Hands-On Data Analysis with NumPy and pandas (Free PDF)

 

Key Features

• Explore the tools you need to become a data analyst
• Discover practical examples to help you grasp data processing concepts
• Walk through hierarchical indexing and grouping for data analysis

Book Description

Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning.

Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python's NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python's pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them.

By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation.

What you will learn

• Understand how to install and manage Anaconda
• Read, sort, and map data using NumPy and pandas
• Find out how to create and slice data arrays using NumPy
• Discover how to subset your DataFrames using pandas
• Handle missing data in a pandas DataFrame
• Explore hierarchical indexing and plotting with pandas

Who This Book Is For

Hands-On Data Analysis with NumPy and Pandas is for you if you are a Python developer and want to take your first steps into the world of data analysis. No previous experience of data analysis is required to enjoy this book.

Table of Contents

1. Setting Up a Python Data Analysis Environment
2. Diving into NumPY
3. Operations on NumPy Arrays
4. Pandas Are Fun! What Is pandas?
5. Arithmetic, Function Application and Mapping with pandas
6. Managing, Indexing, and Plotting

Kindle - Hands-On Data Analysis with NumPy and pandas: Implement Python packages from data manipulation to processing

PDF Link - 

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Python Coding challenge - Day 72 | What is the output of the following Python code?

 

Code - 

j = 1

while j <= 2 :

  print(j)

  j++

Explanation - 

It looks like there's a small syntax error in your code. In Python, the increment operator is +=, not ++. Here's the corrected version:

j = 1

while j <= 2:

    print(j)

    j += 1

This code initializes the variable j with the value 1 and then enters a while loop. The loop continues as long as j is less than or equal to 2. Inside the loop, the current value of j is printed, and then j is incremented by 1 using j += 1.

When you run this corrected code, the output will be:

1

2

Each number is printed on a new line because the print function automatically adds a newline character by default.

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a = 10 b = 60 if a and b > 20 : print('Hello') else : print('Hi')

Code - 

a = 10

b = 60

if a and b > 20 :

  print('Hello')

else :

  print('Hi')

Explanation -

The above code checks the condition a and b > 20. In Python, the and operator has a higher precedence than the comparison (>), so the expression is evaluated as (a) and (b > 20).

Here's how it breaks down:

The value of a is 10.

The value of b is 60.

The first part of the condition, a, is considered "truthy" in Python.

The second part of the condition, b > 20, is also true because 60 is greater than 20.

So, both parts of the and condition are true, and the print('Hello') statement will be executed. Therefore, the output of the code will be:

Hello

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a = 10 a = not not a print(a)

CODE - 

a = 10

a = not not a

print(a)

Solution - 

In the above code, the variable a is initially assigned the value 10. Then, the line a = not not a is used. The not operator in Python is a logical NOT, which negates the truth value of a boolean expression.

In this case, not a would be equivalent to not 10, and since 10 is considered "truthy" in Python, not 10 would be False. Then, not not a is applied, which is equivalent to applying not twice. So, not not 10 would be equivalent to not False, which is True.

Therefore, after the execution of the code, the value of a would be True, and if you print a, you will get: True 

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Rewrite the following code in 1 line

 Rewrite the following code in 1 line

x = 3 y = 3.0 if x == y : print('x and y are equal') else : print('x and y are not equal')

In one line

x, y = 3, 3.0

print('x and y are equal') if x == y else print('x and y are not equal')

Explanation -

Initializes two variables, x and y, with the values 3 and 3.0, respectively. Then, it uses a conditional expression (also known as a ternary operator) to check if x is equal to y. If they are equal, it prints 'x and y are equal'; otherwise, it prints 'x and y are not equal'.

In this case, x and y are equal because the values are numerically the same, even though one is an integer (x) and the other is a float (y). The output of the code would be: x and y are equal

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msg = 'Aeroplane' ch = msg[-0] print(ch)

msg = 'Aeroplane'

ch = msg[-0]

print(ch)

In Python, indexing starts from 0, so msg[-0] is equivalent to msg[0]. Therefore, ch will be assigned the value 'A', which is the first character of the string 'Aeroplane'. If you run this code, the output will be: A

Step by Step : 

msg = 'Aeroplane': This line initializes a variable msg with the string 'Aeroplane'.

ch = msg[-0]: This line attempts to access the character at index -0 in the string msg. However, in Python, negative indexing is used to access elements from the end of the sequence. Since -0 is equivalent to 0, this is the same as accessing the character at index 0.

print(ch): This line prints the value of the variable ch.

Now, let's evaluate the expression step by step:

msg[-0] is equivalent to msg[0], which accesses the first character of the string 'Aeroplane', so ch is assigned the value 'A'.

Therefore, when you run the code, the output will be : A

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Python Coding challenge - Day 71 | What is the output of the following Python code?

 

Questions - 

for index in range(20, 10, -3):

    print(index, end=' ')

Solution - 

range(20, 10, -3): This creates a range of numbers starting from 20, decrementing by 3 at each step, until it reaches a number less than 10. So, the sequence is 20, 17, 14, 11.

for index in range(20, 10, -3):: This is a for loop that iterates over each value in the specified range. In each iteration, the variable index takes on the next value in the range.

print(index, end=' '): This prints the value of index followed by a space. The end=' ' argument is used to specify that a space should be printed at the end of each value instead of the default newline character.

When you run this code, the output will be:

Output :   20 17 14 11

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Process Data from Dirty to Clean

 

What you'll learn

Define data integrity with reference to types of integrity and risk to data integrity

Apply basic SQL functions for use in cleaning string variables in a database

Develop basic SQL queries for use on databases

Describe the process involved in verifying the results of cleaning data

There are 6 modules in this course

This is the fourth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll continue to build your understanding of data analytics and the concepts and tools that data analysts use in their work. You’ll learn how to check and clean your data using spreadsheets and SQL as well as how to verify and report your data cleaning results. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will be able to do the following:

 - Learn how to check for data integrity.

 - Discover data cleaning techniques using spreadsheets. 

 - Develop basic SQL queries for use on databases.

 - Apply basic SQL functions for cleaning and transforming data.

 - Gain an understanding of how to verify the results of cleaning data.

 - Explore the elements and importance of data cleaning reports.

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Analyze Data to Answer Questions

 

What you'll learn

Discuss the importance of organizing your data before analysis with references to sorts and filters

Demonstrate an understanding of what is involved in the conversion and formatting of data

Apply the use of functions and syntax to create SQL queries for combining data from multiple database tables

Describe the use of functions to conduct basic calculations on data in spreadsheets

There are 4 modules in this course

This is the fifth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll explore the “analyze” phase of the data analysis process. You’ll take what you’ve learned to this point and apply it to your analysis to make sense of the data you’ve collected. You’ll learn how to organize and format your data using spreadsheets and SQL to help you look at and think about your data in different ways. You’ll also find out how to perform complex calculations on your data to complete business objectives. You’ll learn how to use formulas, functions, and SQL queries as you conduct your analysis. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

 - Learn how to organize data for analysis.

 - Discover the processes for formatting and adjusting data. 

 - Gain an understanding of how to aggregate data in spreadsheets and by using SQL.

 - Use formulas and functions in spreadsheets for data calculations.

 - Learn how to complete calculations using SQL queries.

JOIN FREE - Analyze Data to Answer Questions

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Go Beyond the Numbers: Translate Data into Insights

 

What you'll learn

Apply the exploratory data analysis (EDA) process

Explore the benefits of structuring and cleaning data

Investigate raw data using Python

Create data visualizations using Tableau 

There are 5 modules in this course

This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations. 

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 build 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:

-Use Python tools to examine raw data structure and format

-Select relevant Python libraries to clean raw data

-Demonstrate how to transform categorical data into numerical data with Python

-Utilize input validation skills to validate a dataset with Python

-Identify techniques for creating accessible data visualizations with Tableau

-Determine decisions about missing data and outliers 

-Structure and organize data by manipulating date strings

Join Free- Go Beyond the Numbers: Translate Data into Insights

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Decisions, Decisions: Dashboards and Reports

 

What you'll learn

Design BI visualizations

Practice using BI reporting and dashboard tools

Create presentations to share key BI insights with stakeholders

Develop professional materials for your job search

There are 6 modules in this course

You’re almost there! This is the third and final course in the Google Business Intelligence Certificate. In this course, you’ll apply your understanding of stakeholder needs, plan and create BI visuals, and design reporting tools, including dashboards. You’ll also explore how to answer business questions with flexible and interactive dashboards that can monitor data over long periods of time.

Google employees who currently work in BI will guide you through this course by providing hands-on activities that simulate job tasks, sharing examples from their day-to-day work, and helping you build business intelligence skills to prepare for a career in the field. 

Learners who complete the three courses in this certificate program will have the skills needed to apply for business intelligence jobs. This certificate program 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:

-Explain how BI visualizations answer business questions

-Identify complications that may arise during the creation of BI visualizations

-Produce charts that represent BI data monitored over time

-Use dashboard and reporting tools

-Build dashboards using best practices to meet stakeholder needs

-Iterate on a dashboard to meet changing project requirements

-Design BI presentations to share insights with stakeholders

-Create or update a resume and prepare for BI interviews

Join Free - Decisions, Decisions: Dashboards and Reports

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Ask Questions to Make Data-Driven Decisions

 

What you'll learn

Explain how each step of the problem-solving road map contributes to common analysis scenarios.

Discuss the use of data in the decision-making process.

Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data.

Describe the key ideas associated with structured thinking.

There are 4 modules in this course

This is the second course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. You’ll build on your understanding of the topics that were introduced in the first Google Data Analytics Certificate course. The material will help you learn how to ask effective questions to make data-driven decisions, while connecting with stakeholders’ needs. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

- Learn about effective questioning techniques that can help guide analysis. 

- Gain an understanding of data-driven decision-making and how data analysts present findings.

- Explore a variety of real-world business scenarios to support an understanding of questioning and decision-making.

- Discover how and why spreadsheets are an important tool for data analysts.

- Examine the key ideas associated with structured thinking and how they can help analysts better understand problems and develop solutions.

- Learn strategies for managing the expectations of stakeholders while establishing clear communication with a data analytics team to achieve business objectives.

Join Free- Ask Questions to Make Data-Driven Decisions

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Google Data Analytics Capstone: Complete a Case Study

 

What you'll learn

Differentiate between a capstone, case study, and a portfolio

Identify the key features and attributes of a completed case study

Apply the practices and procedures associated with the data analysis process to a given set of data

Discuss the use of case studies/portfolios when communicating with recruiters and potential employers

There are 4 modules in this course

This course is the eighth course in the Google Data Analytics Certificate. You’ll have the opportunity to complete an optional case study, which will help prepare you for the data analytics job hunt. Case studies are commonly used by employers to assess analytical skills. For your case study, you’ll choose an analytics-based scenario. You’ll then ask questions, prepare, process, analyze, visualize and act on the data from the scenario. You’ll also learn other useful job hunt skills through videos with common interview questions and responses, helpful materials to build a portfolio online, and more. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

 - Learn the benefits and uses of case studies and portfolios in the job search.

 - Explore real world job interview scenarios and common interview questions.

 - Discover how case studies can be a part of the job interview process. 

 - Examine and consider different case study scenarios. 

 - Have the chance to complete your own case study for your portfolio.

Join FREE - Google Data Analytics Capstone: Complete a Case Study

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Foundations: Data, Data, Everywhere

 

What you'll learn

Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystem

Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking

Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics

Describe the role of a data analyst with specific reference to jobs/positions

There are 5 modules in this course

This is the first course in the Google Data Analytics Certificate. These courses will equip you with the skills you need to apply to introductory-level data analyst jobs. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

- Gain an understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job. 

- Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. 

- Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. 

- Evaluate the role of analytics in the data ecosystem. 

- Conduct an analytical thinking self-assessment. 

- Explore job opportunities available to you upon program completion, and learn about best practices in the job search.

Join Free - Foundations: Data, Data, Everywhere

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Google Business Intelligence Professional Certificate

 

What you'll learn

Explore the roles of business intelligence (BI) professionals within an organization

Practice data modeling and extract, transform, load (ETL) processes that meet organizational goals 

Design data visualizations that answer business questions

Create dashboards that effectively communicate data insights to stakeholders

Professional Certificate - 3 course series

Get professional training designed by Google and take the next step in your career with advanced skills in the high-growth field of business intelligence. There are over 166,000 open jobs in business intelligence and the median salary for entry-level roles is $96,000.¹

Business intelligence professionals collect, organize, interpret, and report on data to help organizations make informed business decisions. Some responsibilities include measuring performance, tracking revenue or spending, and monitoring progress.

This certificate builds on your data analytics skills and experience to take your career to the next level. It's designed for graduates of the 

Google Data Analytics Certificate

 or people with equivalent data analytics experience. Expand your knowledge with practical, hands-on projects, featuring BigQuery, SQL, and Tableau.

After three courses, you’ll be prepared for jobs like business intelligence analyst, business intelligence engineer, business intelligence developer, and more. At under 10 hours a week, the certificate program can be completed in less than two months. Upon completion, you can apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, and Verizon.

75% of certificate graduates report a positive career outcome (e.g., new job, promotion or raise) within six months of completion2

¹Lightcast™ US Job Postings (Last 12 Months: 1/1/2022 – 12/31/2022) 

2Based on program graduate survey responses, US 2022

Applied Learning Project

This program includes over 70 hours of instruction and 50+ practice-based assessments, which will help you simulate real-world business intelligence scenarios that are critical for success in the workplace. The content is highly interactive and exclusively developed by Google employees with decades of experience in business intelligence. Through a mix of videos, assessments, and hands-on labs, you’ll get introduced to BI tools and platforms and key technical skills required for an entry-level job.

Platforms and tools you will learn include: BigQuery, SQL, Tableau

In addition to expert training and hands-on projects, you'll complete a portfolio project that you can share with potential employers to showcase your new skill set. Learn concrete skills that top employers are hiring for right now.

Join free - Google Business Intelligence Professional Certificate

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Foundations of Data Science

 

What you'll learn

Understand common careers and industries that use advanced data analytics

Investigate the impact data analysis can have on decision-making

Explain how data professionals preserve data privacy and ethics 

Develop a project plan considering roles and responsibilities of team members

There are 5 modules in this course

This is the first of seven courses in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects.   

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 functions of data analytics and data science within an organization

-Identify tools used by data professionals 

-Explore the value of data-based roles in organizations 

-Investigate career opportunities for a data professional 

-Explain a data project workflow 

-Develop effective communication skills

Join free - Foundations of Data Science

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Google Cybersecurity Professional Certificate

 

What you'll learn

Understand the importance of cybersecurity practices and their impact for organizations.

Identify common risks, threats, and vulnerabilities, as well as techniques to mitigate them.

Protect networks, devices, people, and data from unauthorized access and cyberattacks using Security Information and Event Management (SIEM) tools.

Gain hands-on experience with Python, Linux, and SQL.

Prepare for a career in cybersecurity

Receive professional-level training from Google

Demonstrate your proficiency in portfolio-ready projects

Earn an employer-recognized certificate from Google

Qualify for in-demand job titles: cybersecurity analyst, security analyst, security operations center (SOC) analyst

Professional Certificate - 8 course series

Prepare for a new career in the high-growth field of cybersecurity, no degree or experience required. Get professional training designed and delivered by subject matter experts at Google and have the opportunity to connect with top employers.

Organizations must continuously protect themselves and the people they serve from cyber-related threats, like fraud and phishing. They rely on cybersecurity to maintain the confidentiality, integrity, and availability of their internal systems and information. Cybersecurity analysts use a collection of methods and technologies to safeguard against threats and unauthorized access — and to create and implement solutions should a threat get through.

During the 8 courses in this certificate program, you’ll learn from cybersecurity experts at Google and gain in-demand skills that prepare you for entry-level roles like cybersecurity analyst, security operations center (SOC) analyst, and more. At under 10 hours per week, you can complete the certificate in less than 6 months. 

Upon completion of the certificate, you can directly apply for jobs with Google and over 150 U.S. employers, including American Express, Deloitte, Colgate-Palmolive, Mandiant (now part of Google Cloud), T-Mobile, and Walmart.

The Google Cybersecurity Certificate helps prepare you for the CompTIA Security+ exam, the industry leading certification for cybersecurity roles. You’ll earn a dual credential when you complete both.

Applied Learning Project

This program includes 170 hours of instruction and hundreds of practice-based assessments and activities that simulate real-world cybersecurity scenarios that are critical for success in the workplace. Through a mix of videos, assessments, and hands-on labs, you’ll become familiar with the cybersecurity tools, platforms, and skills required for an entry-level job.

Skills you’ll gain will include: Python, Linux, SQL, Security Information and Event Management (SIEM) tools, Intrusion Detection Systems (IDS), communication, collaboration, analysis, problem solving and more! 

Additionally, each course includes portfolio activities through which you’ll showcase examples of cybersecurity skills that you can share with potential employers. Acquire concrete skills that top employers are hiring for right now.

Join Free - Google Cybersecurity Professional Certificate

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Free Courses from Cisco

 

Free Courses from Cisco

1. Cybersecurity Operations Fundamentals Specialization

2. Introduction to Network Automation

3. Security Operations Center (SOC)

4. Network Automation Engineering Fundamentals Specialization

5. Network Security

6. DevOps for Network Automation (NetDevOps)

7. Ansible for Network Automation

8. Using APIs for Network Automation

9. Data Security

10. Introducing Model-Driven Programmability

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Machine Learning with Apache Spark (Free Course)

 

What you'll learn

Describe ML, explain its role in data engineering, summarize generative AI, discuss Spark's uses, and analyze ML pipelines and model persistence. 

Evaluate ML models, distinguish between regression, classification, and clustering models, and compare data engineering pipelines with ML pipelines. 

Construct the data analysis processes using Spark SQL, and perform regression, classification, and clustering using SparkML. 

Demonstrate connecting to Spark clusters, build ML pipelines, perform feature extraction and transformation, and model persistence. 

There are 4 modules in this course

Explore the exciting world of machine learning with this IBM course. 

Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos.

Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML.  

In practical labs, you'll utilize SparkML for regression, classification, and clustering, enabling you to construct prediction and classification models. Connect to Spark clusters, analyze SparkSQL datasets, perform ETL activities, and create ML models using Spark ML and sci-kit learn. Finally, demonstrate your acquired skills through a final assignment. 

This intermediate course is suitable for aspiring and experienced data engineers, as well as working professionals in data analysis and machine learning. Prior knowledge in Big Data, Hadoop, Spark, Python, and ETL is highly recommended for this course.

Free Course - Machine Learning with Apache Spark

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Data Science Coding Challenge: Loan Default Prediction (Free Project)

 

Objectives

Load, clean, analyze, process, and visualize data using Python and Jupyter Notebooks

Produce an end-to-end machine learning prediction model using Python and Jupyter Notebooks

Skills you’ll demonstrate

Data Science

Data Analysis

Python Programming

Machine Learning

About this Project

In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model.

Important Information:

How to register?

To participate, you’ll need to complete simple steps. First, click the “Start Project” button to register.

Next, you’ll need to create a Coursera Skills Profile, which only takes a few minutes. We’ll send you a profile link the week of the challenge.

When does the challenge start?

The coding challenge begins Tuesday, August 29th, at 8 AM (PST) and closes Thursday, August 31st, at 11:59 PM (PST). If you’re registered, you’ll receive a reminder email on the challenge start date. 

Please note this is a timed competition. Once the challenge is unlocked, you’ll have 72 hours to complete it. You can submit as many times as you would like within this timeframe.

What will the winners receive?

Participants will be evaluated based on their model’s prediction accuracy. The top 20% of participants will receive an achievement badge on their Coursera Skills Profile, highlighting their performance to recruiters.  The top 100 performers will get complimentary access to select Data Science courses.

All participants can showcase their projects to potential employers on their Coursera Skills Profile.

Winners will be notified by email the week of September 10th.

Good luck, and have fun!

Project plan

This project requires you to independently complete the following steps:

•Importing and preprocessing data

•Analyze the data

•Build machine learning models

•Evaluate machine learning models

Join Free -  Data Science Coding Challenge: Loan Default Prediction

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Machine Learning with Python

 

What you'll learn

Describe the various types of Machine Learning algorithms and when to use them  

Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression  

Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics  

 There are 6 modules in this course

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.  

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.  

You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. 

With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.  

By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

Join free - Machine Learning with Python

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Python Coding challenge - Day 70 | What is the output of the following Python code?

 

a == c: This checks if the values of a and c are equal. The values of both lists are [1, 2, 3], so a == c is True.

a is c: This checks if a and c refer to the exact same object. As mentioned before, although the values of the lists are the same, a and c are two different list objects. Therefore, a is c is False.

So, the output of this code will be: True, False

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result = max(-0.0, 0.0) print(result)

result = max(-0.0, 0.0) 
print(result)

The correct explanation is that in Python, -0.0 and 0.0 are considered equal, and the max() function does not distinguish between them based on sign. When you use max(-0.0, 0.0), the result will be the number with the higher magnitude, regardless of its sign. In this case, both -0.0 and 0.0 have the same magnitude, so the result will be the one that appears first in the arguments, which is -0.0:

result = max(-0.0, 0.0)

print(result)

Output:

-0.0

So, max(-0.0, 0.0) returns -0.0 due to the way Python handles the comparison of floating-point numbers.

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round(3 / 2) round(5 / 2)

 The round() function is used to round a number to the nearest integer. Let's calculate the results:

round(3 / 2) is equal to round(1.5), and when rounded to the nearest integer, it becomes 2.

round(5 / 2) is equal to round(2.5), and when rounded to the nearest integer, it becomes 2.

So, the results are:

round(3 / 2) equals 2.

round(5 / 2) equals 2.

Why does round(5 / 2) return 2 instead of 3? The issue here is that Python’s round method implements banker’s rounding, where all half values will be rounded to the closest even number. 

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The most difficult Python questions:

• What is the Global Interpreter Lock (GIL)? Why is it important?

• Define self in Python?

• What is pickling and unpickling in Python?

• How do you reverse a list in Python?

• What does break and continue do in Python?

• Can break and continue be used together?

• Explain generators vs iterators.

• How do you access a module written in Python from C?

• What is the difference between a List and a Tuple?

• What is __init__() in Python?

• What is the difference between a mutable data type and an immutable data type?

• Explain List, Dictionary, and Tuple comprehension with an example.

• What is monkey patching in Python?

• What is the Python “with” statement designed for?

• Why use else in try/except construct in Python?

• What are the advantages of NumPy over regular Python lists?

• What is the difference between merge, join and concatenate?

• How do you identify and deal with missing values?

• Which all Python libraries have you used for visualization?

• What is the most difficult Python question you have ever encountered?

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Python: Lists vs. Tuples vs. Sets vs. Dictionaries

 lists, tuples, sets, and dictionaries in Python based on various characteristics:

Mutability:

Lists: Mutable. You can modify, add, or remove elements after creation.

Tuples: Immutable. Once created, elements cannot be changed.

Sets: Mutable. You can add or remove elements, but each element must be unique.

Dictionaries: Mutable. You can add, modify, or remove key-value pairs.

Ordering:

Lists: Ordered. Elements are stored in a specific order and can be accessed by index.

Tuples: Ordered. Similar to lists, elements have a specific order.

Sets: Unordered. Elements have no specific order, and you cannot access them by index.

Dictionaries: Prior to Python 3.7, dictionaries were unordered. From Python 3.7 onwards, dictionaries maintain insertion order.

Duplicates:

Lists: Can contain duplicate elements.

Tuples: Can contain duplicate elements.

Sets: Cannot contain duplicate elements.

Dictionaries: Keys must be unique.

Syntax:

Lists: Defined using square brackets [].

Tuples: Defined using parentheses ().

Sets: Defined using curly braces {}.

Dictionaries: Defined using curly braces {} with key-value pairs separated by colons.

Use Cases:

Lists: Use when you need a mutable, ordered collection with the possibility of duplicate elements.

Tuples: Use when you need an immutable, ordered collection. Suitable for situations where data should not be changed.

Sets: Use when you need an unordered collection of unique elements and order doesn't matter.

Dictionaries: Use when you need a mutable collection of key-value pairs, providing fast lookup based on keys.

Example:

my_list = [1, 2, 3]

my_tuple = (4, 5, 6)

my_set = {7, 8, 9}

my_dict = {'a': 10, 'b': 11, 'c': 12}

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IBM Full Stack Software Developer Professional Certificate

 

Prepare for a career as a full stack developer. Gain the in-demand skills and hands-on experience to get job-ready in less than 4 months. No prior experience required.

What you'll learn

Master the most up-to-date practical skills and tools that full stack developers use in their daily roles

Learn how to deploy and scale applications using Cloud Native methodologies and tools such as Containers, Kubernetes, Microservices, and Serverless

Develop software with front-end development languages and tools such as HTML, CSS, JavaScript, React, and Bootstrap

Build your GitHub portfolio by applying your skills to multiple labs and hands-on projects, including a capstone

Professional Certificate - 12 course series

Prepare for a career in the high-growth field of software development. In this program, you’ll learn in-demand skills and tools used by professionals for front-end, back-end, and cloud native application development to get job-ready in less than 4 months, with no prior experience needed. 

Full stack refers to the end-to-end computer system application, including the front end and back end coding. This Professional Certificate covers development for both of these scenarios. Cloud native development refers to developing a program designed to work on cloud architecture. The flexibility and adaptability that full stack and cloud native developers provide make them highly sought after in this digital world. 

You’ll  learn how to build, deploy, test, run, and manage full stack cloud native applications. Technologies covered includes Cloud foundations, GitHub, Node.js, React, CI/CD, Containers, Docker, Kubernetes, OpenShift, Istio, Databases, NoSQL, Django ORM, Bootstrap, Application Security, Microservices, Serverless computing, and more. 

After completing the program you will have developed several applications using front-end and back-end technologies and deployed them on a cloud platform using Cloud Native methodologies. You will publish these projects through your GitHub repository to share your portfolio with your peers and prospective employers.

This program is ACE® recommended—when you complete, you can earn up to 18 college credits.

Applied Learning Project

Throughout the courses in the Professional Certificate, you will develop a portfolio of hands-on projects involving various popular technologies and programming languages in Full Stack Cloud Application Development. These projects include creating:

HTML pages on Cloud Object Storage

An interest rate calculator using HTML, CSS, and JavaScript

An AI program deployed on Cloud Foundry using DevOps principles and CI/CD toolchains with a NoSQL database

A Node.js back-end application and a React front-end application

A containerized guestbook app packaged with Docker deployed with Kubernetes and managed with OpenShift

A Python app bundled as a package

A database-powered application using Django ORM and Bootstrap

An app built using Microservices & Serverless

A scalable, Cloud Native Full Stack application using the technologies learned in previous courses

You will publish these projects through your GitHub repository to share your skills with your peers and prospective employers.

Join - IBM Full Stack Software Developer Professional Certificate

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Object-Oriented Python: Inheritance and Encapsulation

 

What you'll learn

How to architect larger programs using object-oriented principles

Re-use parts of classes using inheritance

Encapsulate relevant information and methods in a class

There are 4 modules in this course

Code and run your first python program in minutes without installing anything!

This course is designed for learners with limited coding experience, providing a solid foundation of not just python, but core Computer Science topics that can be transferred to other languages. The modules in this course cover inheritance, encapsulation, polymorphism, and other object-related topics. Completion of the prior 3 courses in this specialization is recommended.

To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You'll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours.

Join free - Object-Oriented Python: Inheritance and Encapsulation

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Python Coding challenge - Day 69 | What is the output of the following Python code?

 

The above code of the list my_list using slicing and assigns a new set of values [7, 8, 9] to the sliced portion. Here's a step-by-step breakdown:

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

This initializes a list with the values [1, 2, 3, 4, 5].

my_list[1:3] = [7, 8, 9]

This slices the elements at index 1 and 2 (exclusive) of my_list and replaces them with the values [7, 8, 9]. After this line executes, my_list becomes [1, 7, 8, 9, 4, 5].

print(my_list)

This prints the modified list:

[1, 7, 8, 9, 4, 5]

So, the final output is [1, 7, 8, 9, 4, 5].

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Python Coding challenge - Day 68 | What is the output of the following Python code?

 

The expression 3 * 2 ** 3 involves exponentiation and multiplication. The exponentiation operator ** has higher precedence than multiplication.

Let's break it down step by step:

3. 2∗∗32∗∗3 is 2×2×22×2×2, which equals 88.

4. 3×83×8 is 2424.

So, the output of the code: print(3 * 2 ** 3)

24

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