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Reading and Writing CSV Files in Python
Chase DeHan
Chase DeHan
This course solves the problem of loading data from CSV files.
Read more
This course solves the problem of loading data from CSV files.
Read more
This course solves the problem of loading data from CSV files.
Loading data from CSV files includes a number of different formats. There are also a large number of different use cases for the data once it is loaded - there is not a one-size-fits-all solution to loading data. In this course, Reading and Writing CSV Files in Python, you’ll gain the ability to load CSV data using a number of different Python tools. First, you’ll explore Python’s built-in CSV module. Next, you’ll discover how to load data from external URLs. Finally, you’ll learn how to use NumPy and Pandas to import CSV files. When you’re finished with this course, you’ll have the skills and knowledge of reading Python CSVs needed to build data intensive applications.
Register for this course and see more details by visiting:
OpenCourser.com/course/pkuxto/reading
What's inside
Syllabus
Course Overview
Opening and Modifying Files
Read CSVs with Python’s CSV Package
Importing with Pandas and NumPy
Read more
Syllabus
Course Overview
Opening and Modifying Files
Read CSVs with Python’s CSV Package
Importing with Pandas and NumPy
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches how to utilize the native CSV package in Python, which is standard for Python data manipulation
Suitable for diverse data use cases due to coverage of multiple data manipulation tools
Incorporates industry-standard libraries such as NumPy and Pandas for data analysis
Lacks coverage of advanced topics such as data cleaning techniques or machine learning algorithms
Does not cover data visualization or the integration of CSV data with other software or applications
Prerequisites may be necessary, as the course assumes familiarity with Python's syntax and data structures
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Career center
Learners who complete Reading and Writing CSV Files in Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst helps businesses make better decisions by extracting insights from data. Data Analysts typically use a variety of software tools to clean, analyze, and visualize data in order to discover trends and patterns.
Data Engineer
Data Engineer
Data Engineers make sure that data is ready for analysis by deploying and maintaining large scale data infrastructure. Data Engineers work with Data Analysts and Data Scientists in order to make sure that enterprise data is of sufficient quality.
Data Scientist
Data Scientist
Data Scientists use statistical techniques to analyze data and build predictive models that help businesses make better decisions. Data Scientists are often responsible for extracting insights from large volumes of data in order to solve business problems.
Software Engineer
Software Engineer
Software Engineers design, build, maintain, and improve software systems. The skills learned by reading and writing CSV files in Python are foundational for any Software Engineer who works with data. Because CSV files are ubiquitous, this course may be useful in helping Software Engineers write code that works well in the real world.
Product Manager
Product Manager
Product Managers define, develop, and manage products. They work with engineers, designers, and other stakeholders to bring a product to market. Being able to read and write CSV files in Python may be useful for Product Managers who need to work with data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of fields, including finance, marketing, and healthcare.
Financial Analyst
Financial Analyst
Financial Analysts help businesses make better financial decisions. They use a variety of financial models to analyze data and make recommendations.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical models to help businesses make better decisions. They work on a variety of problems, such as scheduling, inventory management, and supply chain management.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. They work with stakeholders to define business needs and develop solutions.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. They work with data analysts and data scientists to communicate insights to stakeholders.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make investment decisions. They work for hedge funds, investment banks, and other financial institutions.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and injury. They work with data to track the spread of disease and develop strategies to prevent it.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze studies in the biomedical sciences.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data about markets in order to help businesses make better decisions. They use a variety of research methods to collect data, including surveys, interviews, and focus groups.
Data Librarian
Data Librarian
Data Librarians manage and organize data for organizations. They work with data scientists and other stakeholders to ensure that data is accessible and usable.
For more career information including salaries, visit:
OpenCourser.com/course/pkuxto/reading
Reading list
We've selected 14 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
Reading and Writing CSV Files in Python.
Save
Provides a practical guide to data analysis with Pandas, a powerful Python library for data manipulation and analysis. It great resource for anyone who wants to learn how to use Pandas to solve real-world data analysis problems.
Provides an in-depth look at using NumPy and Pandas for data manipulation, including topics such as data structures, operations, and visualization.
Provides a general overview of data analysis using Python, including topics such as data exploration, cleaning, and visualization.
Save
Provides a comprehensive overview of data mining in Python, covering topics such as data preprocessing, feature selection, and model evaluation. It valuable resource for anyone who wants to learn more about data mining in Python.
Provides a comprehensive overview of machine learning in Python, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn more about machine learning in Python.
Provides a comprehensive overview of deep learning in Python, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn more about deep learning in Python.
Provides a comprehensive introduction to data science, including topics such as data collection, cleaning, analysis, and visualization.
Provides a comprehensive overview of computer vision in Python, covering topics such as image processing, feature extraction, and model evaluation. It valuable resource for anyone who wants to learn more about computer vision in Python.
Provides a comprehensive introduction to machine learning using Python, including topics such as data preparation, model selection, and evaluation.
Provides a comprehensive introduction to deep learning using Python, including topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive introduction to natural language processing using Python, including topics such as tokenization, stemming, lemmatization, and parsing.
Provides a comprehensive overview of financial data analysis in Python, covering topics such as data cleaning, exploration, visualization, and modeling. It valuable resource for anyone who wants to learn more about financial data analysis in Python.
Provides a comprehensive introduction to data visualization using Python, including topics such as data exploration, cleaning, and visualization.
Save
Provides a comprehensive introduction to web development using Python, including topics such as web frameworks, databases, and deployment.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pkuxto/reading
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Time
46 hours
Level
Intermediate
Via
Pluralsight
Instructor
Chase DeHan
Language
English
Good to know
Teaches how to utilize the native CSV package in Python, which is standard for Python data manipulation
Suitable for diverse data use cases due to coverage of multiple data manipulation tools
Incorporates industry-standard libraries such as NumPy and Pandas for data analysis
Lacks coverage of advanced topics such as data cleaning techniques or machine learning algorithms
Does not cover data visualization or the integration of CSV data with other software or applications
Prerequisites may be necessary, as the course assumes familiarity with Python's syntax and data structures
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