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This course covers "Exam block #2: Math, Science, and Engineering Tools" for the certification exam: [PCPP-32-1: Certified Professional in Python Programming 1 Certification](https://pythoninstitute.org/certification/pcpp-certification-professional/pcpp-32-1-exam-syllabus/) Topics, as called out in the exam syllabus, are: - Math: A basic tool for elementary evaluations - NumPy: A fundamental package for scientific computing - SciPy: An ecosystem for mathematics, science, and engineering - Matplotlib: A 2D plotting library producing publication-quality figures - Pandas: A library providing high-performance and data analysis tools - SciKit-image: A collection of algorithms for image processing
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Delves into a range of mathematical and scientific tools crucial for building a strong foundation in Python programming
Taught by experienced professionals from A Cloud Guru, known for their expertise in cloud computing and Python programming
Examines NumPy, SciPy, Matplotlib, Pandas, and SciKit-image, providing a comprehensive understanding of essential libraries for scientific computing and data analysis
Prepares learners for the PCPP-32-1 certification exam, validating their proficiency in Python programming
Requires a foundational understanding of Python programming, making it most suitable for intermediate learners
Assumes access to necessary software and development tools, which may incur additional costs for learners
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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 Using Python's Math, Science, and Engineering Libraries with these
activities:
Read 'Python Crash Course'
Show steps
Reviews important prerequisite knowledge and fills gaps, especially for beginners new to Python.
View
Python Crash Course, 3rd Edition: A Hands-On,...
on Amazon
Show steps
-
Purchase and read 'Python Crash Course' cover-to-cover.
-
Complete practice exercises and projects in the book.
-
Make notes and summarize key concepts and techniques.
Follow NumPy Tutorial
Show steps
Provides a structured introduction to NumPy's core functionality and best practices.
Browse courses on
Numerical Computing
Show steps
-
Identify a comprehensive NumPy tutorial, such as the official NumPy documentation or a reputable online course.
-
Follow the tutorial step-by-step.
-
Experiment and practice using NumPy in your own projects.
Attend a Machine Learning Workshop
Show steps
Provides practical hands-on experience with real-world machine learning techniques and tools.
Browse courses on
Advanced Analytics
Show steps
-
Research and identify reputable machine learning workshops or training programs.
-
Attend the workshop and actively participate in exercises and discussions.
One other activity
Expand to see all activities and additional details
Show all four activities
Participate in a Kaggle Competition
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Applies learned concepts in a competitive environment, fostering innovative problem-solving and collaboration.
Browse courses on
Kaggle Competition
Show steps
-
Identify a Kaggle competition that aligns with your interests and skill level.
-
Gather a team or work independently.
-
Develop a solution and submit it for evaluation.
Read 'Python Crash Course'
Show steps
Reviews important prerequisite knowledge and fills gaps, especially for beginners new to Python.
View
Python Crash Course, 3rd Edition: A Hands-On,...
on Amazon
Show steps
Show steps
Show steps
- Purchase and read 'Python Crash Course' cover-to-cover.
- Complete practice exercises and projects in the book.
- Make notes and summarize key concepts and techniques.
Follow NumPy Tutorial
Show steps
Provides a structured introduction to NumPy's core functionality and best practices.
Browse courses on
Numerical Computing
Show steps
Show steps
Show steps
- Identify a comprehensive NumPy tutorial, such as the official NumPy documentation or a reputable online course.
- Follow the tutorial step-by-step.
- Experiment and practice using NumPy in your own projects.
Attend a Machine Learning Workshop
Show steps
Provides practical hands-on experience with real-world machine learning techniques and tools.
Browse courses on
Advanced Analytics
Show steps
Show steps
Show steps
- Research and identify reputable machine learning workshops or training programs.
- Attend the workshop and actively participate in exercises and discussions.
One other activity
Expand to see all activities and additional details
Show all four activities
Participate in a Kaggle Competition
Show steps
Applies learned concepts in a competitive environment, fostering innovative problem-solving and collaboration.
Browse courses on
Kaggle Competition
Show steps
Show steps
Show steps
- Identify a Kaggle competition that aligns with your interests and skill level.
- Gather a team or work independently.
- Develop a solution and submit it for evaluation.
Career center
Learners who complete Using Python's Math, Science, and Engineering Libraries will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, analyze, interpret, and present data to help businesses make informed decisions. They use their skills in mathematics, statistics, programming, and data visualization to extract meaningful insights from large datasets. This course provides a strong foundation in the Python programming language, which is essential for Data Analysts, as well as in the NumPy, SciPy, Matplotlib, Pandas, and SciKit-image libraries. These libraries are widely used for data analysis, scientific computing, and image processing, all of which are important skills for Data Analysts to have.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use their skills in mathematics, statistics, programming, and machine learning to create models that can learn from data and make predictions. This course provides a strong foundation in the Python programming language, which is essential for Machine Learning Engineers, as well as in the NumPy, SciPy, Matplotlib, Pandas, and SciKit-image libraries. These libraries are widely used for data analysis, scientific computing, and image processing, all of which are important skills for Machine Learning Engineers to have.
Data Scientist
Data Scientist
Data Scientists use their skills in mathematics, statistics, programming, and machine learning to extract meaningful insights from data. They develop and implement data-driven solutions to business problems. This course provides a strong foundation in the Python programming language, which is essential for Data Scientists, as well as in the NumPy, SciPy, Matplotlib, Pandas, and SciKit-image libraries. These libraries are widely used for data analysis, scientific computing, and image processing, all of which are important skills for Data Scientists to have.
Statistician
Statistician
Statisticians use their skills in mathematics, statistics, and programming to collect, analyze, and interpret data. They use this data to make inferences about the world around us. This course may be useful for Statisticians who want to learn more about using Python for data analysis and visualization.
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software systems. They use their skills in mathematics, computer science, and programming to create software that meets the needs of users. This course may be useful for Software Engineers who want to learn more about using Python for scientific computing, data analysis, and image processing.
Financial Analyst
Financial Analyst
Financial Analysts use their skills in mathematics, statistics, and programming to analyze financial data and make investment recommendations. They use their knowledge of the economy, financial markets, and accounting to help clients make informed decisions about their investments. This course may be useful for Financial Analysts who want to learn more about using Python for data analysis and financial modeling.
Data Engineer
Data Engineer
Data Engineers design, develop, and maintain the infrastructure that is used to store and process data. They use their skills in mathematics, computer science, and programming to create and manage data pipelines that can handle large volumes of data. This course may be useful for Data Engineers who want to learn more about using Python for data analysis and scientific computing.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their skills in mathematics, statistics, and programming to develop and implement financial models. They use these models to make investment decisions and to manage risk. This course may be useful for Quantitative Analysts who want to learn more about using Python for scientific computing and data analysis.
Actuary
Actuary
Actuaries use their skills in mathematics, statistics, and programming to assess and manage financial risk. They develop and implement models to help insurance companies and other financial institutions make informed decisions. This course may be useful for Actuaries who want to learn more about using Python for scientific computing and data analysis.
Epidemiologist
Epidemiologist
Epidemiologists use their skills in mathematics, statistics, and programming to study the distribution and determinants of health-related states or events in specified populations. They use this data to develop and implement public health interventions that are designed to prevent and control disease. This course may be useful for Epidemiologists who want to learn more about using Python for data analysis and visualization.
Biostatistician
Biostatistician
Biostatisticians use their skills in mathematics, statistics, and programming to design and analyze studies that are used to evaluate the safety and efficacy of new drugs and treatments. They also use their skills to develop statistical models that can be used to predict the outcomes of clinical trials. This course may be useful for Biostatisticians who want to learn more about using Python for data analysis and statistical modeling.
Market Researcher
Market Researcher
Market Researchers use their skills in mathematics, statistics, and programming to collect and analyze data about consumer behavior. They use this data to help businesses understand their customers and to develop marketing strategies that are more likely to be successful. This course may be useful for Market Researchers who want to learn more about using Python for data analysis and visualization.
Computational Scientist
Computational Scientist
Computational Scientists use their skills in mathematics, computer science, and programming to simulate and model complex phenomena. They use their models to study a wide range of topics, including the behavior of atoms and molecules, the evolution of the universe, and the spread of disease. This course may be useful for Computational Scientists who want to learn more about using Python for scientific computing and visualization.
Business Analyst
Business Analyst
Business Analysts use their skills in mathematics, statistics, and programming to analyze business data and make recommendations for improvement. They work with stakeholders to identify business needs and to develop and implement solutions that meet those needs. This course may be useful for Business Analysts who want to learn more about using Python for data analysis and visualization.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their skills in mathematics, statistics, and programming to solve complex business problems. They develop and implement models to help businesses make better decisions about how to allocate resources, schedule production, and manage inventory. This course may be useful for Operations Research Analysts who want to learn more about using Python for scientific computing and data analysis.
For more career information including salaries, visit:
OpenCourser.com/course/4wyifb/using
Reading list
We've selected seven 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
Using Python's Math, Science, and Engineering Libraries.
Comprehensive guide to using Python for data science. It covers a wide range of topics, from data manipulation and visualization to more advanced topics such as machine learning and deep learning. This book great resource for anyone who wants to learn more about Python for data science.
Practical guide to using Python for machine learning. It covers a wide range of topics, from data preprocessing and model selection to more advanced topics such as deep learning and natural language processing. This book great resource for anyone who wants to learn more about Python for machine learning.
Practical guide to using Python for deep learning. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks. This book great resource for anyone who wants to learn more about Python for deep learning.
Practical guide to the Pandas library. It covers the basics of working with Pandas DataFrames, as well as more advanced topics such as data manipulation and visualization. This book great resource for anyone who wants to learn more about Pandas.
Collection of recipes for using the Pandas library. It covers a wide range of topics, from basic data manipulation to more advanced topics such as data analysis and visualization. This book great resource for anyone who wants to learn how to use Pandas to solve specific problems.
Save
Gentle introduction to the Python programming language. It covers the basics of programming, as well as more advanced topics such as object-oriented programming and data structures. This book great resource for anyone who wants to learn more about Python.
Quick reference to the NumPy library. It covers the most commonly used NumPy functions and methods. This book great resource for anyone who wants to quickly look up NumPy functions and methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4wyifb/using
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Time
300 hours
Level
Intermediate
Via
Pluralsight
Instructor
A Cloud Guru
Language
English
Good to know
Delves into a range of mathematical and scientific tools crucial for building a strong foundation in Python programming
Taught by experienced professionals from A Cloud Guru, known for their expertise in cloud computing and Python programming
Examines NumPy, SciPy, Matplotlib, Pandas, and SciKit-image, providing a comprehensive understanding of essential libraries for scientific computing and data analysis
Prepares learners for the PCPP-32-1 certification exam, validating their proficiency in Python programming
Requires a foundational understanding of Python programming, making it most suitable for intermediate learners
Assumes access to necessary software and development tools, which may incur additional costs for learners
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