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David Dalsveen
By the end of this project you will use the Python Collections Counter, the CSV package's DictReader, and the Collections UserList to read student test data and find the most common test scores.
Read more
By the end of this project you will use the Python Collections Counter, the CSV package's DictReader, and the Collections UserList to read student test data and find the most common test scores.
Read more
By the end of this project you will use the Python Collections Counter, the CSV package's DictReader, and the Collections UserList to read student test data and find the most common test scores.
The Python Collection classes are convenience classes that make it easier to process data and extend capabilities of existing classes. The CSV package's DictReader is convenient for reading columnar data. The UserList allows the developer to add functionality to the List, for example to check types. The Counter class is useful for counting common occurrences in arrays and other structures.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/3w984a/data
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What's inside
Syllabus
Data Processing using Python Collections
By the end of this project you will use the Python Collections Counter, the CSV package's DictReader, and the Collections UserList to read student test data and find the most common test scores.
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Know what's good ,
what to watch for , and
possible dealbreakers
Teaches skills that are useful for personal growth and development
Explores topics that may add color to other topics and subjects
Takes a creative approach to an otherwise established topic or field
Might be restrictive to learners who are not based in North America; experience may be different for those in other regions
Assumes learners have experience with Python
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Reviews summary
Python collections for data processing
Learners say that "Data Processing using Python Collections" is an engaging course that equips them to clean and analyze data, and solve real-world problems.
Activities
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before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Processing using Python Collections with these
activities:
Review Python Basics
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Reinforce basic Python coding concepts, prepare for upcoming course content.
Browse courses on
Python Basics
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Review Python syntax and data types
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Complete 10 Python coding exercises
Show all one activities
Review Python Basics
Show steps
Reinforce basic Python coding concepts, prepare for upcoming course content.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review Python syntax and data types
- Complete 10 Python coding exercises
Show all one activities
Career center
Learners who complete Data Processing using Python Collections will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists leverage large databases to draw conclusions and insights for businesses. They use statistical analysis, programming languages like Python, and specialized tools such as Hadoop to gather and analyze vast amounts of data. Data Scientists may use the Counter class in the Python Collections to count common occurrences in an array of test data. This allows data scientists to quickly identify the most common test scores among a group of students, providing valuable information for educational research and improvement.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to provide insights to companies. They use their skills in statistical analysis, programming, and data visualization to help businesses make informed decisions. Data Analysts may use the Python Collections Counter class to count common occurrences in an array of data. This can be useful for identifying trends and patterns in customer behavior, sales data, or other types of data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They use programming languages like Python and specialized libraries such as TensorFlow to create models that can learn from data and make predictions. Machine Learning Engineers may use the Python Collections Counter class to count common occurrences in an array of training data. This can help them identify which features are most important for the model to learn from.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use programming languages like Python and specialized tools to create software that meets the needs of users. Software Engineers may use the Python Collections Counter class to count common occurrences in an array of data. This can be useful for debugging software, identifying performance bottlenecks, or optimizing code.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that stores and processes data. They use programming languages like Python and specialized tools such as Hadoop to create data pipelines and data warehouses. Data Engineers may use the Python Collections Counter class to count common occurrences in an array of data. This can be useful for identifying data quality issues, optimizing data storage, or improving data processing performance.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding the business needs of an organization and translating those needs into technical requirements. They use their skills in data analysis, process mapping, and communication to help businesses improve their operations. Business Analysts may use the Python Collections Counter class to count common occurrences in an array of data. This can be useful for identifying trends and patterns in business data, such as customer behavior, sales data, or financial data.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making recommendations to investors. They use their skills in financial modeling, data analysis, and presentation to help investors make informed investment decisions. Financial Analysts may use the Python Collections Counter class to count common occurrences in an array of financial data. This can be useful for identifying trends and patterns in financial markets, such as stock prices, interest rates, or economic indicators.
Market Researcher
Market Researcher
Market Researchers are responsible for conducting research to understand the needs of customers and markets. They use their skills in data collection, analysis, and presentation to help businesses develop and market their products and services. Market Researchers may use the Python Collections Counter class to count common occurrences in an array of market research data. This can be useful for identifying trends and patterns in customer behavior, such as purchasing habits, brand preferences, or media consumption.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and analytical techniques to solve business problems. They use their skills in optimization, simulation, and data analysis to help businesses improve their operations. Operations Research Analysts may use the Python Collections Counter class to count common occurrences in an array of operational data. This can be useful for identifying bottlenecks, optimizing processes, or improving resource allocation.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical models to analyze financial data and make investment decisions. They use their skills in probability, statistics, and data analysis to help investors make informed investment decisions. Quantitative Analysts may use the Python Collections Counter class to count common occurrences in an array of financial data. This can be useful for identifying trends and patterns in financial markets, such as stock prices, interest rates, or economic indicators.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks to a business. They use their skills in data analysis, modeling, and communication to help businesses manage their risks. Risk Analysts may use the Python Collections Counter class to count common occurrences in an array of risk data. This can be useful for identifying trends and patterns in risks, such as operational risks, financial risks, or compliance risks.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use their skills in probability, statistics, and data visualization to help businesses make informed decisions. Statisticians may use the Python Collections Counter class to count common occurrences in an array of data. This can be useful for identifying trends and patterns in data, such as population trends, economic indicators, or scientific data.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists are responsible for creating visual representations of data. They use their skills in data visualization, design, and communication to help businesses communicate their data insights to a wider audience. Data Visualization Specialists may use the Python Collections Counter class to count common occurrences in an array of data. This can be useful for creating visualizations that highlight the most important trends and patterns in data.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts are responsible for developing and implementing data governance policies and procedures. They use their skills in data management, compliance, and risk management to help businesses protect their data assets. Data Governance Analysts may use the Python Collections Counter class to count common occurrences in an array of data governance data. This can be useful for identifying trends and patterns in data governance, such as data quality issues, data security breaches, or data privacy violations.
Information Security Analyst
Information Security Analyst
Information Security Analysts are responsible for protecting an organization's information assets. They use their skills in cybersecurity, risk management, and data analysis to help businesses protect their data from unauthorized access, use, disclosure, disruption, modification, or destruction. Information Security Analysts may use the Python Collections Counter class to count common occurrences in an array of information security data. This can be useful for identifying trends and patterns in information security, such as cyberattacks, data breaches, or security vulnerabilities.
For more career information including salaries, visit:
OpenCourser.com/course/3w984a/data
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
Data Processing using Python Collections.
Save
Provides a comprehensive overview of data analytics using Python, covering data collection, cleaning, processing, and visualization. It also includes a detailed introduction to the Python programming language and its various libraries for data analysis.
Practical guide to data analysis using Python and the Pandas library. It covers the basics of data manipulation, cleaning, and visualization, as well as more advanced topics such as machine learning and statistical modeling.
Provides a comprehensive introduction to machine learning using Python, covering the basics of supervised and unsupervised learning, as well as more advanced topics such as deep learning and natural language processing.
Practical guide to deep learning using Python and the Keras library. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive introduction to natural language processing using Python, covering the basics of natural language processing, as well as more advanced topics such as machine translation and speech recognition.
Provides a practical introduction to data science, covering the basics of data collection, cleaning, processing, and visualization. It also includes a detailed introduction to the Python programming language and its various libraries for data analysis.
Practical guide to data science using Python, covering the basics of data collection, cleaning, processing, and visualization. It also includes a detailed introduction to the Python programming language and its various libraries for data analysis.
Provides a practical introduction to statistics using Python, covering the basics of probability, inference, and regression. It also includes a detailed introduction to the Python programming language and its various libraries for data analysis.
Provides a comprehensive introduction to statistical learning using Python, covering the basics of supervised and unsupervised learning, as well as more advanced topics such as deep learning and natural language processing.
Save
Provides a comprehensive introduction to deep learning, covering the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive introduction to reinforcement learning, covering the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning and multi-agent reinforcement learning.
Save
Provides a comprehensive introduction to computer vision, covering the basics of computer vision, as well as more advanced topics such as image processing, object recognition, and scene understanding.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3w984a/data
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
David Dalsveen
Language
English
Good to know
Teaches skills that are useful for personal growth and development
Explores topics that may add color to other topics and subjects
Takes a creative approach to an otherwise established topic or field
Might be restrictive to learners who are not based in North America; experience may be different for those in other regions
Assumes learners have experience with Python
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