Introduction to Data Science in Python
5.0 /5- Offered byCoursera
- Public/Government Institute
Introduction to Data Science in Python at Coursera Overview
Duration | 31 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Data Science in Python at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 5 in the Applied Data Science with Python Specialization
- Intermediate Level
- Approx. 31 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, English, Spanish
Introduction to Data Science in Python at Coursera Course details
- 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.
Introduction to Data Science in Python at Coursera Curriculum
Fundamentals of Data Manipulation with Python
Introduction to Specialization
Introduction to the Course
The Coursera Jupyter Notebook System
Python Functions
Python Types and Sequences
Python More on Strings
Python Demonstration: Reading and Writing CSV files
Python Dates and Times
Advanced Python Objects, map()
Advanced Python Lambda and List Comprehensions
Numerical Python Library (NumPy)
Manipulating Text with Regular Expression
Syllabus
Notice for Auditing Learners: Assignment Submission
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Week 1 Textbook Reading Assignment (Optional)
50 years of Data Science, David Donoho (Optional)
Regular Expression Operations documentation
Quiz 1
Basic Data Processing with Pandas
Introduction to Pandas
The Series Data Structure
Querying a Series
DataFrame Data Structure
DataFrame Indexing and Loading
Querying a DataFrame
Indexing Dataframes
Missing Values
Example: Manipulating DataFrame
Week 2 Reading Assignments (Optional)
Quiz 2
More Data Processing with Pandas
Merging Dataframes
Pandas Idioms
Group by
Scales
Pivot Table
Date/Time Functionality
Week 3 Reading Assignments (Optional)
Quiz 3
Answering Questions with Messy Data
Basic Statistical Testing
Other Forms of Structured Data
Science Isn't Broken: p-hacking
Goodhart's Law (Optional)
The 5 Graph Algorithms that you should know
Post-course Survey
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Final Quiz
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