Associate Professor of Information
Introduction to Data Science in Python
What You'll Learn
- Understand techniques such as lambdas and manipulating csv files
- Describe common Python functionality and features used for data science
- Query DataFrame structures for cleaning and processing
- Explain distributions, sampling, and t-tests
4 Modules
32 Hours
8 hrs per module (approx.)
Rating
About Introduction to Data Science in Python
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
Skills You'll Gain
- Data Cleansing
- Data Mining
- Matplotlib (Python Package)
- 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 Introduction to Data Science in Python, a practical course that introduces data manipulation and analysis using Python. Learners work with NumPy and pandas to clean, analyze, and transform real-world datasets while developing skills with Series, DataFrames, and inferential statistics. This course, the first in the Applied Data Science with Python Specialization, builds a strong foundation for applied 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: Fundamentals of Data Manipulation with Python
- Video: Introduction to Specialization
- Video: Introduction to the Course
- Reading: Syllabus
- Reading: Pre-Course Survey
- Video: The Coursera Jupyter Notebook System
- Ungraded Lab: Your Personal Jupyter Notebook Workspace
- Video: Python Functions
- Video: Python Types and Sequences
- Video: Python More on Strings
- Video: Python Demonstration: Reading and Writing CSV files
- Video: Python Dates and Times
- Video: Advanced Python Objects, map()
- Video: Advanced Python Lambda and List Comprehensions
- Graded Assignment: Practice Quiz: Python Programming
- Ungraded Lab: Module 1 Jupyter Notebooks
- Video: Creating Array
- Video: Manipulating Array
- Video: From Arrays to Images and Back
- Video: Indexing, Slicing, and Iterating
- Video: Trying NumPy with Datasets
- Graded Assignment: Practice Quiz: Numerical Python Library (NumPy)
- Video: Regex Matching and Anchors
- Video: Patterns and Character Classes
- Video: Quantifiers
- Video: Groups
- Video: Advanced Assertions and Applications
- Reading: Regular Expression Operations Documentation
- Ungraded Plugin: Regex Practice Session
- Reading: Module 1 Textbook Reading Assignment
- Graded: Quiz 1
- Graded: Assignment 1
Module 2: Basic Data Processing with Pandas
- Ungraded Lab: Module 2 Jupyter Notebooks
- Video: Introduction to Pandas
- Video: The Series Data Structure
- Video: Creating Pandas' Series
- Video: Querying a Series
- Video: Vectorized Operations
- Video: Appending Series
- Graded Assignment: Practice Quiz: Pandas and Series Data
- Video: DataFrame Data Structure
- Video: DataFrame Indexing and Loading
- Video: Querying a DataFrame
- Video: Indexing Dataframes
- Video: Missing Values, Part 1
- Video: Missing Values, Part 2
- Video: Example: Manipulating DataFrame
- Graded Assignment: Practice Quiz: DataFrame
- Reading: Module 2 Textbook Reading Assignment
- Graded: Quiz 2
- Graded: Assignment 2
Module 3: More Data Processing with Pandas
- Ungraded Lab: Module 3 Jupyter Notebooks
- Video: Merging Dataframes
- Video: Handling Conflicts between Dataframes
- Video: Concatenating DataFrames
- Video: Pandas Idioms, Part 1
- Video: Pandas Idioms, Part 2
- Video: Group by, Part 1
- Video: Group by, Part 2
- Video: Group by, Part 3
- Video: Scales
- Video: Pivot Table
- Video: Date/Time Functionality
- Video: Working with Dates in a Dataframe
- Graded Assignment: Practice Quiz: More Data Processing with Pandas
- Reading: Module 3 Textbook Reading Assignment
- Graded: Quiz 3
- Graded: Assignment 3
Module 4: Statistical Analysis in Python and Project
- Ungraded Lab: Module 4 Jupyter Notebooks
- Video: Basic Statistical Testing
- Video: Other Forms of Structured Data
- Reading: Science Isn't Broken: p-hacking
- Reading: Goodhart's Law
- Reading: The 5 Graph Algorithms that you should know
- Reading: Post-Course Survey
- Reading: Keep Learning with Michigan Online!
- Reading: Progress Note from Christopher Brooks
- Graded: Assignment 4
- Graded: Final Quiz
Grading Policy
There is one quiz and one programming assignment per module. Each quiz is worth 10% and each programming assignment is worth 15% of your total grade. Learners must earn 80% or higher on all assignments.
Intermediate Level
Some related experience required
What Learners Are Saying
These courses really helped me enhance my skills and increase my knowledge and helped me a lot to learn more about data science with hands-on practical experience, which might help me in my professional career in the near future.
Anshuman Sahoo Learner in India
