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Di Wu
How many times have you decided to learn a programming language but got stuck somewhere along the way, grew frustrated, and gave up? This specialization is designed for learners who have little or no programming experience but want to use Python as a tool to play with data.
Read more
How many times have you decided to learn a programming language but got stuck somewhere along the way, grew frustrated, and gave up? This specialization is designed for learners who have little or no programming experience but want to use Python as a tool to play with data.
Read more
How many times have you decided to learn a programming language but got stuck somewhere along the way, grew frustrated, and gave up? This specialization is designed for learners who have little or no programming experience but want to use Python as a tool to play with data.
Now that you have mastered the fundamentals of Python and Python functions, you will turn your attention to Python packages specifically used for Data Science, such as Pandas, Numpy, Matplotlib, and Seaborn.
Are you ready? Let's go!
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Register for this course and see more details by visiting:
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What's inside
Syllabus
Hello, packages!
Now you have learned the basics of Python to be able to play the magic! In this module, you are going to learn Python packages and experience their convenience and power. You are going to use the packages for something fun. Are you ready? Let's go!
Read more
Syllabus
Hello, packages!
Now you have learned the basics of Python to be able to play the magic! In this module, you are going to learn Python packages and experience their convenience and power. You are going to use the packages for something fun. Are you ready? Let's go!
Read more
Data Manipulation: Numpy and Pandas
In Data Science, we play with data. Python has many useful packages for data creation, integration, and manipulation. In this module, you are going to learn NumPy and Pandas, the most widely used two packages for data science. Are you ready? Let's go!
Data Visualization: Matplotlib
An outstanding data scientist is good at not only data processing and data analyzing but also data visualization and communication. In this module, you are going to learn Matplotlib, one of the most widely used Python package to transform your data in a much more interesting taste. Are you ready? Let's go!
Data Visualization: Seaborn
Data visualization can be done by matplotlib, and it is not enough. Seaborn is built upon matplotlib, and it provides even more power and convenience to the project. Are you ready? Let's go!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches data science using Python, which is highly relevant in industry
Builds a strong foundation for beginners in data science using Python
Teaches essential Python packages such as Pandas, Numpy, Matplotlib, and Seaborn for data science
Course is part of a series, indicating comprehensiveness and detail
Assumes familiarity with Python basics
Save this course
Save Python Packages for Data Science to your list so you can find it easily later:
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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 Python Packages for Data Science with these
activities:
Revisit basic Python concepts
Show steps
Reinforces basic Python concepts, creating a strong foundation for the course.
Browse courses on
Data Science Fundamentals
Show steps
-
Review variables, data types, and operators
-
Practice writing simple Python scripts
Read 'Python for Data Analysis'
Show steps
Expands knowledge of Python libraries and techniques used in data science.
View
Python for Data Analysis
on Amazon
Show steps
-
Review chapters on data manipulation, visualization, and analysis techniques
-
Work through code examples and exercises
Join a Python study group
Show steps
Fosters collaboration, peer support, and diverse perspectives on Python concepts.
Browse courses on
Collaborative Learning
Show steps
-
Find or create a study group with peers enrolled in the course or with similar interests
-
Discuss course material, work on practice problems together, and provide feedback
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Python coding challenges
Show steps
Sharpens coding skills, enhancing problem-solving abilities for data science.
Show steps
-
Work through coding challenges on platforms like HackerRank or LeetCode
-
Analyze and debug solutions to improve coding accuracy
Follow tutorials on NumPy and Pandas
Show steps
Enhances understanding of NumPy and Pandas, essential libraries for data manipulation and analysis.
Browse courses on
Data Science Libraries
Show steps
-
Locate tutorials on beginner-friendly websites or platforms
-
Follow step-by-step instructions to implement NumPy and Pandas functions
Develop a Python project
Show steps
Provides hands-on experience applying Python skills to a real-world data science problem.
Browse courses on
Data Science Project
Show steps
-
Identify a data science problem and gather relevant data
-
Develop a Python script to analyze and visualize the data
-
Present the project findings and insights
Revisit basic Python concepts
Show steps
Reinforces basic Python concepts, creating a strong foundation for the course.
Browse courses on
Data Science Fundamentals
Show steps
Show steps
Show steps
- Review variables, data types, and operators
- Practice writing simple Python scripts
Read 'Python for Data Analysis'
Show steps
Expands knowledge of Python libraries and techniques used in data science.
View
Python for Data Analysis
on Amazon
Show steps
Show steps
Show steps
- Review chapters on data manipulation, visualization, and analysis techniques
- Work through code examples and exercises
Join a Python study group
Show steps
Fosters collaboration, peer support, and diverse perspectives on Python concepts.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Find or create a study group with peers enrolled in the course or with similar interests
- Discuss course material, work on practice problems together, and provide feedback
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Python coding challenges
Show steps
Sharpens coding skills, enhancing problem-solving abilities for data science.
Show steps
Show steps
Show steps
- Work through coding challenges on platforms like HackerRank or LeetCode
- Analyze and debug solutions to improve coding accuracy
Follow tutorials on NumPy and Pandas
Show steps
Enhances understanding of NumPy and Pandas, essential libraries for data manipulation and analysis.
Browse courses on
Data Science Libraries
Show steps
Show steps
Show steps
- Locate tutorials on beginner-friendly websites or platforms
- Follow step-by-step instructions to implement NumPy and Pandas functions
Develop a Python project
Show steps
Provides hands-on experience applying Python skills to a real-world data science problem.
Browse courses on
Data Science Project
Show steps
Show steps
Show steps
- Identify a data science problem and gather relevant data
- Develop a Python script to analyze and visualize the data
- Present the project findings and insights
Career center
Learners who complete Python Packages for Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world data science problems. If you're interested in a career as a Data Scientist, this course is a great place to start.
Data Analyst
Data Analyst
Data Analysts use data to solve problems and make informed decisions. They work with a variety of data sources, including structured and unstructured data. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world data analysis problems. If you're interested in a career as a Data Analyst, this course is a great place to start.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to build and deploy machine learning models. If you're interested in a career as a Machine Learning Engineer, this course is a great place to start.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. They work with a variety of data sources, including structured and unstructured data. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to build and deploy data pipelines. If you're interested in a career as a Data Engineer, this course is a great place to start.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make informed decisions. They work with a variety of stakeholders, including clients, executives, and employees. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world business problems. If you're interested in a career as a Business Analyst, this course is a great place to start.
Statistician
Statistician
Statisticians use data to solve problems and make informed decisions. They work with a variety of data sources, including structured and unstructured data. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world statistical problems. If you're interested in a career as a Statistician, this course is a great place to start.
Financial Analyst
Financial Analyst
Financial Analysts use data to make informed decisions about investments and financial planning. They work with a variety of data sources, including financial statements, market data, and economic indicators. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world financial problems. If you're interested in a career as a Financial Analyst, this course is a great place to start.
Actuary
Actuary
Actuaries use data to assess and manage risk. They work with a variety of data sources, including insurance data, financial data, and demographic data. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world actuarial problems. If you're interested in a career as an Actuary, this course is a great place to start.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies, including Python. This course will teach you the basics of Python and how to use it to build and deploy software applications. If you're interested in a career as a Software Engineer, this course is a great place to start.
Web Developer
Web Developer
Web Developers design and develop websites and web applications. They work with a variety of programming languages and technologies, including Python. This course will teach you the basics of Python and how to use it to build and deploy websites and web applications. If you're interested in a career as a Web Developer, this course is a great place to start.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and inform the public. They work with a variety of data sources, including news articles, social media data, and government data. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world data journalism problems. If you're interested in a career as a Data Journalist, this course is a great place to start.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and market trends. They work with a variety of data sources, including surveys, focus groups, and sales data. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world market research problems. If you're interested in a career as a Market Researcher, this course is a great place to start.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency and effectiveness of organizations. They work with a variety of data sources, including production data, inventory data, and customer data. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world operations research problems. If you're interested in a career as an Operations Research Analyst, this course is a great place to start.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess and manage risk. They work with a variety of data sources, including financial data, insurance data, and economic data. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world risk analysis problems. If you're interested in a career as a Risk Analyst, this course is a great place to start.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make informed decisions about investments and financial planning. They work with a variety of data sources, including financial statements, market data, and economic indicators. They use a variety of tools and techniques, including Python packages such as Pandas, NumPy, Matplotlib, and Seaborn. This course will teach you the basics of these packages and how to use them to solve real-world quantitative analysis problems. If you're interested in a career as a Quantitative Analyst, this course is a great place to start.
For more career information including salaries, visit:
OpenCourser.com/course/d1hkbb/python
Reading list
We've selected ten 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
Python Packages for Data Science.
A comprehensive guide to data science with Python, covering essential topics like data manipulation, analysis, visualization, and machine learning. Provides a strong foundation for the course's focus on Python packages for data science.
An extensive guide to machine learning with Python, covering the fundamentals and implementation of various algorithms using popular Python libraries. Complements the course's exploration of data science packages by providing a deeper dive into machine learning techniques.
A practical introduction to data science that emphasizes coding and problem-solving. Provides a strong foundation in data science concepts and techniques, complementing the course's focus on Python packages.
A comprehensive guide to data analysis with Python, covering essential techniques for data cleaning, transformation, and visualization. Provides a solid foundation for the course's exploration of Python packages for data science.
A comprehensive guide to deep learning with Python, covering the fundamentals and implementation of neural networks. Extends the course's exploration of data science packages by introducing advanced techniques for data analysis and modeling.
A detailed guide to the Pandas library for data manipulation and analysis. Provides in-depth coverage of Pandas' capabilities, complementing the course's introduction to the package.
A beginner-friendly guide to data science with Python. Provides a gentle introduction to the concepts and techniques covered in the course, suitable for those with limited programming experience.
A comprehensive guide to machine learning with Python, covering the fundamentals and implementation of various algorithms. Extends the course's exploration of data science packages by providing a deeper dive into machine learning techniques.
A concise guide to data science with Python, covering essential topics and techniques. Provides a solid foundation for the course's focus on Python packages.
A guide to using AWS for data science, covering topics such as data storage, processing, and analysis. Provides insights into cloud-based data science practices, complementing the course's focus on Python packages.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/d1hkbb/python
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Python for Data Science, AI & Development
Effort
4 weeks of study, 4-6 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Di Wu
Language
English
Transcripts
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This course belongs to a
collection
called
Expressway to Data Science: Python Programming. It's comprised of three courses:
- Introduction to Python Fundamentals
- Introduction to Python Functions
- Python Packages for Data Science (viewing)
Good to know
Teaches data science using Python, which is highly relevant in industry
Builds a strong foundation for beginners in data science using Python
Teaches essential Python packages such as Pandas, Numpy, Matplotlib, and Seaborn for data science
Course is part of a series, indicating comprehensiveness and detail
Assumes familiarity with Python basics
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