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Yaroslav Vyklyuk
One of the most commonly used tools in data science, pandas is a Python library used to load, process, and analyze datasets using SQL-like queries. Pandas offers several advantages, such as data representation, simpler lines of code, and the ability to handle large sets of data. A number of academic and commercial domains, including finance, economics, statistics, web analytics, and other entities, use pandas as part of their data analytics toolkit.
Read more
One of the most commonly used tools in data science, pandas is a Python library used to load, process, and analyze datasets using SQL-like queries. Pandas offers several advantages, such as data representation, simpler lines of code, and the ability to handle large sets of data. A number of academic and commercial domains, including finance, economics, statistics, web analytics, and other entities, use pandas as part of their data analytics toolkit.
Read more
One of the most commonly used tools in data science, pandas is a Python library used to load, process, and analyze datasets using SQL-like queries. Pandas offers several advantages, such as data representation, simpler lines of code, and the ability to handle large sets of data. A number of academic and commercial domains, including finance, economics, statistics, web analytics, and other entities, use pandas as part of their data analytics toolkit.
In this hands-on guided project, you will learn to perform preliminary data analysis and credit risk analysis on a credit card client dataset using the Python library pandas. You will learn how to import required libraries, explore datasets, analyze data, and visualize the dataset. By the end of this project, you will have learned the fundamentals of data analysis using pandas and developed job-ready skills.
You will be provided with access to a Cloud based-IDE which has all of the required software, including Python pandas, pre-installed. All you need is a recent version of a modern web browser to complete this project.
Register for this course and see more details by visiting:
OpenCourser.com/course/sret97/guided
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What's inside
Learning objectives
- After completing this hands-on guided project, you’ll be able to:
- Explore a dataset and perform calculations using its data
- Build dependencies among existing attributes of a dataset
- Visualize the results of data analysis with various plot types
- After completing this hands-on guided project, you’ll be able to:
- Explore a dataset and perform calculations using its data
- Build dependencies among existing attributes of a dataset
- Visualize the results of data analysis with various plot types
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches hands-on data analysis using Python's pandas library, which is a valuable skill for data scientists
Covers fundamental concepts of data analysis, making it accessible to beginners
Provides practical experience with a real-world dataset, preparing learners for job-related tasks
Utilized by academic and commercial domains, including finance and statistics, indicating its relevance in various fields
May require learners to have some prior knowledge of Python or data analysis
Save this course
Save Guided Project: Secure Analysis of a Credit Card Dataset to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Guided Project: Secure Analysis of a Credit Card Dataset with these
activities:
Organize Class Notes and Materials
Show steps
Improves retention and understanding by organizing lecture notes, assignments, and quizzes for easy review.
Show steps
-
Create a dedicated folder or notebook for the course materials.
-
Organize lecture notes, assignments, and quizzes by topic.
-
Review the materials regularly to reinforce learning.
Review Basic Python Concepts
Show steps
Strengthens the foundation for Pandas by refreshing essential Python concepts, ensuring a seamless learning experience.
Browse courses on
Python
Show steps
-
Review the basics of Python syntax and data types.
-
Practice writing simple Python scripts.
Review 'Python Data Analysis Library'
Show steps
Deepens your understanding of the Python pandas library as it will provide a deeper dive into its functionalities and capabilities.
View
Football Analytics with Python & R: Learning...
on Amazon
Show steps
-
Read the first three chapters of 'Python Data Analysis Library'.
-
Install the Python Pandas library on your local machine.
-
Create a sample dataset and explore it using basic Pandas commands.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete the Pandas Tutorial on Kaggle
Show steps
Provides hands-on experience with Pandas, reinforcing the concepts learned from the book and the lectures.
Browse courses on
Pandas Library
Show steps
-
Go to the Kaggle website and search for the 'Pandas Tutorial'.
-
Follow the tutorial step-by-step, completing the exercises and quizzes.
Solve Pandas Coding Challenges
Show steps
Reinforces Pandas concepts through practical coding exercises, improving problem-solving and coding proficiency.
Browse courses on
Pandas Library
Show steps
-
Find a website or platform that offers Pandas coding challenges.
-
Solve a series of challenges of varying difficulty levels.
Create a Pandas cheat sheet
Show steps
Helps solidify your understanding of Pandas syntax and functionality by creating a personalized reference guide.
Browse courses on
Pandas Library
Show steps
-
Review the Pandas documentation and lecture notes.
-
Identify the most commonly used Pandas functions and methods.
-
Create a cheat sheet that summarizes the syntax and usage of these functions.
Analyze a real-world dataset using Pandas
Show steps
Applies Pandas skills to a practical scenario, enhancing problem-solving abilities and data analysis proficiency.
Browse courses on
Pandas Library
Show steps
-
Download a real-world dataset related to your interests or industry.
-
Use Pandas to clean, explore, and analyze the dataset.
-
Visualize the results of your analysis using plots and charts.
-
Write a report summarizing your findings.
Develop a Data Analysis Application
Show steps
Enhances practical skills by applying Pandas to a real-world project, fostering independence and creativity.
Browse courses on
Pandas Library
Show steps
-
Identify a problem or opportunity that can be addressed using data analysis.
-
Design and implement a data analysis solution using Pandas.
-
Deploy the application and evaluate its performance.
Organize Class Notes and Materials
Show steps
Improves retention and understanding by organizing lecture notes, assignments, and quizzes for easy review.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for the course materials.
- Organize lecture notes, assignments, and quizzes by topic.
- Review the materials regularly to reinforce learning.
Review Basic Python Concepts
Show steps
Strengthens the foundation for Pandas by refreshing essential Python concepts, ensuring a seamless learning experience.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python syntax and data types.
- Practice writing simple Python scripts.
Review 'Python Data Analysis Library'
Show steps
Deepens your understanding of the Python pandas library as it will provide a deeper dive into its functionalities and capabilities.
View
Football Analytics with Python & R: Learning...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of 'Python Data Analysis Library'.
- Install the Python Pandas library on your local machine.
- Create a sample dataset and explore it using basic Pandas commands.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete the Pandas Tutorial on Kaggle
Show steps
Provides hands-on experience with Pandas, reinforcing the concepts learned from the book and the lectures.
Browse courses on
Pandas Library
Show steps
Show steps
Show steps
- Go to the Kaggle website and search for the 'Pandas Tutorial'.
- Follow the tutorial step-by-step, completing the exercises and quizzes.
Solve Pandas Coding Challenges
Show steps
Reinforces Pandas concepts through practical coding exercises, improving problem-solving and coding proficiency.
Browse courses on
Pandas Library
Show steps
Show steps
Show steps
- Find a website or platform that offers Pandas coding challenges.
- Solve a series of challenges of varying difficulty levels.
Create a Pandas cheat sheet
Show steps
Helps solidify your understanding of Pandas syntax and functionality by creating a personalized reference guide.
Browse courses on
Pandas Library
Show steps
Show steps
Show steps
- Review the Pandas documentation and lecture notes.
- Identify the most commonly used Pandas functions and methods.
- Create a cheat sheet that summarizes the syntax and usage of these functions.
Analyze a real-world dataset using Pandas
Show steps
Applies Pandas skills to a practical scenario, enhancing problem-solving abilities and data analysis proficiency.
Browse courses on
Pandas Library
Show steps
Show steps
Show steps
- Download a real-world dataset related to your interests or industry.
- Use Pandas to clean, explore, and analyze the dataset.
- Visualize the results of your analysis using plots and charts.
- Write a report summarizing your findings.
Develop a Data Analysis Application
Show steps
Enhances practical skills by applying Pandas to a real-world project, fostering independence and creativity.
Browse courses on
Pandas Library
Show steps
Show steps
Show steps
- Identify a problem or opportunity that can be addressed using data analysis.
- Design and implement a data analysis solution using Pandas.
- Deploy the application and evaluate its performance.
Career center
Learners who complete Guided Project: Secure Analysis of a Credit Card Dataset will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts examine data to extract meaningful insights and present their findings to various stakeholders. With the help of their data-driven analysis, they play a key role in helping their employers understand their customers, improve their operations, and mitigate risks. By enabling their employers to make data-informed decisions, Data Analysts can have a significant impact on the success of their organizations. This course is an excellent starting point for anyone who aspires to be a Data Analyst. It provides a solid foundation in data analysis using Python and pandas, which are essential tools in this field.
Financial Analyst
Financial Analyst
Financial Analysts specialize in the analysis of financial data. They use their skills in data analysis to assess the financial performance of companies, make investment recommendations, and develop financial models. The skills you learn in this course, such as data exploration, data analysis, and data visualization, will be highly valuable to you as a Financial Analyst. More specifically, financial analysts must often work with large datasets, and must be able to extract meaningful insights from data. This course can help you develop the skills necessary to do so.
Market Researcher
Market Researcher
Market Researchers are responsible for gathering and interpreting data about markets, customers, and competitors. They use this information to help their clients make informed decisions about product development, marketing campaigns, and other business strategies. The skills you learn in this course, such as data analysis and data visualization, will be essential to your success as a Market Researcher. Specifically, the ability to analyze data and extract meaningful insights will be highly valuable in this role.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts help organizations improve their efficiency and effectiveness. They use data analysis and modeling to identify and solve problems in areas such as supply chain management, logistics, and scheduling. The skills you learn in this course, such as data analysis and data visualization, will be useful to you as an Operations Research Analyst. Specifically, this course will help you develop the skills necessary to analyze data and identify trends.
Quantitative Trader
Quantitative Trader
Quantitative Traders use mathematical and statistical models to make trading decisions. They use data analysis to identify and exploit opportunities in the financial markets. The skills you learn in this course, such as data analysis and data visualization, may be useful to you as a Quantitative Trader. Specifically, this course will help you develop the skills necessary to analyze data and identify trends.
Business Analyst
Business Analyst
Business Analysts identify and solve problems in business processes. They use data analysis to understand the current state of a business and to develop recommendations for improvement. The skills you learn in this course, such as data analysis and data visualization, will be valuable to you as a Business Analyst. Specifically, this course will help you develop the skills necessary to analyze data and identify opportunities for improvement.
Data Scientist
Data Scientist
Data Scientists use data analysis to extract meaningful insights from data. They use this information to help their employers make informed decisions about product development, marketing campaigns, and other business strategies. The skills you learn in this course, such as data exploration, data analysis, and data visualization, will be essential to your success as a Data Scientist. Specifically, the ability to analyze data and extract meaningful insights will be highly valuable in this role.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use this information to draw conclusions about the world around us. The skills you learn in this course, such as data analysis and data visualization, will be essential to your success as a Statistician. Specifically, the ability to analyze data and extract meaningful insights will be highly valuable in this role.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use data analysis to identify and solve problems in software systems. The skills you learn in this course, such as data analysis and data visualization, may be useful to you as a Software Engineer. Specifically, this course will help you develop the skills necessary to analyze data and identify trends.
Actuary
Actuary
Actuaries use data analysis to assess risk and uncertainty. They use this information to help their clients make informed decisions about insurance, pensions, and other financial products. The skills you learn in this course, such as data analysis and data visualization, may be useful to you as an Actuary. Specifically, this course will help you develop the skills necessary to analyze data and identify trends.
Data Architect
Data Architect
Data Architects design and manage data systems. They use data analysis to understand the data needs of their organizations and to develop data systems that meet those needs. The skills you learn in this course, such as data analysis and data visualization, may be useful to you as a Data Architect. Specifically, this course will help you develop the skills necessary to analyze data and identify trends.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They use data analysis to identify and solve problems in database systems. The skills you learn in this course, such as data analysis and data visualization, may be useful to you as a Database Administrator. Specifically, this course will help you develop the skills necessary to analyze data and identify trends.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect their organizations from cyberattacks. They use data analysis to identify and mitigate vulnerabilities in their organizations' IT systems. The skills you learn in this course, such as data analysis and data visualization, may be useful to you as an Information Security Analyst. Specifically, this course will help you develop the skills necessary to analyze data and identify trends.
Financial Risk Manager
Financial Risk Manager
Financial Risk Managers identify and mitigate financial risks. They use data analysis to assess the financial health of their organizations and to develop strategies to reduce risk. The skills you learn in this course, such as data analysis and data visualization, may be useful to you as a Financial Risk Manager. Specifically, this course will help you develop the skills necessary to analyze data and identify trends.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data analysis to improve the decision-making process within their organizations. They use data to identify trends, patterns, and opportunities. The skills you learn in this course, such as data analysis and data visualization, will be essential to your success as a Business Intelligence Analyst. Specifically, the ability to analyze data and extract meaningful insights will be highly valuable in this role.
For more career information including salaries, visit:
OpenCourser.com/course/sret97/guided
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Guided Project: Secure Analysis of a Credit Card Dataset.
Provides a comprehensive overview of machine learning concepts and techniques, using Python as the primary programming language. It covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation.
An excellent resource for gaining hands-on experience with machine learning libraries in Python. It covers topics such as data preprocessing, model selection, and deep learning.
Provides a comprehensive introduction to data science concepts and techniques, without relying on any specific programming language. It covers topics such as data collection, data analysis, and machine learning.
A comprehensive guide to using machine learning techniques for risk management, covering topics such as risk assessment, risk modeling, and risk management.
A comprehensive guide to using Python for data analysis, including topics such as data manipulation, data visualization, and statistical modeling.
A practical guide to credit risk management, covering topics such as credit risk identification, measurement, and mitigation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sret97/guided
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Effort
1 hours per week for 1 week
Level
Intermediate
Via
edX
Institution
IBM
Instructor
Yaroslav Vyklyuk
Session
April 30, 2024
- April 30, 2025
Language
English
Transcript
English
Good to know
Teaches hands-on data analysis using Python's pandas library, which is a valuable skill for data scientists
Covers fundamental concepts of data analysis, making it accessible to beginners
Provides practical experience with a real-world dataset, preparing learners for job-related tasks
Utilized by academic and commercial domains, including finance and statistics, indicating its relevance in various fields
May require learners to have some prior knowledge of Python or data analysis
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