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David Dalsveen
Visualizing data patterns often involves re-arrangement and elimination to determine patterns. For example, in a list of data with yearly rainfall amounts, to quickly determine the years with the most rainfall, the data can be sorted according to rainfall in descending order. A filter could be used to limit the amount of data observed, for example, to only show rainfall amounts greater than an inch. A merge can be used to join two datasets together, for example rainfall and temperature data from two different sources. The ability to sort, merge and filter data has always existed using SQL with database data, now it can be done in application memory space using Python.
Read more
Visualizing data patterns often involves re-arrangement and elimination to determine patterns. For example, in a list of data with yearly rainfall amounts, to quickly determine the years with the most rainfall, the data can be sorted according to rainfall in descending order. A filter could be used to limit the amount of data observed, for example, to only show rainfall amounts greater than an inch. A merge can be used to join two datasets together, for example rainfall and temperature data from two different sources. The ability to sort, merge and filter data has always existed using SQL with database data, now it can be done in application memory space using Python.
Read more
Visualizing data patterns often involves re-arrangement and elimination to determine patterns. For example, in a list of data with yearly rainfall amounts, to quickly determine the years with the most rainfall, the data can be sorted according to rainfall in descending order. A filter could be used to limit the amount of data observed, for example, to only show rainfall amounts greater than an inch. A merge can be used to join two datasets together, for example rainfall and temperature data from two different sources. The ability to sort, merge and filter data has always existed using SQL with database data, now it can be done in application memory space using Python.
In this course, you will create an application that reads data from two CSV files. You will learn how to merge, sort, and filter the data to ultimately produce a regression plot to determine a possible correlation between two data sets.
Register for this course and see more details by visiting:
OpenCourser.com/course/i1agz1/merge
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What's inside
Syllabus
Python Pandas Merge Sort Filter
Visualizing data patterns often involves re-arrangement and elimination to determine patterns. For example, in a list of data with yearly rainfall amounts, to quickly determine the years with the most rainfall, the data can be sorted according to rainfall in descending order. A filter could be used to limit the amount of data observed, for example, to only show rainfall amounts greater than an inch. A merge can be used to join two datasets together, for example rainfall and temperature data from two different sources. The ability to sort, merge and filter data has always existed using SQL with database data, now it can be done in application memory space using Python.
In this course, you will create an application that reads data from two CSV files. You will learn how to merge, sort, and filter the data to ultimately produce a regression plot to determine a possible correlation between two data sets.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines data visualization techniques, which are essential for finding patterns in datasets
Covers concepts such as sorting, merging, and filtering, which are fundamental to data manipulation
Teaches Python Pandas library, which is widely used for data manipulation and analysis
Includes hands-on exercises for practical application of concepts
Suitable for beginners with little to no prior knowledge in data manipulation
Led by David Dalsveen, an experienced instructor in data science and visualization
Save this course
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Reviews summary
Introductory python for data manipulation
Learners say this introductory course in Python for data manipulation (Pandas) is straightforward, clear, and engaging. Students describe the assignments as good for beginners but acknowledge that more advanced learners may not find the course challenging enough. Overall, students recommend this course for students starting out with Python.
Suitable for those new to Python.
"Rally quick and simple."
"Does what it says on the tin!"
"I loved this course because of the clarity and simplicity"
Rhyme's automatic fullscreen may distract learners.
"The Rhyme environment has a function to automatically switch fullscreen on the video which makes simultaneous coding distracting"
"Rhyme platform feels bug, demo video screen automatically becoming full screen it was really annoying and made coding uneasy. Please fix this."
May not be challenging enough for advanced learners.
"there could be more stuff, given this price"
"only for beginners"
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 Merge, Sort and Filter Data in Python Pandas with these
activities:
Brush up on data analysis fundamentals
Show steps
Recalling the basics of data analysis and Python libraries will enhance your comprehension of the course materials.
Browse courses on
Data Analysis
Show steps
-
Review your notes or textbooks on data analysis.
-
Practice basic data analysis tasks in Python using libraries like Pandas and NumPy.
Revise Python programming basics
Show steps
Refreshing your Python programming skills will provide a strong foundation for understanding the course materials, especially the implementation of data analysis techniques.
Browse courses on
Python Programming
Show steps
-
Review online tutorials or books on Python programming basics.
-
Practice writing Python code to implement simple data structures and algorithms.
Organize course materials for effective learning
Show steps
Properly organizing course materials will help you stay on track, improve retention, and maximize learning outcomes.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Categorize and file materials according to topics or modules.
-
Take notes during lectures and add them to your organized materials.
-
Review your organized materials regularly to reinforce learning.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve data manipulation exercises
Show steps
Engaging in data manipulation exercises will reinforce the concepts covered in the course, improving your proficiency in handling and transforming data.
Browse courses on
Data Manipulation
Show steps
-
Find online resources or textbooks with data manipulation exercises.
-
Practice solving these exercises regularly.
Participate in a study group
Show steps
Engaging with peers in a study group provides opportunities to discuss concepts, exchange ideas, and reinforce learning through collaboration.
Show steps
-
Find or form a study group with fellow students taking the course.
-
Meet regularly to discuss course materials, work on assignments, and share insights.
Assist with a data analytics project
Show steps
Participating in a data analytics project will provide you with hands-on experience and expose you to real-world applications of data analysis.
Browse courses on
Data Analytics
Show steps
-
Identify organizations or research groups working on data analytics projects.
-
Contact them to inquire about volunteering opportunities.
-
Assist with data collection, analysis, or visualization tasks as needed.
Create a data visualization dashboard
Show steps
Building a data visualization dashboard will allow you to apply the techniques learned in the course to a real-world scenario, solidifying your understanding of data presentation.
Browse courses on
Data Visualization
Show steps
-
Gather a dataset of your choice.
-
Choose appropriate visualization techniques to represent the data.
-
Use Python libraries like Plotly or Seaborn to create interactive dashboards.
Explore online tutorials on advanced data analysis techniques
Show steps
Delving into online tutorials on advanced data analysis techniques will extend your knowledge beyond the scope of the course, enhancing your proficiency in data handling and analysis.
Browse courses on
Advanced Data Analysis
Show steps
-
Identify online platforms or resources that offer tutorials on advanced data analysis techniques.
-
Select tutorials that align with your learning goals.
-
Follow the tutorials and complete the exercises provided.
Develop a data analytics application
Show steps
Creating a data analytics application will challenge you to apply the concepts comprehensively and showcase your ability to solve real-world problems using data analysis techniques.
Browse courses on
Data Analytics
Show steps
-
Define a problem statement and gather the required data.
-
Design and develop an application using Python and relevant libraries.
-
Test and iterate on your application to improve its functionality.
-
Present your application and its findings to demonstrate your understanding and skills.
Brush up on data analysis fundamentals
Show steps
Recalling the basics of data analysis and Python libraries will enhance your comprehension of the course materials.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Review your notes or textbooks on data analysis.
- Practice basic data analysis tasks in Python using libraries like Pandas and NumPy.
Revise Python programming basics
Show steps
Refreshing your Python programming skills will provide a strong foundation for understanding the course materials, especially the implementation of data analysis techniques.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review online tutorials or books on Python programming basics.
- Practice writing Python code to implement simple data structures and algorithms.
Organize course materials for effective learning
Show steps
Properly organizing course materials will help you stay on track, improve retention, and maximize learning outcomes.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Categorize and file materials according to topics or modules.
- Take notes during lectures and add them to your organized materials.
- Review your organized materials regularly to reinforce learning.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve data manipulation exercises
Show steps
Engaging in data manipulation exercises will reinforce the concepts covered in the course, improving your proficiency in handling and transforming data.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Find online resources or textbooks with data manipulation exercises.
- Practice solving these exercises regularly.
Participate in a study group
Show steps
Engaging with peers in a study group provides opportunities to discuss concepts, exchange ideas, and reinforce learning through collaboration.
Show steps
Show steps
Show steps
- Find or form a study group with fellow students taking the course.
- Meet regularly to discuss course materials, work on assignments, and share insights.
Assist with a data analytics project
Show steps
Participating in a data analytics project will provide you with hands-on experience and expose you to real-world applications of data analysis.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Identify organizations or research groups working on data analytics projects.
- Contact them to inquire about volunteering opportunities.
- Assist with data collection, analysis, or visualization tasks as needed.
Create a data visualization dashboard
Show steps
Building a data visualization dashboard will allow you to apply the techniques learned in the course to a real-world scenario, solidifying your understanding of data presentation.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather a dataset of your choice.
- Choose appropriate visualization techniques to represent the data.
- Use Python libraries like Plotly or Seaborn to create interactive dashboards.
Explore online tutorials on advanced data analysis techniques
Show steps
Delving into online tutorials on advanced data analysis techniques will extend your knowledge beyond the scope of the course, enhancing your proficiency in data handling and analysis.
Browse courses on
Advanced Data Analysis
Show steps
Show steps
Show steps
- Identify online platforms or resources that offer tutorials on advanced data analysis techniques.
- Select tutorials that align with your learning goals.
- Follow the tutorials and complete the exercises provided.
Develop a data analytics application
Show steps
Creating a data analytics application will challenge you to apply the concepts comprehensively and showcase your ability to solve real-world problems using data analysis techniques.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Define a problem statement and gather the required data.
- Design and develop an application using Python and relevant libraries.
- Test and iterate on your application to improve its functionality.
- Present your application and its findings to demonstrate your understanding and skills.
Career center
Learners who complete Merge, Sort and Filter Data in Python Pandas will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst collects, interprets, and presents data using statistical methods. The analyst then uses this data to make recommendations to clients who want to know how to improve their business. Our data manipulation course teaches students how to acquire and interpret data, which is a key element of market research, financial analysis, marketing, and more. Because our course teaches the specific skills that a Data Analyst uses, it can help those interested in this career field quickly get started toward an entry-level position.
Data Scientist
Data Scientist
A Data Scientist uses data analysis, statistics, and machine learning to solve business problems. The Data Scientist uses programming to conduct testing and develop prototypes. Data Scientists are in high demand in a wide range of industries, including technology, healthcare, finance, and retail. Our course can help those who wish to pursue this career because it teaches how data manipulation can be used to solve business problems. This is a key part of what a Data Scientist does.
Market Researcher
Market Researcher
A Market Researcher collects, analyzes, and interprets data about a specific market. Market Researchers may conduct surveys, focus groups, and interviews, and they also gather, analyze, and report on data from a company's website, social media feeds, and other sources. The Market Researcher presents actionable insights to help guide business decisions, and our course can help those who wish to enter this field develop the data manipulation skills necessary to succeed.
Business Analyst
Business Analyst
A Business Analyst evaluates and solves business problems by leveraging data-driven insights. They collaborate with stakeholders to define the scope of a business problem, gather and analyze data, and develop and implement solutions. A course in data manipulation, such as ours, allows Business Analysts to develop the necessary skills to solve business problems using data-driven insights.
Financial Analyst
Financial Analyst
A Financial Analyst researches investments and makes recommendations to clients on how to manage their money with a focus on analyzing financial data to identify investment opportunities. Our course on data manipulation can provide the foundational skills a Financial Analyst needs to conduct the necessary data analysis to prepare investment recommendations.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses advanced analytical techniques to solve complex business problems. They build mathematical models to represent real-world systems and use these models to analyze and improve the efficiency of operations. Our data manipulation course can help Operations Research Analysts develop the skills they need to build mathematical models to represent real-world systems.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. They use their knowledge of statistics to make informed decisions about a variety of topics, such as public health, marketing, and finance. Our data manipulation course can help aspiring Statisticians develop the skills they need to collect, analyze, interpret, and present data, which are fundamental tasks in this field.
Database Administrator
Database Administrator
A Database Administrator designs, implements, and maintains databases. They ensure that databases are reliable, secure, and efficient. A data manipulation course like ours can help aspiring Database Administrators develop the skills to manage data in a database system.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. Our data manipulation course can help aspiring Software Engineers learn how to manage data in software applications.
Computer Scientist
Computer Scientist
A Computer Scientist conducts research on the theory of computation and the design of computer systems. They use their knowledge of computer science to develop new technologies and solve complex problems. A data manipulation course like ours can help aspiring Computer Scientists develop the skills to manage data in computer systems.
Information Systems Manager
Information Systems Manager
An Information Systems Manager plans, implements, and maintains information systems. They work with users to define the functional requirements of new systems and then design and build those systems. A data manipulation course like ours can help those pursuing this career develop the skills to manage data in information systems.
Data Engineer
Data Engineer
A Data Engineer builds and maintains the infrastructure for data storage and processing. They work with data analysts, data scientists, and other IT professionals to ensure that data is accessible, reliable, and secure. A data manipulation course like ours can help aspiring Data Engineers develop the skills to manage data in a data warehouse or other data storage system.
Web Developer
Web Developer
A Web Developer designs, develops, and maintains websites. They use their knowledge of HTML, CSS, and JavaScript to create websites that are user-friendly and visually appealing. Our data manipulation course can help aspiring Web Developers learn how to manage data on a website.
Computer Programmer
Computer Programmer
A Computer Programmer writes and maintains computer code. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. A data manipulation course like ours can help aspiring Computer Programmers learn how to manage data in software programs.
Systems Analyst
Systems Analyst
A Systems Analyst studies an organization's current business processes and develops plans to improve them. They work with stakeholders to identify areas for improvement and then design and implement new systems to address those needs. A data manipulation course like ours can help aspiring Systems Analysts develop the skills to manage data in business systems.
For more career information including salaries, visit:
OpenCourser.com/course/i1agz1/merge
Reading list
We've selected 12 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
Merge, Sort and Filter Data in Python Pandas.
Provides a comprehensive introduction to data manipulation with Pandas. It covers various aspects of data manipulation, such as data cleaning, data transformation, data merging, and data aggregation. It valuable resource for beginners who want to learn more about data manipulation with Pandas.
Provides a practical introduction to data science. It covers various aspects of data science, such as data collection, data cleaning, data exploration, data visualization, and machine learning. It valuable resource for beginners who want to learn more about data science.
Save
Provides a comprehensive introduction to data mining. It covers various aspects of data mining, such as data preprocessing, data clustering, data classification, and data visualization. It valuable resource for beginners who want to learn more about data mining.
Provides a practical introduction to data analysis with Pandas. It covers various aspects of data analysis with Pandas, such as data cleaning, data transformation, data visualization, and data wrangling. It valuable resource for beginners who want to learn more about data analysis with Pandas.
Provides a comprehensive introduction to machine learning with Python. It covers various aspects of machine learning, such as supervised learning, unsupervised learning, and deep learning. It valuable resource for beginners who want to learn more about machine learning with Python.
Provides a comprehensive overview of data science. It covers various aspects of data science, such as data collection, data cleaning, data analysis, data visualization, and data mining. It valuable resource for beginners who want to learn more about data science.
Provides a comprehensive introduction to data ethics. It covers various aspects of data ethics, such as data privacy, data security, and data bias. It valuable resource for beginners who want to learn more about data ethics.
Provides a comprehensive introduction to machine learning with Python. It covers various aspects of machine learning, such as supervised learning, unsupervised learning, and deep learning. It valuable resource for beginners who want to learn more about machine learning with Python.
Provides a comprehensive introduction to data science for business. It covers various aspects of data science for business, such as data collection, data cleaning, data analysis, data visualization, and data mining. It valuable resource for beginners who want to learn more about data science for business.
Provides a comprehensive introduction to deep learning with Python. It covers various aspects of deep learning, such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for beginners who want to learn more about deep learning with Python.
Provides a comprehensive introduction to natural language processing with Python. It covers various aspects of natural language processing, such as text classification, text clustering, and text generation. It valuable resource for beginners who want to learn more about natural language processing with Python.
Provides a comprehensive introduction to computer vision with Python. It covers various aspects of computer vision, such as image classification, object detection, and image segmentation. It valuable resource for beginners who want to learn more about computer vision with Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i1agz1/merge
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
David Dalsveen
Language
English
Good to know
Examines data visualization techniques, which are essential for finding patterns in datasets
Covers concepts such as sorting, merging, and filtering, which are fundamental to data manipulation
Teaches Python Pandas library, which is widely used for data manipulation and analysis
Includes hands-on exercises for practical application of concepts
Suitable for beginners with little to no prior knowledge in data manipulation
Led by David Dalsveen, an experienced instructor in data science and visualization
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