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Genevieve M. Lipp,
Nick Eubank,
Kyle Bradbury,
and
Andrew D. Hilton
Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators.
Read more
Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators.
Read more
Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators.
Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices. Hands-on practice with NumPy will equip you with essential skills to tackle big data challenges and solve data problems effectively. You'll write Python programs to manipulate and filter data, as well as create useful insights out of large datasets.
By the end of the course, you'll be adept at summarizing datasets, such as calculating averages, minimums, and maximums. Additionally, you'll gain advanced skills in optimizing data analysis with vectorization and randomizing data.
Throughout your learning journey, you'll use many kinds of data structures and analytic techniques for a variety of data science challenges , including mathematical operations, text file analysis, and image processing. Stepwise, guided assignments each week will reinforce your skills, enabling you to solve problems and draw data-driven conclusions independently.
Prepare yourself for a rewarding career in data science by mastering NumPy and honing your programming prowess. Start this transformative learning experience today!
Register for this course and see more details by visiting:
OpenCourser.com/course/0nv16z/data
What's inside
Syllabus
Sets and Dictionaries: Storing and Working with Data
This week, you will learn the basics of object oriented programming as well as how to use sets and dictionaries to store and work with data in Python. You will apply these concepts with Python to perform some mathematical operations and analytical tasks, including solving geometric problems with circles and counting words in a document.
Read more
Syllabus
Sets and Dictionaries: Storing and Working with Data
This week, you will learn the basics of object oriented programming as well as how to use sets and dictionaries to store and work with data in Python. You will apply these concepts with Python to perform some mathematical operations and analytical tasks, including solving geometric problems with circles and counting words in a document.
Read more
NumPy and Vectors
This week, you will learn how to utilize NumPy--one of the most useful Python packages we use in data science--as well as learn additional data structures, arrays, beginning with the simplest type of an array, a vector. With NumPy and your new understanding of vectors, you will develop histograms as well as analyze household income distribution data in the United States, drawing your own data-driven conclusions.
Matrices and Arrays
This week, you will first learn how NumPy handles data in your program using views and copies of your data. You will then learn how to work with more complex arrays called matrices, as well as how you can subset, filter, and modify data in matrices. Finally, you will write your own programs to manipulate data matrices and report your results for a given dataset.
Summarizing Datasets, Performance Optimization, and Data Randomization
You will learn this week how to use NumPy to summarize data from matrices (e.g., calculating averages, minimums, maximums, etc.) as well as how to begin to analyze and manipulate image data. You will also explore two new data science techniques: how to make your analysis of data matrices more computationally efficient (vectorization) and how to randomize data (randomization).
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Tailored towards novices in data science, software engineering, and other tech fields
Provides foundational programming concepts
Develops skills in data manipulation and analysis using NumPy
Taught by experienced instructors in the field
Requires familiarity with sets, dictionaries, and basic computer science concepts
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Save Data Science with NumPy, Sets, and Dictionaries 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 Data Science with NumPy, Sets, and Dictionaries with these
activities:
Review Python object oriented programming
Show steps
Revisit the basics of object oriented programming concepts to give a solid foundation for understanding NumPy and data science.
Show steps
-
Review the principles of object-oriented programming, such as classes, objects, and inheritance.
-
Practice creating and manipulating objects in Python.
-
Apply object-oriented programming concepts to solve simple data science problems.
Practice using NumPy functions and methods
Show steps
Enhance your proficiency in data manipulation with NumPy by working through a series of exercises and challenges.
Browse courses on
Data Manipulation
Show steps
-
Work through exercises that demonstrate the usage of NumPy functions for array creation, manipulation, and statistical operations.
-
Utilize online resources and tutorials to find additional practice problems.
-
Create your own exercises to test your understanding and identify areas for improvement.
Seek mentorship from a NumPy expert
Show steps
Accelerate your learning by seeking guidance and support from an experienced NumPy user.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through your network, online forums, or professional organizations.
-
Reach out to your chosen mentor and express your interest in learning from them.
-
Establish clear expectations and goals for the mentorship.
-
Regularly connect with your mentor for guidance, feedback, and support.
Show all three activities
Review Python object oriented programming
Show steps
Revisit the basics of object oriented programming concepts to give a solid foundation for understanding NumPy and data science.
Show steps
Show steps
Show steps
- Review the principles of object-oriented programming, such as classes, objects, and inheritance.
- Practice creating and manipulating objects in Python.
- Apply object-oriented programming concepts to solve simple data science problems.
Practice using NumPy functions and methods
Show steps
Enhance your proficiency in data manipulation with NumPy by working through a series of exercises and challenges.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Work through exercises that demonstrate the usage of NumPy functions for array creation, manipulation, and statistical operations.
- Utilize online resources and tutorials to find additional practice problems.
- Create your own exercises to test your understanding and identify areas for improvement.
Seek mentorship from a NumPy expert
Show steps
Accelerate your learning by seeking guidance and support from an experienced NumPy user.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through your network, online forums, or professional organizations.
- Reach out to your chosen mentor and express your interest in learning from them.
- Establish clear expectations and goals for the mentorship.
- Regularly connect with your mentor for guidance, feedback, and support.
Show all three activities
Career center
Learners who complete Data Science with NumPy, Sets, and Dictionaries will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst converts raw data into insights to improve strategic decision-making. This course emphasizes data analysis and interpretation with Python, including using sets and dictionaries to organize data. The hands-on practice in this course can help you build skills for a successful career as a Data Analyst.
Data Scientist
Data Scientist
A Data Scientist combines coding, statistics, and data analysis to explore, interpret, and present data. This course introduces basic Python concepts, data structures like sets and dictionaries, and big data challenges. The skills and knowledge taught in this course will help you build a foundation for a career as a Data Scientist.
Software Developer
Software Developer
A Software Developer designs, builds, and maintains software systems. This course provides a foundation in Python programming, data structures, and data manipulation. The skills you gain from this course can help you succeed as a Software Developer.
Quant
Quant
A Quant uses mathematical and statistical models to analyze data and make predictions in the financial industry. This course provides a foundation in Python programming, data structures like vectors and matrices, and data analysis techniques. The skills you gain in this course can help you build a foundation for a career as a Quant.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course covers data organization and manipulation using Python, sets, and dictionaries. The knowledge and skills you learn can help you build a strong foundation for a career as a Database Administrator.
Market Researcher
Market Researcher
A Market Researcher conducts research to understand market trends and consumer behavior. This course provides a foundation in Python programming, data analysis with NumPy, and data presentation. The knowledge and skills you gain can help you build a foundation for a successful career as a Market Researcher.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data to improve business processes and decision-making. This course provides a foundation in Python programming, data analysis techniques, and data visualization. The skills you gain from this course can help you build a foundation for a successful career as a Business Intelligence Analyst.
Data Engineer
Data Engineer
A Data Engineer designs and builds data pipelines and systems to manage and analyze data. This course provides a foundation in Python programming, data structures like vectors and matrices, and data manipulation. The knowledge and skills you gain can help you succeed as a Data Engineer.
Actuary
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty in the insurance industry. This course provides a foundation in Python programming, data analysis techniques, and probability. The skills you gain in this course may be useful for a career as an Actuary.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to solve complex problems in business and industry. This course provides a foundation in Python programming, data analysis techniques, and optimization. The skills you gain in this course may be useful for a career as an Operations Research Analyst.
Quantitative Researcher
Quantitative Researcher
A Quantitative Researcher uses mathematical and statistical models to analyze data and make investment decisions. This course provides a foundation in Python programming, data analysis techniques, and machine learning. The skills you gain in this course may be useful for a career as a Quantitative Researcher.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to draw conclusions. This course provides a foundation in Python programming, data analysis techniques, and statistical modeling. The skills you gain in this course may be useful for a career as a Statistician.
Financial Analyst
Financial Analyst
A Financial Analyst uses financial data to make investment recommendations. This course provides a foundation in Python programming, data analysis techniques, and financial modeling. The skills you gain in this course may be useful for a career as a Financial Analyst.
Risk Analyst
Risk Analyst
A Risk Analyst uses data to assess and manage risk in various industries. This course provides a foundation in Python programming, data analysis techniques, and risk modeling. The skills you gain in this course may be useful for a career as a Risk Analyst.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs and builds machine learning models to solve real-world problems. This course provides a foundation in Python programming, data analysis techniques, and machine learning algorithms. The skills you gain in this course may be useful for a career as a Machine Learning Engineer.
For more career information including salaries, visit:
OpenCourser.com/course/0nv16z/data
Reading list
We've selected eight 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 Science with NumPy, Sets, and Dictionaries.
Save
A comprehensive guide to data science using R and Python. It covers topics like data loading, cleaning, transformation, modeling, and visualization, and it provides numerous examples and exercises.
A practical guide to using Python for data analysis, with a focus on the Pandas library. It covers topics like data loading, cleaning, transformation, and visualization, and it provides numerous examples and exercises.
Provides a comprehensive introduction to Python programming and the fundamental concepts of data science. It covers topics like data structures, algorithms, and machine learning, and it emphasizes hands-on practice and experimentation.
Provides a foundation in computational science and modeling, with a focus on Python programming. It covers topics like data structures, algorithms, numerical methods, and visualization, and it provides hands-on exercises and projects.
A comprehensive guide to machine learning using Python. It covers topics like supervised learning, unsupervised learning, model evaluation, and deployment, and it provides numerous examples and exercises.
A concise and practical guide to Python for data science. It covers topics like data structures, algorithms, machine learning, and data visualization, and it provides numerous examples and exercises.
Save
A concise and practical introduction to NumPy. It covers topics like array operations, linear algebra, and data visualization, and it provides numerous examples and exercises.
An introduction to deep learning using Python. It covers topics like neural networks, convolutional neural networks, recurrent neural networks, and natural language processing, and it provides numerous examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0nv16z/data
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Effort
4 weeks of study, 4-7 hours of work per week
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Genevieve M. Lipp
Nick Eubank
Kyle Bradbury
Andrew D. Hilton
Language
English
Good to know
Tailored towards novices in data science, software engineering, and other tech fields
Provides foundational programming concepts
Develops skills in data manipulation and analysis using NumPy
Taught by experienced instructors in the field
Requires familiarity with sets, dictionaries, and basic computer science concepts
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