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Numpy, Pandas, Matplotlib
Mat Leonard,
Andrew Paster,
Jennifer Staab,
Luis Serrano,
Juan Delgado,
Juno Lee,
Mike Yi,
Grant Sanderson,
and
Ortal Arel
Mat Leonard,
Andrew Paster,
Jennifer Staab,
Luis Serrano,
Juan Delgado,
Juno Lee,
Mike Yi,
Grant Sanderson,
and
Ortal Arel
Let's focus on library packages for Python, such as : Numpy (which adds support for large data),
Pandas (which is used for data manipulation and analysis)
And Matplotlib (which is used for data visualization).
Register for this course and see more details by visiting:
OpenCourser.com/course/6zyzox/numpy
What's inside
Syllabus
Anaconda is a package and environment manager built specifically for data. Learn how to use Anaconda to improve your data analysis workflow.
Jupyter Notebooks are a great tool for getting started with writing python code. Though in production you often will write code in scripts, notebooks are wonderful for sharing insights and data viz!
Read more
Syllabus
Anaconda is a package and environment manager built specifically for data. Learn how to use Anaconda to improve your data analysis workflow.
Jupyter Notebooks are a great tool for getting started with writing python code. Though in production you often will write code in scripts, notebooks are wonderful for sharing insights and data viz!
Read more
Learn the basics of NumPy and how to use it to create and manipulate arrays.
Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.
Learn how to use matplotlib and seaborn to visualize your data. In this lesson, you will learn how to create visualizations to depict the distributions of single variables.
In this lesson, you will use matplotlib and seaborn to create visualizations to depict the relationships between two variables.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores the use of Python for data science tasks including data manipulation, analysis, and visualization
Taught by experienced instructors in the field of data science
Incorporates hands-on labs and uses Jupyter Notebooks, which is a widely used tool in data science
Covers the fundamentals of NumPy, Pandas, and Matplotlib, which are essential libraries for data analysis and visualization
Provides a strong foundation for beginners who want to start with data science
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Activities
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Numpy, Pandas, Matplotlib.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Numpy, Pandas, Matplotlib will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis tools and techniques, such as those in this course, to extract insights from data, build predictive models, and develop solutions to business problems. This course can help you build a foundation in the skills and knowledge needed to succeed as a Data Scientist.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to examine and interpret data, identify trends and patterns, and make recommendations based on their findings. This course can help you build a foundation in the skills and knowledge needed to succeed as a Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their knowledge of data analysis tools and techniques, such as those in this course, to build and deploy machine learning models. This course can help you build a foundation in the skills and knowledge needed to succeed as a Machine Learning Engineer.
Statistician
Statistician
Statisticians use their knowledge of data analysis tools and techniques, such as those in this course, to collect, analyze, interpret, and present data. This course can help you build a foundation in the skills and knowledge needed to succeed as a Statistician.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to develop and implement mathematical models to analyze financial data and make investment decisions. This course can help you build a foundation in the skills and knowledge needed to succeed as a Quantitative Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to analyze financial data and make investment recommendations. This course can help you build a foundation in the skills and knowledge needed to succeed as a Financial Analyst.
Actuary
Actuary
Actuaries use their knowledge of data analysis tools and techniques, such as those in this course, to assess and manage financial risk. This course can help you build a foundation in the skills and knowledge needed to succeed as an Actuary.
Market Researcher
Market Researcher
Market Researchers use their knowledge of data analysis tools and techniques, such as those in this course, to collect and analyze data about consumer behavior and trends. This course can help you build a foundation in the skills and knowledge needed to succeed as a Market Researcher.
Business Analyst
Business Analyst
Business Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to analyze business data and make recommendations to improve business performance. This course can help you build a foundation in the skills and knowledge needed to succeed as a Business Analyst.
Computer Programmer
Computer Programmer
Computer Programmers use their knowledge of data analysis tools and techniques, such as those in this course, to develop and maintain computer programs. This course can help you build a foundation in the skills and knowledge needed to succeed as a Computer Programmer.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to analyze and solve business problems. This course can help you build a foundation in the skills and knowledge needed to succeed as an Operations Research Analyst.
Software Engineer
Software Engineer
Software Engineers use their knowledge of data analysis tools and techniques, such as those in this course, to develop and maintain software applications. This course can help you build a foundation in the skills and knowledge needed to succeed as a Software Engineer.
Database Administrator
Database Administrator
Database Administrators use their knowledge of data analysis tools and techniques, such as those in this course, to manage and maintain databases. This course can help you build a foundation in the skills and knowledge needed to succeed as a Database Administrator.
Data Engineer
Data Engineer
Data Engineers use their knowledge of data analysis tools and techniques, such as those in this course, to design, build, and maintain data infrastructure. This course can help you build a foundation in the skills and knowledge needed to succeed as a Data Engineer.
Systems Analyst
Systems Analyst
Systems Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to analyze and design business systems. This course can help you build a foundation in the skills and knowledge needed to succeed as a Systems Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/6zyzox/numpy
Reading list
We've selected 13 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
Numpy, Pandas, Matplotlib.
Provides a comprehensive overview of Python's capabilities for data analysis, covering essential topics such as data structures, data manipulation, and statistical analysis. It valuable resource for both beginners and experienced Python users looking to enhance their data analysis skills.
Provides a comprehensive overview of Pandas, a powerful Python library for data manipulation and analysis. It covers essential concepts such as data structures, indexing, filtering, and merging, making it a valuable reference for both beginners and experienced users.
This comprehensive handbook covers a wide range of topics in Python data science, including NumPy, Pandas, and Matplotlib. It is an excellent reference for experienced users looking to expand their knowledge and explore advanced techniques.
This comprehensive handbook covers a wide range of topics in data science, including data analysis, machine learning, and big data. It is an excellent reference for experienced professionals looking to expand their knowledge and explore advanced techniques.
Provides a practical introduction to machine learning using popular Python libraries such as scikit-learn, Keras, and TensorFlow. While it goes beyond the scope of the course, it offers a valuable resource for those interested in exploring machine learning in more depth.
Provides a unique approach to data science, focusing on building everything from scratch using Python. It offers a deeper understanding of the underlying concepts and is particularly valuable for those interested in exploring the theoretical foundations of data science.
Introduces the fundamental concepts of statistical learning, providing a solid foundation for understanding data analysis and machine learning algorithms. While it is not directly focused on Python, it offers a theoretical background that can be applied to practical applications using Python libraries.
Covers the practical aspects of building machine learning systems using Python. While it goes beyond the scope of the course, it offers a valuable resource for those interested in exploring machine learning in a production environment.
Provides a fast-paced introduction to Python, covering essential concepts and practical applications. It is an excellent resource for those who want to quickly get up to speed with Python.
Covers Python's scikit-learn library, a powerful tool for machine learning and data analysis. While it goes beyond the scope of the course, it provides valuable insights into applying machine learning techniques using Python.
Provides a practical introduction to Python, covering essential programming concepts and practical applications. While it does not directly focus on data science, it offers a solid foundation in Python for those new to the language.
Provides a unique approach to data analysis, focusing on collective intelligence and swarm behavior. While it does not directly cover Python libraries, it offers valuable insights into the underlying principles of data analysis and problem-solving.
Save
Provides a gentle introduction to Python, focusing on the core principles of programming. It is an excellent starting point for those with little to no programming experience.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6zyzox/numpy
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Time
450 hours
Level
Introductory
Via
Udacity
Instructors
Mat Leonard
Andrew Paster
Jennifer Staab
Luis Serrano
Juan Delgado
Juno Lee
Mike Yi
Grant Sanderson
Ortal Arel
Language
English
Good to know
Explores the use of Python for data science tasks including data manipulation, analysis, and visualization
Taught by experienced instructors in the field of data science
Incorporates hands-on labs and uses Jupyter Notebooks, which is a widely used tool in data science
Covers the fundamentals of NumPy, Pandas, and Matplotlib, which are essential libraries for data analysis and visualization
Provides a strong foundation for beginners who want to start with data science
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