About this course: 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.
Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. The class is taught in a tutorial format using the pandas library, and only a minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Taught by: Christopher Brooks
| Basic Info | Course 1 of 5 in the Applied Data Science with Python Specialization |
| Level | Intermediate |
| Language | English, Subtitles: Korean, Vietnamese, Chinese (Traditional) |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Reading: Syllabus
- Reading: Help us learn more about you!
- Video: Data Science
- Reading: 50 years of Data Science, David Donoho (optional)
- Video: The Coursera Jupyter Notebook System
- Reading: Notice for Auditing Learners: Assignment Submission
- Notebook: Week 1 Lectures Jupyter Notebook
- 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
- Video: Advanced Python Demonstration: The Numerical Python Library (NumPy)
- Notebook: Week 2 Lectures Jupyter Notebook
- Video: The Series Data Structure
- Video: Querying a Series
- Video: The DataFrame Data Structure
- Video: DataFrame Indexing and Loading
- Video: Querying a DataFrame
- Video: Indexing Dataframes
- Video: Missing Values
- LTI Item: Hacked Data
- Notebook: Assignment 2
- Notebook: Week 3 Lectures Jupyter Notebook
- Video: Pandas Idioms
- Video: Group by
- Video: Scales
- Video: Pivot Tables
- Video: Date Functionality
- Discussion Prompt: Goodhart's Law
- Notebook: Assignment 3
- Notebook: Week 4 Lectures Jupyter Notebook
- Video: Distributions
- Video: More Distributions
- Video: Hypothesis Testing in Python
- LTI Item: End of Theory
- Discussion Prompt: Science Isn't Broken: p-hacking activity
- Notebook: Assignment 4 - Project
- Reading: Post-course Survey
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Well designed course
Excellent basics
Learnt a lot
Very Good learning
Nice intro for Data Science with Python