Introduction to Data Science in Python

User Review 4.5

In this course, learners will be introduced to the fundamentals of the python programming environment. They will learn essential python programming techniques and how to manipulate csv files and use the numpy library. The course will also cover data manipulation and cleaning using pandas, with a focus on Series and DataFrame as central data structures for analysis. Students will learn how to effectively use functions like groupby, merge, and pivot tables. By completing this course, students will gain the ability to clean tabular data, manipulate it, and conduct basic inferential statistical analyses. Additionally, it is recommended that participants take this course before enrolling in 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.

After completing this course, students will have a strong foundation in Python programming and data manipulation techniques using libraries such as numpy and pandas. This knowledge will be essential for further courses in Applied Data Science with Python, including topics like plotting and charting, machine learning, text mining, and social network analysis.

This course, which is part of the Applied Data Science with Python Specialization, offers the opportunity to learn new concepts from industry experts and develop job-relevant skills through hands-on projects. By enrolling in this course, you’ll also be enrolled in this Specialization and have the chance to gain a foundational understanding of a subject or tool while earning a shareable career certificate.

Course Syllabus:

Fundamentals of Data Manipulation with Python

Introduction to Specialization  3 minutes
Introduction to the Course  4 minutes
The Coursera Jupyter Notebook System  8 minutes
Python Functions  8 minutes
Python Types and Sequences  8 minutes
Python More on Strings  3 minutes
Python Demonstration: Reading and Writing CSV files  3 minutes
Python Dates and Times  2 minutes
Advanced Python Objects, map()  5 minutes
Advanced Python Lambda and List Comprehensions 2 minutes
Numerical Python Library(NumPy) 32 minutes
Manipulating Text with Regular Expression 27 minutes

Basic Data Processing with Pandas

Introduction to Pandas – 3 minutes  Preview module
The Series Data Structure  10 minutes
Querying a Series  15 minutes
DataFrame Data Structure  12 minutes
DataFrame Indexing and Loading  8 minutes
Querying a DataFrame  9 minutes
Indexing Dataframes  8 minutes
Missing Values  11 minutes
Example: Manipulating DataFrame  8 minutes

More Data Processing with Pandas

Merging Dataframes  15 minutes
Pandas Idioms  15 minutes
Group by  19 minutes
Scales  10 minutes
Pivot Table  9 minutes
Date/Time Functionality  12 minutes

Answering Questions with Messy Data

Basic Statistical Testing  13 minutes
Other Forms of Structured Data  6 minutes
Science Isn’t Broken: p-hacking  45 minutes
Goodhart’s Law (Optional) 30 minutes
The 5 Graph Algorithms that you should know  10 minutes
Post-course Survey  10 minutes
Keep Learning with Michigan Online!  10 minutes
Tags: Data Science, Free Course, Free Courses, University of Michigan

This course includes:

  • Duration: 34 hours (approximately)
  • Student satisfaction: 92%
  • Flexible schedule
  • Shareable certificate
  • Subtitles: English
  • Other Subtitles: German, Russian, +11 More
Free Start for Free
Difficulty Level: Intermediate

Certificate of completion: yes
Earn a Certificate upon completion

Platform: Coursera

Course Instructor(s):
  • Christopher Brooks