Advanced Django: Advanced Django Rest Framework

 

What you'll learn

Optimize the Django Rest Framework

Integrate with ReactJS

Join Free: Advanced Django: Advanced Django Rest Framework

There are 4 modules in this course

Code and run Django websites without installing anything!

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

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

Course Learning Objectives: 

Write and run tests on Django applications

Optimize code performance using caching, throttling, and filtering

Use a 3rd Party library

Integrate with common Frontends

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Select Topics in Python Specialization

 

What you'll learn

Create websites with Django

Create charts and plots with Matplotlib and Jupyter notebooks

Create a chatbot with the NLTK library

Join Free: Select Topics in Python Specialization

Specialization - 4 course series

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

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

Applied Learning Project

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

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Web Applications and Command-Line Tools for Data Engineering

 

What you'll learn

Construct Python Microservices with FastAPI

Build a Command-Line Tool in Python using Click

Compare multiple ways to set up and use a Jupyter notebook

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

There are 4 modules in this course

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

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Database Engineer Capstone

 

What you'll learn

Build a MySQL database solution.

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

Join Free: Database Engineer Capstone

There are 4 modules in this course

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

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

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

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

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

-Set up a database project,

-Add sales reports,

-Create a table booking system,

-Work with data analytics and visualization,

-And create a database client.

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

-Git,

-MySQL Workbench,

-Tableau,

-And Python.

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Web Application Technologies and Django

 

What you'll learn

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

Install and deploy a simple DJango application

Build simple web pages in HTML and style them using CSS

Explain the basic operations in SQL

Join Free: Web Application Technologies and Django

There are 5 modules in this course

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

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

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

 

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

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

2\t4

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

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Fundamentals of Machine Learning in Finance

 

Build your subject-matter expertise

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

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Fundamentals of Machine Learning in Finance

There are 4 modules in this course

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

A learner with some or no previous knowledge of Machine Learning (ML)  will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.

Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The course is designed for three categories of students:

Practitioners working at financial institutions such as banks, asset management firms or hedge funds

Individuals interested in applications of ML for personal day trading

Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance  

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

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Python and Machine Learning for Asset Management

 

What you'll learn

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets  

Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes    

Utilize powerful Python libraries to implement machine learning algorithms in case studies    

Learn about factor models and regime switching models and their use in investment management    \

Join Free: Python and Machine Learning for Asset Management

There are 5 modules in this course

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions.

The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. 

We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis.

You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept.

At the end of this course, you will master the various machine learning techniques in investment management.

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Guided Tour of Machine Learning in Finance

 

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Python and Machine-Learning for Asset Management with Alternative Data Sets

 

What you'll learn

Learn what alternative data is and how it is used in financial market applications. 

Become immersed in current academic and practitioner state-of-the-art research pertaining to alternative data applications.

Perform data analysis of real-world alternative datasets using Python.

Gain an understanding and hands-on experience in data analytics, visualization and quantitative modeling applied to alternative data in finance

Join Free: Python and Machine-Learning for Asset Management with Alternative Data Sets

There are 4 modules in this course

Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications. The approach of this course is somewhat unique because while the theory covered is still a main component, practical lab sessions and examples of working with alternative datasets are also key. This course is fo you if you are aiming at carreers prospects as a data scientist in financial markets, are looking to enhance your analytics skillsets to the financial markets, or if you are interested in cutting-edge technology and research as  they apply to big data. The required background is: Python programming, Investment theory , and Statistics. This course will enable you to learn new data and research techniques applied to the financial markets while strengthening data science and python skills.

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Python for Finance: Beta and Capital Asset Pricing Model

 What you'll learn

Understand the theory and intuition behind the Capital Asset Pricing Model (CAPM)

Calculate Beta and expected returns of securities in python

Perform interactive data visualization using Plotly Express

Join Free: Python for Finance: Beta and Capital Asset Pricing Model

About this Guided Project

In this project, we will use Python to perform stocks analysis such as calculating stock beta and expected returns using the Capital Asset Pricing Model (CAPM). CAPM is one of the most important models in Finance and it describes the relationship between the expected return and risk of securities. We will analyze the performance of several companies such as Facebook, Netflix, Twitter and AT&T over the past 7 years. This project is crucial for investors who want to properly manage their portfolios, calculate expected returns, risks, visualize datasets, find useful patterns, and gain valuable insights. This project could be practically used for analyzing company stocks, indices or  currencies and performance of portfolio.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Introduction to Python for Civil Engineers: a Beginner’s Guide

 

This book serves as a means to bridge the gap between civil engineering and programming skills, with a specific focus on Python. Python is highly regarded among users due to its user-friendly nature, making it applicable in a wide range of subjects such as:

• Data mining

• Big data problems

• Artificial intelligence

• Machine learning

• Engineering calculations

To master Python and acquire comprehensive knowledge, it is crucial to embark on your journey with a well-scripted book. Our aim in writing this book was to provide abundant examples that cater specifically to individuals with basic knowledge in civil engineering.

Now, why do civil engineers need to acquire knowledge about Python? The answer is simple: depending on the field a civil engineering graduate chooses to pursue, having Python skills can be pivotal. For instance, structural health monitoring is currently a trending topic, and effectively interpreting collocated data requires proficiency in data manipulation, visualization, and optimization techniques. Therefore, possessing these capabilities greatly increases your chances of securing your dream job.

"Introduction to Python for Civil Engineers: A Beginner's Guide" offers simple and thorough explanations of the basics, accompanied by numerous examples. After covering the fundamentals, the book delves into the useful and essential features of popular libraries including Numpy, Pandas, Matplotlib, and Scipy. Abundant examples and mini-projects are provided to enhance your understanding of these concepts. Additionally, the book includes four real-world projects with step-by-step solutions, guiding you through your very first hands-on training experiences.

So, what are you waiting for? Start your learning journey today!

Hard Copy: Introduction to Python for Civil Engineers: a Beginner’s Guide

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Python Coding challenge - Day 132 | What is the output of the following Python Code?

 

Let's break down the expression:

a = True

b = False

print(a == b or not b)

a == b: This checks if the value of a is equal to the value of b. In this case, True == False evaluates to False.

not b: This negates the value of b. Since b is False, not b evaluates to True.

a == b or not b: The or operator returns True if at least one of the conditions is True. In this case, False or True evaluates to True.

So, the output of the print statement will be True.

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The Amazing Technique of Returning Results in Python Functions

 

1. Single Return Value:

def add_numbers(a, b):

    result = a + b

    return result

​

sum_result = add_numbers(3, 4)

print(sum_result)  # Output: 7

​

#clcoding.com

7

2. Multiple Return Values:

def operate_numbers(a, b):

    addition = a + b

    subtraction = a - b

    multiplication = a * b

    return addition, subtraction, multiplication

​

result_tuple = operate_numbers(5, 3)

print(result_tuple)

# Output: (8, 2, 15)

​

# Unpack the tuple

add_result, sub_result, mul_result = operate_numbers(5, 3)

print(add_result, sub_result, mul_result)

# Output: 8 2 15

​

#clcoding.com

(8, 2, 15)

8 2 15

3. Returning a Dictionary :

def get_person_info(name, age):

    person_info = {'Name': name, 'Age': age}

    return person_info

​

info_dict = get_person_info('John', 30)

print(info_dict)

# Output: {'Name': 'John', 'Age': 30}

​

#clcoding.com

{'Name': 'John', 'Age': 30}

4. Returning None:

def simple_function():

    print("This function does something")

​

result = simple_function()

print(result)  # Output: None

​

#clcoding.com

This function does something

None

5. Returning Early:

def divide(a, b):

    if b == 0:

        print("Cannot divide by zero.")

        return  # Exit the function early

    result = a / b

    return result

​

result = divide(8, 2)

print(result)  # Output: 4.0

​

#clcoding.com

4.0

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Box and Whisker plot using Python

 

#!/usr/bin/env python

# coding: utf-8

# # Box and whisker plot using Python

# # 1. Matplotlib:

# In[1]:

import matplotlib.pyplot as plt

# Sample data

data = [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]

# Create boxplot

plt.boxplot(data)

# Customize labels and title

plt.xlabel("Data")

plt.ylabel("Value")

plt.title("Boxplot with Matplotlib")

plt.show()

# # 2. Pandas:

# In[2]:

import pandas as pd

import matplotlib.pyplot as plt

# Sample DataFrame

data = pd.DataFrame({"values": [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]})

# Create boxplot

data.plot.box()

# Customize labels and title

plt.xlabel("Data")

plt.ylabel("Value")

plt.title("Boxplot with Pandas")

plt.show()

# # 3. Seaborn:

# In[3]:

import seaborn as sns

# Sample data (same as before)

data = [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]

# Create boxplot

sns.boxplot(data=data)

# Customize with hue (category) plot

data = {"category": ["A", "B", "A", "A", "B", "A", "A", "B", "B", "A"], "values": data}

sns.boxplot(x="category", y="values", data=data)

plt.show()

# In[ ]:

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Python Coding challenge - Day 131 | What is the output of the following Python Code?

 

Let's break down the code step by step:

x = 'Monday'

In this line, a variable x is assigned the value 'Monday'. The variable x now holds the string 'Monday'.

print('Mon' in x)

This line uses the print function to output the result of the expression 'Mon' in x. The in keyword is used here to check if the substring 'Mon' is present in the string x.

Here's how it works:

'Mon' is a string representing the substring we are looking for.

x is the string 'Monday' that we are searching within.

The expression 'Mon' in x evaluates to True if the substring 'Mon' is found anywhere within the string 'Monday', and False otherwise.

In this case, since 'Mon' is a part of 'Monday', the result of the expression is True. Therefore, the print function will output True to the console.

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

True

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Computer Architecture

 

There are 21 modules in this course

In this course, you will learn to design the computer architecture of complex modern microprocessors.

Join Free: Computer Architecture

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What will be the output after the following statements? x = 'Python Pi Py Pip' print(x.count('p'))

 

x = 'Python Pi Py Pip'

print(x.count('p'))

The code defines a string variable named x and assigns the value 'Python Pi Py Pip' to it. Then, it uses the count() method to count the number of occurrences of the letter 'p' in the string. The count() method returns an integer value, which is the number of times the specified substring is found within the string.

In this case, the string 'Python Pi Py Pip' contains one lowercase letter 'p'. Therefore, the output of the code is:

1

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Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling

 

Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries

Key Features:

• Conduct Bayesian data analysis with step-by-step guidance
• Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling
• Enhance your learning with best practices through sample problems and practice exercises
• Purchase of the print or Kindle book includes a free PDF eBook.

Book Description:

The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.

In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.

By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.

What You Will Learn:

• Build probabilistic models using PyMC and Bambi
• Analyze and interpret probabilistic models with ArviZ
• Acquire the skills to sanity-check models and modify them if necessary
• Build better models with prior and posterior predictive checks
• Learn the advantages and caveats of hierarchical models
• Compare models and choose between alternative ones
• Interpret results and apply your knowledge to real-world problems
• Explore common models from a unified probabilistic perspective
• Apply the Bayesian framework's flexibility for probabilistic thinking

Who this book is for:

If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.

Hard Copy:  Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling

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The Power of Statistics

 

What you'll learn

Explore and summarize a dataset 

Use probability distributions to model data

Conduct a hypothesis test to identify insights about data

Perform statistical analyses using Python 

Join Free: The Power of Statistics

There are 6 modules in this course

This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional. 

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:

-Describe the use of statistics in data science 

-Use descriptive statistics to summarize and explore data

-Calculate probability using basic rules

-Model data with probability distributions

-Describe the applications of different sampling methods 

-Calculate sampling distributions 

-Construct and interpret confidence intervals

-Conduct hypothesis tests

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Automate Cybersecurity Tasks with Python

 

What you'll learn

Explain how the Python programming language is used in cybersecurity

Create new, user-defined Python functions

Use regular expressions to extract information from text

Practice debugging code

Join Free: Automate Cybersecurity Tasks with Python

There are 4 modules in this course

This is the seventh course in the Google Cybersecurity Certificate. These courses will equip you with the skills you need to apply for an entry-level cybersecurity job. You’ll build on your understanding of the topics that were introduced in the sixth Google Cybersecurity Certificate course.

In this course, you will be introduced to the Python programming language and apply it in a cybersecurity setting to automate tasks. You'll start by focusing on foundational Python programming concepts, including data types, variables, conditional statements, and iterative statements. You'll also learn to work with Python effectively by developing functions, using libraries and modules, and making your code readable. In addition, you'll work with string and list data, and learn how to import, parse and debug files.  

Google employees who currently work in cybersecurity will guide you through videos, provide hands-on activities and examples that simulate common cybersecurity tasks, and help you build your skills to prepare for jobs. 

Learners who complete this certificate will be equipped to apply for entry-level cybersecurity roles. No previous experience is necessary.

By the end of this course, you will: 

- Explain how the Python programming language is used in cybersecurity.

- Write conditional and iterative statements in Python.

- Create new, user-defined Python functions.

- Use Python to work with strings and lists.

- Use regular expressions to extract information from text.

- Use Python to open and read the contents of a file.

- Identify best practices to improve code readability.

- Practice debugging code.

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Introduction to Git and GitHub

 

What you'll learn

Understand why version control is a fundamental tool for coding and collaboration

Install and run Git on your local machine 

Use and interact with GitHub 

Collaborate with others through remote repositories

Join Free: Introduction to Git and GitHub

There are 4 modules in this course

In this course, you’ll learn how to keep track of the different versions of your code and configuration files using a popular version control system (VCS) called Git. We'll also go through how to set up an account with a service called GitHub so that you can create your very own remote repositories to store your code and configuration.

Throughout this course, you'll learn about Git's core functionality so you can understand how and why it’s used in organizations. We’ll look into both basic and more advanced features, like branches and merging. We'll demonstrate how having a working knowledge of a VCS like Git can be a lifesaver in emergency situations or when debugging. And then we'll explore how to use a VCS to work with others through remote repositories, like the ones provided by GitHub. By the end of this course, you'll be able to store your code's history in Git and collaborate with others in GitHub, where you’ll also start creating your own portfolio! In order to follow along and complete the assessments, you’ll need a computer where you can install Git or ask your administrator to install it for you.

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The Nuts and Bolts of Machine Learning

 

What you'll learn

Identify characteristics of the different types of machine learning 

Prepare data for machine learning models 

Build and evaluate supervised and unsupervised learning models using Python

Demonstrate proper model and metric selection for a machine learning algorithm

Join Free: The Nuts and Bolts of Machine Learning

There are 5 modules in this course

This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You’ll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision tree, random forest, and more.  

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:

-Apply feature engineering techniques using Python

-Construct a Naive Bayes model

-Describe how unsupervised learning differs from supervised learning

-Code a K-means algorithm in Python 

-Evaluate and optimize the results of K-means model

-Explore decision tree models, how they work, and their advantages over other types of supervised machine learning

-Characterize bagging in machine learning, specifically for random forest models 

-Distinguish boosting in machine learning, specifically for XGBoost models 

-Explain tuning model parameters and how they affect performance and evaluation metrics

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Regression Analysis: Simplify Complex Data Relationships

 

What you'll learn

Investigate relationships in datasets

Identify regression model assumptions 

Perform linear and logistic regression using Python

Practice model evaluation and interpretation

Join Free: Regression Analysis: Simplify Complex Data Relationships

There are 6 modules in this course

This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.  

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:

-Explore the use of predictive models to describe variable relationships, with an emphasis on correlation

-Determine how multiple regression builds upon simple linear regression at every step of the modeling process

-Run and interpret one-way and two-way ANOVA tests

-Construct different types of logistic regressions including binomial, multinomial, ordinal, and Poisson log-linear regression models

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Using Python to Interact with the Operating System

 

What you'll learn

Setup, configure, and use your own developer environment in Python

Manipulate files and processes running on the Operating System using Python

Understand and use regular expressions (regex), a powerful tool for processing text files

Know when to choose Bash or Python, and create small scripts using Bash

Join Free: Using Python to Interact with the Operating System

There are 7 modules in this course

By the end of this course, you’ll be able to manipulate files and processes on your computer’s operating system. You’ll also have learned about regular expressions -- a very powerful tool for processing text files -- and you’ll get practice using the Linux command line on a virtual machine. And, this might feel like a stretch right now, but you’ll also write a program that processes a bunch of errors in an actual log file and then generates a summary file. That’s a super useful skill for IT Specialists to know.

We’ll kick off by exploring how to execute Python locally, and organize and use code across different Python files. We'll then learn how to read and write different types of files, and use subprocesses and input streams. We'll also dive into Bash scripting and regular expressions -- both very powerful tools for anyone working with systems. We'll even touch on automatic testing, which allows us to automate how we check if our code is correct. To finish, we’ll put all this together by using the tools that we’ve acquired to process data and generate automatic reports. We’ll also explain how to set up your own developer environment in your machine. This is a key step in being able to write and deploy powerful automation tools.

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Python Coding challenge - Day 130 | What is the output of the following Python Code?

 

Codes: 

import re

x = re.compile(r'\W')

y = x.findall('clcoding')

print(y)

Answer and Solution:

In the above Python code, the re module to create a regular expression pattern and then using it to find all non-word characters in the string 'clcoding'. The regular expression pattern r'\W' matches any non-word character (equivalent to [^a-zA-Z0-9_]).

Here's a breakdown of the code:

import re: Imports the regular expression module.

x = re.compile(r'\W'): Compiles a regular expression pattern \W, where \W matches any non-word character.

y = x.findall('clcoding'): Uses the compiled regular expression to find all non-word characters in the string 'clcoding' and stores the result in the variable y.

print(y): Prints the result, which is a list of non-word characters found in the string.

In the given example, since the string 'clcoding' contains only letters (no non-word characters), the output will be an empty list []. If there were non-word characters in the string, they would be included in the list.

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Python Coding challenge - Day 129 | What is the output of the following Python Code?

 

x = 'clcoding'

print(x.isalpha())

This code outputs the following:

True

The expression x.isalpha() checks if all the characters in the string x are alphabetic letters (a-z and A-Z). Since the string 'clcoding' only contains alphabetic letters, the expression evaluates to True.

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Python Programming For Beginners: Crack the Code to Success, From Zero to Python Hero in Less 45 Days! Include Code Examples and Exercise | New Edition 2024

 

Are you ready to embark on a transformative journey into the world of Python programming?

Look no further than "Crack the Code to Success: From Zero to Python Hero in Less Than 45 Days!" This brand-new 2024 edition is your ultimate guide to mastering Python, and it's designed with you in mind.

🚀Kickstart your coding adventure with a book that takes you from absolute beginner to Python hero in under 45 days! 🚀

🔑Here's what you'll unlock along the way:

✓ Python Foundations: Start with the basics, from installing Python to writing your very first Python script. You'll gain a solid grasp of syntax and code structure, setting the stage for your journey.

✓ Control Flow: Dive into Python's conditional statements and loops. We guide you through them step by step, with practical exercises to reinforce your understanding.

✓ Data Structures: Discover how to work with lists, tuples, dictionaries, and sets. Get hands-on experience to cement your knowledge.

✓ Functions and Modularity: Learn to create and use functions effectively, understand parameters and return values, and gain insights into variable scope.

✓ Object-Oriented Python: Unlock the power of object-oriented programming (OOP). Understand classes, objects, constructors, attributes, methods, inheritance, and polymorphism.

✓ FileHandlingMaster: file input/output, work with JSON data, and become adept at handling exceptions during file operations.

✓ Python Modules and Packages: Explore standard modules, create custom ones, and structure larger projects like a pro.

✓ Python for Web Development: Take your skills to the web. Learn Flask, a web application framework, and handle routing, templates, databases, user authentication, and deployment.

✓ Data Handling and Analysis: Tackle real-world data with confidence. Read and write data, utilize SQLite, and perform data analysis with Pandas.

✓ Data Visualization: Make your data shine with Matplotlib, Seaborn, and Plotly. Create stunning visualizations that tell compelling stories.

✓ Best Practices and Beyond: Elevate your coding game with best practices for clean code. Explore Python's role in machine learning and find additional resources to fuel your learning journey.

Don't let your dreams of Python proficiency remain just dreams. Whether you're looking to land your dream job, tackle complex coding challenges, or simply expand your horizons, this book is your key to success.

Hard Copy: Python Programming For Beginners: Crack the Code to Success, From Zero to Python Hero in Less 45 Days! Include Code Examples and Exercise | New Edition 2024

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Python Programming and SQL: 5 books in 1 - The #1 Coding Course from Beginner to Advanced. Learn it Well & Fast (2023) (Computer Programming)

 

Supercharge Your Career with Python programming and SQL: The #1 Coding Course from Beginner to Advanced (2024)

Are you looking to turbocharge your career prospects? Do you want to gain the skills that are in high demand in today's job market?

Whether you're a complete beginner or an experienced programmer, this #1 bestseller book is designed to make your learning journey simple, regardless of your current skills. It aims to guide you seamlessly through the content and fast-track your career in no time.

This 5-in-1 guide covers both Python and SQL fundamental and advanced concepts, ensuring that you not only gain a comprehensive understanding but also stand out among your peers and stay ahead of the competition:

✅ Step-by-Step Instructions: This easy-to-understand guide provides step-by-step instructions, making it effortless to grasp Python and SQL fundamentals.

✅ Fast Learning Curve: Progress rapidly from beginner to advanced levels with our carefully crafted curriculum. Gain confidence to tackle coding challenges.

✅ Boost Your Career: Acquire sought-after skills desired by employers, making you stand out in the job market. Get job-ready and attractive to potential employers.

✅ Competitive Edge: Stand out among peers with our cutting-edge course covering fundamentals and advanced concepts. Your coding proficiency will make you invaluable to any organization.

✅ Versatile Job Opportunities: Python and SQL open doors in tech, data analysis, web development, and more. Stay ahead of the competition.

✅ Start Writing Your Own Programs: Empower yourself to create efficient code, unleash creativity, and achieve peak performance.

✅ Real-World Projects: Gain practical experience through hands-on projects, showcasing your coding expertise effectively.

✅ Expert Guidance: Acquire practical skills and knowledge from expert guidance to become a proficient programmer.

Here are just a few things you'll learn in Python programming and SQL:

Get started with Python programming, covering variables, functions, loops, and conditionals

Discover how to work with data in Python, including data types, structures, and manipulation techniques

Learn different data structures, such as sequences, tuples, lists, matrices, and dictionaries

Understand conditional statements and their role in decision making

Discover object-oriented programming (OOP) and learn how to define classes and methods.

Discover the art of exception handling, ensuring robust and error-free code

Explore the power of algorithms, information processing and master the essential features of algorithms

Master file processing in Python, including opening, reading, writing, and appending files, etc.

Master SQL essentials such as basics of SQL, data types, statements, and clauses

Work with databases using SQL, including creating, modifying, and deleting tables and records.

Learn powerful queries: Perform joins, unions, ordering, grouping, and utilize aliases for advanced SQL queries

Explore efficient data management: Navigate MySQL, work with databases, tables, and views

Advanced techniques: explore stored procedures, indexing, truncating, and working with triggers

Master data optimization: Fine-tune SQL queries for optimal performance and efficiency

Gain practical skills and techniques that you can directly apply in you career

Hard Copy: Python Programming and SQL: 5 books in 1 - The #1 Coding Course from Beginner to Advanced. Learn it Well & Fast (2023) (Computer Programming)

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Python Programming for Beginners: Go from Novice to Ninja with this Stress-Free Guide to Confident Python Programming Featuring Clear Explanations and Hands-on Examples

 

Learn to program in Python with confidence! Whether you're pursuing Python as a hobby, or seeking to advance your career, this book will help you go from novice to ninja.

What you'll get from this book:

Access to all the completed hands-on code. Don't worry if you get stuck, access to completed code samples is included so you can study and learn.

Clear understandable explanations. Every coding example is explained line-by-line so you don't just learn what to do, you'll also learn the thinking behind it.

What you'll learn:

How to install Python and set up an IDE.

Operators and Expressions.

Loops and iterations.

Functions and Methods

Data Structures: Lists, tuples, dictionaries, and sets.

Object-Oriented Programming.

Debugging and Troubleshooting.

Web Development.

Data Analysis and Visualization.

Scripting and Automation.

Database Basics and Python Integration.

By the time you're done, you'll have a fully functioning web-based weather app. You'll also know how to connect Python to a database and perform essential database functions. Whether you're learning Python as a hobby or to improve your skillset and marketability, this book is for you!

Hard Copy: Python Programming for Beginners: Go from Novice to Ninja with this Stress-Free Guide to Confident Python Programming Featuring Clear Explanations and Hands-on Examples

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Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning Algorithms into Web Apps

 

Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps.

Author Tshepo Chris Nokeri begins by introducing you to the basics of constructing and styling static and interactive charts and tables before exploring the basics of HTML, CSS, and Bootstrap, including an approach to building web pages with HTML. From there, he’ll show you the key Python web frameworks and techniques for building web apps with them. You’ll then see how to style web apps and incorporate themes, including interactive charts and tables to build dashboards, followed by a walkthrough of creating URL routes and securing web apps. You’ll then progress to more advanced topics, like building machine learning algorithms and integrating them into a web app. The book concludes with a demonstration of how to deploy web apps in prevalent cloud platforms.

Web App Development and Real-Time Web Analytics with Python is ideal for intermediate data scientists, machine learning engineers, and web developers, who have little or no knowledge about building web apps that implement bootstrap technologies. After completing this book, you will have the knowledge necessary to create added value for your organization, as you will understand how to link front-end and back-end development, including machine learning.

What You Will Learn

Create interactive graphs and render static graphs into interactive ones

Understand the essentials of HTML, CSS, and Bootstrap

Gain insight into the key Python web frameworks, and how to develop web applications using them

Develop machine learning algorithms and integrate them into web apps

Secure web apps and deploy them to cloud platforms

Who This Book Is For

Intermediate data scientists, machine learning engineers, and web developers.

Hard Copy: Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning Algorithms into Web Apps

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Web Scraping With Selenium and Python: Build a Pinterest Web Scraper With Me

 

🚀 Welcome to the "Web Scraping with Selenium and Python" world! 🌐 Whether you're an experienced developer or a curious beginner, this book is your passport to the captivating realm of web scraping. In an era where data reigns supreme, mastering the art of extracting information from the vast expanses of the internet can be a game-changer.

🎯 Target Audience: Whether you're a data enthusiast, a business professional, or a tech-savvy hobbyist, the content is designed to benefit a wide range of readers. No prior web scraping experience is necessary, but a basic understanding of Python is essential.

💡 Purpose: As we navigate through the intricacies of this digital landscape, you'll discover how to leverage Python and Selenium to scrape dynamic websites, providing you with the tools to turn information into actionable insights.

🐍 Python Prerequisites: To join this adventure, a fundamental understanding of Python is a must. We won't cover Python basics in this book, so ensure you're comfortable with the language before diving in. If you're new to Python, consider brushing up on the basics to make the most of your web scraping journey.

🤖 Selenium: Ever wondered how to interact with dynamic websites? Enter Selenium, your trusty companion in the web scraping realm. Discover how to use Selenium to navigate through web pages, interact with elements, and extract the data you desire.

🔍 Project Focus – Pinterest Scraper: What better way to apply your newfound skills than working on a real project? This book guides you through creating a Pinterest scraper—from setting up your environment to handling dynamic content. Gain valuable insights immediately applicable to your projects.

🎢 So, fasten your seatbelt as we embark on a thrilling journey down "Web Scraping with Selenium and Python"! By the end of this book, you'll be equipped with the knowledge and skills to scrape data effectively, opening doors to endless possibilities in data-driven decision-making. Let the scraping adventure begin! 🌐✨

Hard Copy : Web Scraping With Selenium and Python: Build a Pinterest Web Scraper With Me

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Python Coding challenge - Day 128 | What is the output of the following Python Code?

 

Let's analyze the given code:

x = 0

y = 7

while x + y < 10:

    x += 1

    print(x, end='')

Here's a step-by-step breakdown of what the code does:

Initialize x with the value 0 and y with the value 7.

Enter a while loop with the condition x + y < 10. This means the loop will continue as long as the sum of x and y is less than 10.

Inside the loop, increment x by 1 using x += 1.

Print the current value of x without a newline, using print(x, end='').

Repeat steps 3 and 4 until the condition x + y < 10 becomes false.

Let's see how the loop proceeds:

Iteration 1: x = 1, y = 7, and x + y = 8 (since 1 + 7 = 8). The loop continues.

Iteration 2: x = 2, y = 7, and x + y = 9 (since 2 + 7 = 9). The loop continues.

Iteration 3: x = 3, y = 7, and x + y = 10 (since 3 + 7 = 10). The loop stops because the condition x + y < 10 is no longer true.

So, the output of this code will be:

123

The loop prints the values of x (1, 2, 3) on the same line due to the end='' parameter in the print statement.

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Python in Excel: IT'S CELL-FIE TIME: Smooth out that cell-ulite, flattening the curves with python in excel (The Pythonin Prodigy Series: Unveiling the Python Power Across Business Domains)

 

It's time to take a CELL-FIE and upgrade your excel game. With Microsoft now including direct support for python. The Future is now.

Discover the Game-Changer: Dive deep into the compelling world where Python meets Excel, revolutionizing data tasks, automating mundane processes, and elevating analysis to art. If there was ever a book that could make spreadsheets sizzle, this is it!

Why This Book?: This isn't just another technical guide. It's the bridge between two powerhouses in the digital realm. From Vancouver's bustling tech scene, Alice crafts a masterpiece, taking you on an insightful journey that unveils the synergies between Excel's versatility and Python's prowess.

For the Doers and Dreamers: Whether you're an entrepreneur like Hayden, an analyst, a data enthusiast, or someone who just adores spreadsheets, this book offers tools that can transform your workflow, insights that spark innovation, and techniques that promise efficiency.

Journey Beyond the Basics: Transcend beyond the traditional uses of Excel. Discover advanced data manipulations, real-time web integrations, machine learning implementations, and so much more! By the end, you'll be scripting your way to spreadsheet stardom.

Real-World Applications: With 600 meticulously crafted pages, each chapter resonates with Alice's passion and expertise, offering 20 detailed subsections that are rich with real-world examples, case studies, and hands-on challenges. It's practicality and theory in perfect harmony.

The Promise: "Python in Excel: The Ultimate Guide to Automation and Analysis" isn't just a book; it's a commitment. A commitment to upskilling, to innovating, and to mastering the tools of tomorrow, today.

Hard Copy: Python in Excel: IT'S CELL-FIE TIME: Smooth out that cell-ulite, flattening the curves with python in excel (The Pythonin Prodigy Series: Unveiling the Python Power Across Business Domains)

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