Introduction to Data Science in Python
|University:||University of Michigan|
|Certificate:||Yes, if purchased. $79.|
In this intermediate four-week course by Coursera, you will learn the basics of Python from the initial installing and downloading, all the way to DataFrames and running basic inferential statistical analyses.
As this is the first course of five in the Applied Data Science with Python specialization, it is meant for those with basic programming skills or experience in Python. Since the first course acts as a refresher, you will only need a minimal background in statistics.
A complete comprehension of Python basics, so you can successfully continue on track to completing the Applied Data Science with Python course by understanding:
- Types and Sequences
- Data Structure
- Querying and Indexing (DataFrame and Date Structures)
- Statistical techniques (distributions, t-tests, samplings)
- Meging DataFrames
- Pandas Idioms
- Pivot Tables
The course information is explained, including expectations and grading in the first week. You will start with the basics of Python, which will prepare you for the following week when you encounter a toolkit for data cleaning and processing—otherwise known as Panda.
In week three, you will further your understanding about Panda through merging data frames and grouping data. Upon completion, you will have also completed a course project, which is compatible as a data science portfolio. It will involve real-world data and assess your knowledge of finding significance in data.
After this course, you will be able to handle different data types and perform analysis—including filtering, grouping, and merging data. It’s a great introduction to Python libraries and their purpose/function in the field of Data Science. You may also consider to enroll into the entire specialization program this course belongs to.
- Videos, readings, and notebooks present material in different ways.
- Quizzes and assignments help to practice and deeply learn the concepts.
- Discussion questions prompt you to actively think about the material.
- Subtitles are included to ensure a better understanding while listening to the videos.
- Forums are active and informative to clarify any questions.
Introduction to Data Science in Python – Summary, Coupon
Created by the University of Michigan, these detailed lectures provide content in a hands-on oriented approach, which is ideal for those who intend to dig deeper, and improve their Python programming.
Your experienced mentors are active in the learning process and available for help. They also recommended other reference books for individual learning.
The lectures are clear, but the main learning happens while completing the weekly assignments. Each assignment encourages your to search for content beyond the lectures, so you might find new material in the process.