Lecturer IV and Research Investigator
NumPy and Pandas Basics for Future Data Scientists
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.
4 Modules
20 Hours
5 hrs per module (approx.)
Rating
About NumPy and Pandas Basics for Future Data Scientists
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.
Skills You'll Gain
- Critical Thinking
- Data Analysis
- Data Wrangling
- Debugging
- Pandas (Python Package)
- Python For Data Analysis
- Python (Programming Language)
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to NumPy and Pandas Basics for Future Data Scientists, a hands-on course focused on foundational data manipulation skills in Python. Learners work with arrays, DataFrames, and real datasets to build core competencies for data science workflows.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Introduction to NumPy Arrays and Basic Operations
- Video: Welcome to the Specialization
- Reading: Course Syllabus
- Discussion Prompt: Introduce Yourself
- Reading: Help Us Learn About You
- Video: Welcome to 'Course 2'
- Ungraded Lab: Jupyter Lab Environment
- Video: Meet the Numpy Array
- Video: Creating a Numpy Array
- Video: Array Attributes
- Video: Accessing and Slicing Arrays
- Video: Handling Missing Data
- Video: Basic Array Operations
- Reading: NumPy Tutorial
- Video: Debugging Demo
- Ungraded Lab: Jupyter Lab 1
- Graded: Assessment 1
Module 2: Advanced NumPy Array Manipulations and Operations
- Ungraded Lab: Jupyter Lab Environment
- Video: Aggregating Functions
- Video: Broadcasting
- Video: Reshaping Arrays
- Video: Sorting Arrays
- Video: Joining Arrays
- Reading: Getting Started with Pandas
- Video: Debugging Challenge
- Ungraded Lab: Jupyter Lab 2
- Graded: Assessment 2
Module 3: Mastering Pandas for Data Science
- Ungraded Lab: Jupyter Lab Environment
- Video: Series and DataFrames
- Video: Reading and Inspecting DataFrames
- Video: Manipulating DataFrames: Intro to Map and Apply
- Video: Filtering DataFrames
- Video: Handling Missing Data
- Video: Debugging Challenge
- Ungraded Lab: Jupyter Lab 3
- Graded: Assessment 3
Module 4: Advanced Data Handling and Analysis with Pandas
- Ungraded Lab: Jupyter Lab Environment
- Video: Concatenating DataFrames
- Video: Merging/Joining DataFrames
- Video: Reshaping DataFrames
- Video: Aggregation
- Video: Filtration
- Video: Transformation
- Video: Apply
- Video: Debugging Challenge Video
- Ungraded Lab: Jupyter Lab 4
- Reading: Post Course Survey
- Reading: Attributions
- Graded: Assessment 4
Grading Policy
There are four programming assessments in this course, with each accounting for 25% of your final grade.
Michael D. Cohen Collegiate Professor of Information
Lecturer IV
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Learners should complete "Python 3 Programming" on Coursera or have equivalent experience with Python programming basics.
