NumPy and Pandas Basics for Future Data Scientists
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NumPy and Pandas Basics for Future Data Scientists
This course is part of Data-Oriented Python Programming and Debugging Specialization
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What you'll learn
Create and manipulate NumPy arrays, including performing basic arithmetic operations and handling missing data.
Apply advanced NumPy techniques such as broadcasting, masking, and aggregation functions.
Construct and modify pandas DataFrames and Series, use methods to filter and inspect data, and handle missing data.
Utilize pandas for data aggregation, summary statistics, and dataframe merging to analyze a real dataset.
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There are 4 modules in this course
In “NumPy and Pandas Basics for Future Data Scientists,” learn programming techniques using Python's NumPy and pandas libraries to write efficient and bug-free code for numerical computing.
At the start of the course, you’ll be introduced to the NumPy library and will learn to perform basic NumPy array operations. After understanding the basics of the NumPy library, you’ll explore more advanced array manipulations, including aggregating functions, broadcasting, reshaping, sorting, and joining arrays. By the end of this course, you will have the skills to apply multiple data manipulation techniques using advanced methods and apply functions to your code. This is the second course in the four-course series, “Data-Oriented Python Programming and Debugging,” where you’ll work to strengthen your programming capabilities and enhance your problem-solving skills.
What's included
9 videos3 readings1 programming assignment1 discussion prompt2 ungraded labs
9 videos•Total 82 minutes
- Welcome to the Specialization•7 minutes
- Welcome to 'Course 2'•1 minute
- Meet the Numpy Array•12 minutes
- Creating a Numpy Array•12 minutes
- Array Attributes•6 minutes
- Accessing and Slicing Arrays•15 minutes
- Handling Missing Data•8 minutes
- Basic Array Operations•7 minutes
- Debugging Demo•13 minutes
3 readings•Total 35 minutes
- Course Syllabus•10 minutes
- Help Us Learn About You•5 minutes
- NumPy Tutorial •20 minutes
1 programming assignment•Total 120 minutes
- Assessment 1•120 minutes
1 discussion prompt•Total 10 minutes
- Introduce Yourself•10 minutes
2 ungraded labs•Total 60 minutes
- Jupyter Lab Environment•0 minutes
- Jupyter Lab 1•60 minutes
What's included
6 videos1 reading1 programming assignment2 ungraded labs
6 videos•Total 57 minutes
- Aggregating Functions•8 minutes
- Broadcasting•16 minutes
- Reshaping Arrays•10 minutes
- Sorting Arrays•8 minutes
- Joining Arrays•7 minutes
- Debugging Challenge•9 minutes
1 reading•Total 35 minutes
- Getting Started with Pandas•35 minutes
1 programming assignment•Total 120 minutes
- Assessment 2•120 minutes
2 ungraded labs•Total 60 minutes
- Jupyter Lab Environment•0 minutes
- Jupyter Lab 2•60 minutes
What's included
6 videos1 programming assignment2 ungraded labs
6 videos•Total 89 minutes
- Series and DataFrames•22 minutes
- Reading and Inspecting DataFrames•11 minutes
- Manipulating DataFrames: Intro to Map and Apply•13 minutes
- Filtering DataFrames•5 minutes
- Handling Missing Data•19 minutes
- Debugging Challenge•20 minutes
1 programming assignment•Total 120 minutes
- Assessment 3•120 minutes
2 ungraded labs•Total 60 minutes
- Jupyter Lab Environment•0 minutes
- Jupyter Lab 3•60 minutes
What's included
8 videos2 readings1 programming assignment2 ungraded labs
8 videos•Total 94 minutes
- Concatenating DataFrames•11 minutes
- Merging/Joining DataFrames•15 minutes
- Reshaping DataFrames•18 minutes
- Aggregation•14 minutes
- Filtration•6 minutes
- Transformation•6 minutes
- Apply•10 minutes
- Debugging Challenge Video•13 minutes
2 readings•Total 20 minutes
- Post Course Survey•10 minutes
- Attributions•10 minutes
1 programming assignment•Total 120 minutes
- Assessment 4•120 minutes
2 ungraded labs•Total 60 minutes
- Jupyter Lab Environment•0 minutes
- Jupyter Lab 4•60 minutes
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Reviewed on Mar 25, 2025
This course delivers on the title and covers the basics of NumPy and Pandas.
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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