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Ahmad Varasteh
This Guided Project Data Analysis in Python: Using Pandas DataFrames is for those who are interested in using python for data science in practice.
Read more
This Guided Project Data Analysis in Python: Using Pandas DataFrames is for those who are interested in using python for data science in practice.
Read more
This Guided Project Data Analysis in Python: Using Pandas DataFrames is for those who are interested in using python for data science in practice.
In this 90-minute Guided Project, learn how to import and visualize an IMDb data set in Pandas. You will learn how to import JSON data into a Pandas Dataframe and apply the data preparation process to ensure the data is ready for analysis.
To achieve this, we will explore the famous IMDb Movies dataset. We will start with importing our JSON data into a Pandas data frame. After applying some data preparation steps such as dropping and renaming some columns, we are going to start our data analysis by answering some analytical questions about the dataset. This Guided Project is unique as it focuses on how to use Pandas functions to filter, clean, aggregate, and visualize data. In order to be successful in this project, you will need to have basic knowledge of the python programming language. basic Python syntax, simple Python operators, and Python control structures are the main prerequisite of this guided project.
Register for this course and see more details by visiting:
OpenCourser.com/course/bf034o/data
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What's inside
Syllabus
Project Overview
This Guided Project Data Analysis in Python: Using Pandas DataFrames is for those who are interested in using Python for data science in practice. In this Guided Project, learn how to import and visualize an IMDb data set in Pandas. You will learn how to import JSON data into a Pandas dataframe and apply the data preparation process to ensure the data is ready for analysis. To achieve this, we will explore the famous IMDb Movies dataset. We will start with importing our JSON data into a Pandas data frame. After applying some data preparation steps such as dropping and renaming some columns, we are going to start our data analysis by answering some analytical questions about the dataset. This Guided Project is unique as it focuses on how to use Pandas functions to filter, clean, aggregate, and visualize data. In order to be successful in this project, you will need to have basic knowledge of the python programming language. basic Python syntax, simple Python operators, and Python control structures are the main prerequisite of this guided project.
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Know what's good ,
what to watch for , and
possible dealbreakers
Appropriate for learners interested in applying Python for data science in practice
Builds a strong foundation for beginners in using Pandas data frames
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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 Data Analysis in Python: Using Pandas DataFrames with these
activities:
Read 'Python for Data Analysis' by Wes McKinney
Show steps
Gain a comprehensive understanding of Python for data analysis through a well-regarded book.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Read chapters relevant to the course topics, such as data manipulation, data visualization, and statistical modeling
-
Complete exercises and examples provided in the book to reinforce your understanding
-
Refer to the book for additional reference and guidance throughout the course
Review Python syntax and control structures
Show steps
Review basic Python syntax and control structures to strengthen your programming foundation.
Browse courses on
Python Syntax
Show steps
-
Go over Python variables, data types, and operators
-
Practice writing simple Python expressions and statements
-
Review conditional statements (if-else), loops (for, while), and functions
Solve coding exercises on Python data manipulation
Show steps
Engage in coding exercises to improve your ability to manipulate data in Python.
Show steps
-
Find coding exercises on platforms like LeetCode or HackerRank
-
Focus on exercises that cover data structures like lists, dictionaries, and dataframes
-
Practice implementing functions for data sorting, filtering, and aggregation
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group for Python data analysis
Show steps
Engage with peers to discuss concepts, share knowledge, and work through challenges together.
Show steps
-
Find a study group or create one with fellow learners
-
Regularly meet to discuss course materials, practice exercises, and share resources
-
Collaborate on projects and provide constructive feedback to each other
Follow tutorials on advanced Pandas techniques
Show steps
Expand your knowledge of Pandas by exploring advanced techniques through guided tutorials.
Show steps
-
Identify tutorials that cover topics like data merging, data reshaping, and time series analysis
-
Follow the tutorials step-by-step, implementing the techniques in your own Python environment
-
Experiment with different Pandas methods and functions to enhance your understanding
Build a small data visualization project using Pandas
Show steps
Create a data visualization project to apply your knowledge of Pandas and data visualization techniques.
Show steps
-
Choose a dataset that aligns with your interests
-
Load the dataset into a Pandas dataframe and explore its contents
-
Apply Pandas functions to clean, prepare, and analyze the data
-
Create visualizations using libraries like Matplotlib or Seaborn to represent your findings
Contribute to an open-source Python data analysis project
Show steps
Gain practical experience and contribute to the community by working on an open-source data analysis project.
Show steps
-
Identify an open-source project that aligns with your interests
-
Familiarize yourself with the project's codebase and documentation
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation updates
-
Submit pull requests with your contributions, following the project's guidelines
Read 'Python for Data Analysis' by Wes McKinney
Show steps
Gain a comprehensive understanding of Python for data analysis through a well-regarded book.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to the course topics, such as data manipulation, data visualization, and statistical modeling
- Complete exercises and examples provided in the book to reinforce your understanding
- Refer to the book for additional reference and guidance throughout the course
Review Python syntax and control structures
Show steps
Review basic Python syntax and control structures to strengthen your programming foundation.
Browse courses on
Python Syntax
Show steps
Show steps
Show steps
- Go over Python variables, data types, and operators
- Practice writing simple Python expressions and statements
- Review conditional statements (if-else), loops (for, while), and functions
Solve coding exercises on Python data manipulation
Show steps
Engage in coding exercises to improve your ability to manipulate data in Python.
Show steps
Show steps
Show steps
- Find coding exercises on platforms like LeetCode or HackerRank
- Focus on exercises that cover data structures like lists, dictionaries, and dataframes
- Practice implementing functions for data sorting, filtering, and aggregation
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group for Python data analysis
Show steps
Engage with peers to discuss concepts, share knowledge, and work through challenges together.
Show steps
Show steps
Show steps
- Find a study group or create one with fellow learners
- Regularly meet to discuss course materials, practice exercises, and share resources
- Collaborate on projects and provide constructive feedback to each other
Follow tutorials on advanced Pandas techniques
Show steps
Expand your knowledge of Pandas by exploring advanced techniques through guided tutorials.
Show steps
Show steps
Show steps
- Identify tutorials that cover topics like data merging, data reshaping, and time series analysis
- Follow the tutorials step-by-step, implementing the techniques in your own Python environment
- Experiment with different Pandas methods and functions to enhance your understanding
Build a small data visualization project using Pandas
Show steps
Create a data visualization project to apply your knowledge of Pandas and data visualization techniques.
Show steps
Show steps
Show steps
- Choose a dataset that aligns with your interests
- Load the dataset into a Pandas dataframe and explore its contents
- Apply Pandas functions to clean, prepare, and analyze the data
- Create visualizations using libraries like Matplotlib or Seaborn to represent your findings
Contribute to an open-source Python data analysis project
Show steps
Gain practical experience and contribute to the community by working on an open-source data analysis project.
Show steps
Show steps
Show steps
- Identify an open-source project that aligns with your interests
- Familiarize yourself with the project's codebase and documentation
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation updates
- Submit pull requests with your contributions, following the project's guidelines
Career center
Learners who complete Data Analysis in Python: Using Pandas DataFrames will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help organizations make informed decisions. This course can help you develop the skills needed to succeed in this role by providing you with a solid foundation in data analysis using Python and Pandas. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis, machine learning, and statistics to solve business problems. This course can help you build a foundation in data analysis using Python and Pandas, which are essential skills for Data Scientists. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make informed decisions. This course can help you develop the skills needed to succeed in this role by providing you with a solid foundation in data analysis using Python and Pandas. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Financial Analyst
Financial Analyst
Financial Analysts use data to make recommendations about investments and financial decisions. This course can help you develop the skills needed to succeed in this role by providing you with a solid foundation in data analysis using Python and Pandas. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and trends. This course can help you develop the skills needed to succeed in this role by providing you with a solid foundation in data analysis using Python and Pandas. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to solve problems and improve efficiency in organizations. This course can help you develop the skills needed to succeed in this role by providing you with a solid foundation in data analysis using Python and Pandas. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Statistician
Statistician
Statisticians use data to collect, analyze, interpret, and present data. This course can help you develop the skills needed to succeed in this role by providing you with a solid foundation in data analysis using Python and Pandas. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you build a foundation in data analysis using Python and Pandas, which are essential skills for Software Engineers. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Web Developer
Web Developer
Web Developers design, develop, and maintain websites and web applications. This course can help you build a foundation in data analysis using Python and Pandas, which are essential skills for Web Developers. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course can help you build a foundation in data analysis using Python and Pandas, which are essential skills for Data Engineers. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course can help you build a foundation in data analysis using Python and Pandas, which are essential skills for Machine Learning Engineers. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. This course can help you build a foundation in data analysis using Python and Pandas, which are essential skills for Database Administrators. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. This course can help you build a foundation in data analysis using Python and Pandas, which are essential skills for Actuaries. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematics and statistics to analyze financial data. This course can help you build a foundation in data analysis using Python and Pandas, which are essential skills for Quantitative Analysts. You will learn how to import and visualize data, as well as how to use Pandas functions to filter, clean, aggregate, and visualize data.
For more career information including salaries, visit:
OpenCourser.com/course/bf034o/data
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
Data Analysis in Python: Using Pandas DataFrames.
Serves as a comprehensive guide to data analysis using Python. It covers data manipulation, data visualization, and statistical modeling. As a beginner-friendly guide, it useful reference for those new to data analysis.
Offers a comprehensive introduction to data science, covering the fundamental concepts and techniques. It is an excellent resource for those with little to no prior experience in data science, as it provides a solid foundation for understanding data analysis.
Focuses on the use of Python for data analysis. It covers data wrangling, data exploration, and data visualization. It practical guide for those seeking to use Python in data analysis projects.
Provides a practical introduction to machine learning using Python. It covers the fundamental concepts and algorithms of machine learning, making it a valuable resource for those interested in exploring machine learning.
Serves as an introduction to data science and machine learning using Python. It covers data analysis, machine learning algorithms, and data visualization. It useful resource for those seeking a comprehensive understanding of data science and machine learning.
Save
Provides a foundation in Python for data science. It covers data manipulation, data visualization, and data analysis. It useful resource for those new to Python and looking to use it for data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bf034o/data
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ahmad Varasteh
Language
English
Good to know
Appropriate for learners interested in applying Python for data science in practice
Builds a strong foundation for beginners in using Pandas data frames
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