visualization & More

 


Reactive Publishing

"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.

Within these pages, you'll discover:

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.

Hard Copy: Finance with Rust: The 2024 Quantitative Finance Guide to - Financial Engineering, Machine Learning, Algorithmic Trading, Data Visualization & More

PYTHON PROGRAMMING FOR BEGINNERS: Mastering Python With No Prior Experience: The Ultimate Guide to Conquer Your Coding Fear From Crash and Land Your First Job in Tech

 


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.

Hard Copy: PYTHON PROGRAMMING FOR BEGINNERS: Mastering Python With No Prior Experience: The Ultimate Guide to Conquer Your Coding Fear From Crash and Land Your First Job in Tech

Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024

 


Reactive Publishing

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.

Hard Copy: Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024

Python Data Science 2024: Explore Data, Build Skills, and Make Data-Driven Decisions in 30 Days (Machine Learning and Data Analysis for Beginners)

 


Data Science Crash Course for Beginners with Python...

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?

This beginner-friendly route offers you the abilities and self-belief to discover data, construct sensible abilities, and begin making data-driven selections inside a month.

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.

Hard Copy: Python Data Science 2024: Explore Data, Build Skills, and Make Data-Driven Decisions in 30 Days (Machine Learning and Data Analysis for Beginners)



Sunday, 3 March 2024

Slicing in Python

 

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']

Saturday, 2 March 2024

Python Coding challenge - Day 141 | What is the output of the following Python Code?

 


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.

Data Analysis with Python

 


What you'll learn

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

Join Free: Data Analysis with Python

There are 6 modules in this course
Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.  

Topics covered include:  
- collecting and importing data 
- cleaning, preparing & formatting data 
- data frame manipulation 
- summarizing data 
- building machine learning regression models 
- model refinement 
- creating data pipelines 

You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.  

In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions.  


Get Started with Python by Google

 


What you'll learn

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 

Join Free : Get Started with Python

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

The zip function in Python

 


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 challenge - Day 140 | What is the output of the following Python Code?

 


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

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