Data Science with NumPy, Sets, and Dictionaries
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Data Science with NumPy, Sets, and Dictionaries
This course is part of Programming for Python Data Science: Principles to Practice Specialization
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There are 4 modules in this course
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!
This module, 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.
What's included
10 videos5 readings4 programming assignments
10 videosβ’Total 41 minutes
- Introduction: Representing Dataβ’1 minute
- Object-Oriented Programming Overviewβ’4 minutes
- Classesβ’4 minutes
- Constructorsβ’3 minutes
- Modules and Import Statementsβ’2 minutes
- Sets: Motivationβ’6 minutes
- Sets in Pythonβ’7 minutes
- Dictionaries: Introductionβ’4 minutes
- Combining Dictionaries with Classes and Setsβ’7 minutes
- Word Counts: Motivationβ’3 minutes
5 readingsβ’Total 45 minutes
- Python Import Does Not Reload Modulesβ’10 minutes
- Report a problem with the courseβ’5 minutes
- A Bit More About Big Oβ’10 minutes
- Comprehensionsβ’10 minutes
- Introduction to the Interactive Consoleβ’10 minutes
4 programming assignmentsβ’Total 720 minutes
- Circleβ’180 minutes
- Pointβ’180 minutes
- Closest Pointβ’180 minutes
- Count Wordsβ’180 minutes
This module, 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.
What's included
1 video9 readings2 assignments3 ungraded labs
1 videoβ’Total 22 minutes
- Live Coding: Exploring Vector Dataβ’22 minutes
9 readingsβ’Total 90 minutes
- Why Numpy?β’10 minutes
- Working with Vectorsβ’10 minutes
- Math with Vectorsβ’10 minutes
- Histogramsβ’10 minutes
- Type Promotion in numpyβ’10 minutes
- Vector Recapβ’10 minutes
- Subsetting Vectorsβ’10 minutes
- Modifying Subsets of Vectorsβ’10 minutes
- Vector Subsets Recapβ’10 minutes
2 assignmentsβ’Total 60 minutes
- Module 2 Numpy Wrap-Up Quizβ’30 minutes
- Vector Exercise Self-Checkβ’30 minutes
3 ungraded labsβ’Total 180 minutes
- Vector Exercisesβ’60 minutes
- Live Coding Lab: Exploring Vector Dataβ’60 minutes
- Numpy Lab for Answering Quiz Questionsβ’60 minutes
This module, 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.
What's included
1 video14 readings1 assignment3 ungraded labs
1 videoβ’Total 13 minutes
- Live Coding Demo: Subsetting and Filtering Matrices β’13 minutes
14 readingsβ’Total 140 minutes
- Vectors, Matrices and Arraysβ’10 minutes
- Views and Copies in NumPyβ’10 minutes
- Working With Views and Copiesβ’10 minutes
- Views and Copies Recapβ’10 minutes
- Objects and Variablesβ’10 minutes
- Matricesβ’10 minutes
- Reshaping Matricesβ’10 minutes
- Images as Matricesβ’10 minutes
- Subsetting Matricesβ’10 minutes
- Modifying Subsetsβ’10 minutes
- Matrix Recapsβ’10 minutes
- ND Arraysβ’10 minutes
- Broadcastingβ’10 minutes
- ND Array Reviewβ’10 minutes
1 assignmentβ’Total 60 minutes
- Module 3 Quizβ’60 minutes
3 ungraded labsβ’Total 180 minutes
- Exercise: Views and Copiesβ’60 minutes
- Playing with Imagesβ’60 minutes
- Lab for Answering Module 3 Quiz Questionsβ’60 minutes
In this module, you will learn 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).
What's included
1 video12 readings1 assignment2 ungraded labs
1 videoβ’Total 14 minutes
- Live Coding: Demonstrating Vectorizationβ’14 minutes
12 readingsβ’Total 115 minutes
- Moving Past Matricesβ’5 minutes
- Summarizing Arraysβ’10 minutes
- Color Images as Arraysβ’10 minutes
- Examples of Summarizing Arraysβ’10 minutes
- Exercise - Summarizing Arraysβ’10 minutes
- Speed and Ease of Useβ’10 minutes
- Vectorizationβ’10 minutes
- Exercise - Vectorizationβ’10 minutes
- Random Numbersβ’10 minutes
- Random Numbers Exercisesβ’10 minutes
- Course Wrap Up: Moving Past NumPyβ’10 minutes
- Share your learning experienceβ’10 minutes
1 assignmentβ’Total 60 minutes
- Module 4 Quizβ’60 minutes
2 ungraded labsβ’Total 120 minutes
- Exercise - Remote Sensingβ’60 minutes
- Lab for Answering Module 4 Quiz Questionsβ’60 minutes
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Reviewed on Aug 16, 2025
The course was really very interesting and Was very Useful and The concepts was clear with Implementation
Reviewed on Mar 27, 2026
This sequence of courses has been well done and is very interesting - which is great for me coming from a programming background. Thanks!
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