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David Dalsveen
Processing data is used in virtually every field these days. It is used for analyzing web traffic to determine personal preferences, gathering scientific data for biological analysis, analyzing weather patterns, business practices, and on. Data can take on many different forms and come from many different sources. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. It has libraries that can be used to parse and quickly analyze the data in whatever form it comes in, whether it be in XML, CSV, or JSON format. Data cleaning is an important aspect of processing data, particularly in the field of data science.
Read more
Processing data is used in virtually every field these days. It is used for analyzing web traffic to determine personal preferences, gathering scientific data for biological analysis, analyzing weather patterns, business practices, and on. Data can take on many different forms and come from many different sources. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. It has libraries that can be used to parse and quickly analyze the data in whatever form it comes in, whether it be in XML, CSV, or JSON format. Data cleaning is an important aspect of processing data, particularly in the field of data science.
Read more
Processing data is used in virtually every field these days. It is used for analyzing web traffic to determine personal preferences, gathering scientific data for biological analysis, analyzing weather patterns, business practices, and on. Data can take on many different forms and come from many different sources. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. It has libraries that can be used to parse and quickly analyze the data in whatever form it comes in, whether it be in XML, CSV, or JSON format. Data cleaning is an important aspect of processing data, particularly in the field of data science.
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What's inside
Syllabus
Processing Data with Python
Processing data is used in virtually every field these days. It is used for analyzing web traffic to determine personal preferences, gathering scientific data for biological analysis, analyzing weather patterns, business practices, and on. Data can take on many different forms and come from many different sources. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. It has libraries that can be used to parse and quickly analyze the data in whatever form it comes in, whether it be in XML, CSV, or JSON format. Data cleaning is an important aspect of processing data, particularly in the field of data science.
In this course, you will create an application that reads data in a couple of different formats using the Pandas library. You will perform statistical analysis on the data. You will also see how to clean specific areas from the data set to produce valid information.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills, knowledge, and tools in Python, data science, and other areas that are useful in roles across industries
Emphasizes data cleaning, a critical aspect of data science
Uses the Pandas library, a popular and powerful tool for data analysis in Python
Requires learners to come in with basic programming knowledge and a basic understanding of statistics
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Reviews summary
Recommended for python basics
Learners say that this course is good for beginners in Python who already know the basics. It introduces Pandas, a useful library for data processing tasks in Python. The course explains concepts well and includes a project to help you learn. However, some learners found the explanations to be brief and the dataset to be basic.
This course provides a good introduction to Pandas, a popular library for data processing in Python.
"after seeing this type of courses,I was very excited that how processing any data by this language"
"its nice course good opportunity to learn a small thing to create a grate objective"
"This project is simply a demonstration of what you can do with your data after importing Panda."
The instructor does a good job of explaining the concepts of Python and Pandas.
"Instructor Explains very well in the programming part"
"Yes, the project/course with respect to its length was helpful"
"Thank you Coursera to give me this type of courses"
This course is a good starting point for learners who are new to Python.
"Worth a try. Good for getting an overview"
"Really good if u know beginning python programmer"
"I was very excited that how processing any data by this language."
Some learners found that the explanations in the course were a bit brief.
"The explanation is very less in every part of this project."
"This project is simply a demonstration of what you can do with your data after importing Panda."
"This is a basic course at best and should not be branded intermediate."
The dataset used in the course is fairly basic and may not be suitable for learners who are looking for more advanced data processing tasks.
"I took this course hoping to gain experience in real-world data cleaning and manipulation."
"The toy dataset and techniques demonstrated were basic and would not go far in a real-world application."
"This is a basic course at best and should not be branded intermediate."
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 Processing Data with Python with these
activities:
Review Python Basics
Show steps
Ensure a strong foundation in Python syntax and data structures before starting the course.
Browse courses on
Python
Show steps
-
Review basic Python concepts such as variables, data types, and control flow.
Read 'Python for Data Analysis' by Wes McKinney
Show steps
Gain a deeper understanding of data analysis concepts and Python techniques through this comprehensive book.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Read the book's chapters on data loading and cleaning with Pandas.
-
Apply the methods and techniques presented in the book to your data analysis projects.
Practice Reading Data in Python Pandas
Show steps
Identify different data formats and practice reading these formats with the Pandas library.
Browse courses on
Python
Show steps
-
Explore various file formats (e.g., CSV, JSON, XML), and their corresponding Pandas methods for reading them.
-
Practice loading data from files into Pandas DataFrames.
-
Manipulate and explore data in DataFrames using basic functions and methods.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Learn Data Cleaning with Pandas
Show steps
Develop understanding of data cleaning techniques and apply them using Pandas in a guided tutorial.
Browse courses on
Data Cleaning
Show steps
-
Watch tutorials on Pandas data cleaning methods for detecting and handling missing and invalid data.
-
Practice using these methods on real-world datasets to clean and prepare data for analysis.
Statistical Analysis with Pandas
Show steps
Explore statistical functions and perform basic statistical analysis using Pandas in practical exercises.
Browse courses on
Statistical Analysis
Show steps
-
Review statistical concepts such as mean, median, standard deviation, and variance.
-
Practice calculating these statistical measures using Pandas functions.
-
Learn how to generate graphical representations of data using Pandas' plotting capabilities.
Data Cleaning and Analysis Project
Show steps
Apply knowledge of data cleaning and analysis by working on a practical project involving real-world data.
Browse courses on
Data Cleaning
Show steps
-
Identify a dataset of interest.
-
Clean and prepare the data using Pandas' data cleaning techniques.
-
Perform statistical analysis and visualization to extract meaningful insights from the data.
-
Create a polished presentation showcasing your findings.
Create a Resource List for Data Analysis with Python
Show steps
Compile a collection of valuable resources for further learning and reference on data analysis with Python.
Browse courses on
Data Analysis
Show steps
-
Gather links to tutorials, articles, and online courses on data analysis with Python.
-
Organize the resources into categories or topics for easy access.
Attend a Python Data Analysis Workshop
Show steps
Enhance your knowledge and skills by attending a workshop led by experts in Python data analysis.
Browse courses on
Data Analysis
Show steps
-
Research and identify relevant workshops in your area or online.
-
Attend the workshop and actively participate in discussions and hands-on exercises.
Review Python Basics
Show steps
Ensure a strong foundation in Python syntax and data structures before starting the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic Python concepts such as variables, data types, and control flow.
Read 'Python for Data Analysis' by Wes McKinney
Show steps
Gain a deeper understanding of data analysis concepts and Python techniques through this comprehensive book.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Read the book's chapters on data loading and cleaning with Pandas.
- Apply the methods and techniques presented in the book to your data analysis projects.
Practice Reading Data in Python Pandas
Show steps
Identify different data formats and practice reading these formats with the Pandas library.
Browse courses on
Python
Show steps
Show steps
Show steps
- Explore various file formats (e.g., CSV, JSON, XML), and their corresponding Pandas methods for reading them.
- Practice loading data from files into Pandas DataFrames.
- Manipulate and explore data in DataFrames using basic functions and methods.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Learn Data Cleaning with Pandas
Show steps
Develop understanding of data cleaning techniques and apply them using Pandas in a guided tutorial.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Watch tutorials on Pandas data cleaning methods for detecting and handling missing and invalid data.
- Practice using these methods on real-world datasets to clean and prepare data for analysis.
Statistical Analysis with Pandas
Show steps
Explore statistical functions and perform basic statistical analysis using Pandas in practical exercises.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Review statistical concepts such as mean, median, standard deviation, and variance.
- Practice calculating these statistical measures using Pandas functions.
- Learn how to generate graphical representations of data using Pandas' plotting capabilities.
Data Cleaning and Analysis Project
Show steps
Apply knowledge of data cleaning and analysis by working on a practical project involving real-world data.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Identify a dataset of interest.
- Clean and prepare the data using Pandas' data cleaning techniques.
- Perform statistical analysis and visualization to extract meaningful insights from the data.
- Create a polished presentation showcasing your findings.
Create a Resource List for Data Analysis with Python
Show steps
Compile a collection of valuable resources for further learning and reference on data analysis with Python.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Gather links to tutorials, articles, and online courses on data analysis with Python.
- Organize the resources into categories or topics for easy access.
Attend a Python Data Analysis Workshop
Show steps
Enhance your knowledge and skills by attending a workshop led by experts in Python data analysis.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Research and identify relevant workshops in your area or online.
- Attend the workshop and actively participate in discussions and hands-on exercises.
Career center
Learners who complete Processing Data with Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use programming languages like Python to examine, clean, and interpret large amounts of data. This course helps build a foundation in data analysis with Python, covering topics such as data cleaning, statistical analysis, and data visualization. These skills are essential for Data Analysts who need to extract meaningful insights from data.
Data Scientist
Data Scientist
Data Scientists use their programming skills to build models and algorithms that can predict future outcomes. This course provides a solid foundation in Python programming, which is a widely-used language in data science. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course provides a foundation in Python programming, which is a widely-used language in machine learning. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Machine Learning Engineers.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines that collect, store, and process data. This course provides a foundation in Python programming, which is a widely-used language in data engineering. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Data Engineers.
Software Engineer
Software Engineer
Software Engineers use programming languages like Python to build and maintain software applications. This course provides a foundation in Python programming, which is essential for Software Engineers. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which can be helpful for Software Engineers who work on data-intensive applications.
Business Analyst
Business Analyst
Business Analysts use data to understand business needs and make recommendations. This course provides a foundation in Python programming, which can be helpful for Business Analysts who need to analyze data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Business Analysts.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course provides a foundation in Python programming, which can be helpful for Financial Analysts who need to analyze financial data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Financial Analysts.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. This course provides a foundation in Python programming, which can be helpful for Market Researchers who need to analyze consumer data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Market Researchers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to optimize business processes. This course provides a foundation in Python programming, which can be helpful for Operations Research Analysts who need to analyze business data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Operations Research Analysts.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course provides a foundation in Python programming, which can be helpful for Quantitative Analysts who need to analyze financial data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Quantitative Analysts.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess and manage risk. This course provides a foundation in Python programming, which can be helpful for Risk Analysts who need to analyze risk data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Risk Analysts.
Statistician
Statistician
Statisticians use data to collect, analyze, and interpret data. This course provides a foundation in Python programming, which can be helpful for Statisticians who need to analyze data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Statisticians.
Data-Driven Marketer
Data-Driven Marketer
Data-Driven Marketers use data to make marketing decisions. This course provides a foundation in Python programming, which can be helpful for Data-Driven Marketers who need to analyze marketing data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Data-Driven Marketers.
Data Journalist
Data Journalist
Data Journalists use data to tell stories. This course provides a foundation in Python programming, which can be helpful for Data Journalists who need to analyze data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Data Journalists.
Product Manager
Product Manager
Product Managers use data to make decisions about product development. This course provides a foundation in Python programming, which can be helpful for Product Managers who need to analyze product data. Additionally, the course covers data cleaning techniques, statistical analysis, and data visualization, which are all essential skills for Product Managers.
For more career information including salaries, visit:
OpenCourser.com/course/largr7/processing
Reading list
We've selected nine 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
Processing Data with Python.
Is used as background reading and understanding of using Python for data analysis. It covers the basics of using Python for data analysis, including data cleaning, exploration, and visualization.
Practical guide to using Pandas for data analysis. It covers a wide range of topics, including data cleaning, exploration, and visualization. It good resource for learners who want to learn more about using Pandas.
Comprehensive guide to data cleaning with Python. It covers a wide range of topics, including data quality assessment, data cleaning techniques, and data validation. It good resource for learners who want to learn more about data cleaning.
Comprehensive guide to data visualization with Python. It covers a wide range of topics, including data visualization techniques, data visualization tools, and data visualization best practices. It good resource for learners who want to learn more about data visualization.
Is an introduction to deep learning with Python. It covers a wide range of topics, including deep learning algorithms, deep learning techniques, and deep learning applications. It good resource for learners who want to learn more about deep learning.
Save
Is an introduction to data science with Python. It covers a wide range of topics, including data science concepts, data science techniques, and data science applications. It good resource for learners who want to learn more about data science.
Provides a good overview of data science concepts and techniques, including data cleaning, exploration, and modeling. It good resource for learners who want to learn more about data science.
Is an introduction to machine learning with Python. It covers a wide range of topics, including machine learning algorithms, machine learning techniques, and machine learning applications. It good resource for learners who want to learn more about machine learning.
Save
Is an introduction to using Python for data analysis. It covers a wide range of topics, including data cleaning, exploration, and visualization. It good resource for learners who want to learn more about using Python for data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/largr7/processing
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Effort
55 minutes
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
David Dalsveen
Language
English
Good to know
Develops skills, knowledge, and tools in Python, data science, and other areas that are useful in roles across industries
Emphasizes data cleaning, a critical aspect of data science
Uses the Pandas library, a popular and powerful tool for data analysis in Python
Requires learners to come in with basic programming knowledge and a basic understanding of statistics
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