The University of Michigan is a Michigan public research university located in Ann Arbor, Michigan. The University of Michigan’s mission is to serve the people of Michigan and the rest of the world by being a leader in the creation, dissemination, preservation, and application of knowledge, art, and academic values, as well as in the design of leaders and citizens who will enrich the present and challenge the future.
The University of Michigan offers different courses in different sections. One of the University of Michigan courses is about data science, which includes 26 courses. Which will be explained below:

1: Introduction to Data Science in Python (Free Enrolling)

The fundamentals of the Python programming environment, including core python programming methods like lambdas, reading and manipulating CSV files, and the Numpy library, will be covered in this course. The popular Python pandas data science library will be used to teach students how to manipulate and clean data, explain the abstraction of Series and DataFrame as the key data structures for data analysis, and provide lessons on how to efficiently use tools like groupby, merge, and pivot tables.

Students will be able to take tabular data, clean it up, modify it, and perform fundamental inferential statistical analyses at the conclusion of the course. Prior to taking Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, or Applied Social Network Analysis in Python, you should take this course.

Important Details

• Subject: Data Science
• Language: English
• Duration: 4 weeks
• Instructor: Christopher Brooks

You will learn and Gain

• Knowing how to manipulate csv files and using lambdas.
• Describe popular Python capabilities and functions used in data research.
• Inquire about DataFrame structures for processing and cleaning.
• Explain sampling, distributions, and t-tests.
• You will gain: Python Programming, Numpy, Pandas, and Data Cleansing.

2: Applied Social Network Analysis in Python (Free Enrolling)

Through lessons utilizing the NetworkX package, this course will introduce the learner to network analysis. Understanding network analysis and the rationale for why we could represent things as networks is the first step in the course. The notions of connection and network resilience are introduced in the second week. The third week will focus on methods for evaluating a node’s significance or centrality inside a network.

The last week will examine models for network formation and the link prediction issue, as well as the temporal development of networks. After taking Python’s Introduction to Data Science, Python’s Applied Plotting, Charting & Data Representation, and Python’s Applied Machine Learning, students should take this course.

Important Details

• Subject: Data Science
• Language: English
• Duration: 4 weeks
• Instructor: Christopher Brooks

You will learn and Gain

• Utilize the NetworkX library to represent and operate with networked data.
• Analyze a network’s connection.
• Calculate a node’s prominence or centrality inside a network.
• Determine how networks will change throughout time.
• You will gain: Graph Theory, Network Analysis, Python Programming, Social Network Analysis

3: Using Databases with Python (Free Enrolling)

Students will learn the fundamentals of SQL in this course, as well as the fundamentals of database architecture for storing data as part of a multi-step data collection, analysis, and processing endeavor. The database for the course will be SQLite3. Web crawlers and multi-step procedures for data collection and visualization will also be built.

The D3.js package will be used to do simple data visualization. The chapters 14 and 15 of the book “Python for Everyone” will be covered in this session. You must be familiar with the information from the first three courses in this specialty as well as Chapters 1 through 13 of the textbook in order to succeed in this course. This lesson introduces Python 3.

Important Details

• Subject: Data Science
• Language: English
• Duration: 5 weeks
• Instructor: Charles Severance

You will learn and Gain

• To manage databases, use the Create, Read, Update, and Delete actions.
• Describe the fundamentals of Object-Oriented Python.
• Recognize how data is kept in a database’s many tables.
• Data visualization using the Google Maps API.
• You will gain: Python Programming, Database (DBMS), Sqlite, SQL

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• Good with Words Online Course Writing and Editing: Word Choice and Word Order
• Good with Words Online Course Writing and Editing: Structure and Organization

4: Understanding and Visualizing Data with Python (Free Enrolling)

Learners will be introduced to the topic of statistics in this course, covering how data are collected, how studies are designed, how to manage data, and how to explore and visualize data. In addition to learning how to display, evaluate, and interpret summaries for both univariate and multivariate data, learners will be able to recognize various types of data.

Also covered will be the distinctions between probability and non-probability sampling from bigger populations, the notion of sample estimate variability, and the use of probability sampling to draw conclusions about larger populations. At the conclusion of each week, students will use Python in the course environment to apply the statistical principles they have studied. Learners will explore the various applications of Python as a tool during these lab-based workshops, covering the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Learners may follow along with tutorial videos as they create visualizations and handle data with Python. The Jupyter Notebook environment is used in this Coursera course.

Important Details

• Subject: Data Science
• Language: English
• Duration: 4 weeks
• Instructors: Brenda Gunderson, Kerby Shedden, and Brady West.

You will learn and Gain

• Correctly recognize distinct data types and be aware of the varied applications for each.
• Python may be used to produce data visualizations and monetary summary.
• communicate statistical concepts in a simple and succinct manner to a large audience.
• Determine the best analytic methods to use with probability and non-probability samples.
• You will gain: Statistics, Data Analysis, Python Programming, Data Visualization (DataViz)

5: Data Collection and Processing with Python (Free Enrolling)

You will learn how to retrieve and manipulate data from online services in this course. It covers Python list comprehensions and offers chances for deep layered data processing and data extraction practice. Additionally, you’ll learn how to communicate with REST APIs using the Python requests module and what to look for in the documentation of such APIs. You’ll build a “tag recommender” for the flickr photo-sharing website as your final project.

If you have already completed the courses “Python Basics” and “Python Functions, Files, and Dictionaries,” this course is a good fit for you (courses 1 and 2 of the Python 3 Programming Specialization). You can still profit from this course without having taken the first two if you already have a solid understanding of Python’s principles but would like to experience getting complicated nested data from online services and processing it.

Important Details

• Subject: Data Science
• Language: English
• Duration: 4 weeks
• Instructors: Paul Resnick, and Jaclyn Cohen

6: Python Function, Files, and Dictionaries (Free Enrolling)

The dictionary data structure and user-defined functions are introduced in this course. You’ll discover named functions, lambda expressions, optional and keyword parameter passing, local and global variables, and more. You’ll also learn about the sorted function in Python and how to influence the order in which it sorts by providing it with the input of another function.

You’ll read in simulated social media data from a file for your final project, calculate sentiment ratings, and output.csv files. It covers chapters 10 through 16 of the optional and free supplementary text for this course, “Fundamentals of Python Programming.” If you have already completed the “Python Basics” course and wish to learn more about the Python language’s foundations, this course is a good fit for you. Together, both courses are designed for those who are new to Python programming, require a refresher on the language’s fundamentals, or who may already be familiar with it but want a more in-depth explanation and vocabulary for describing and evaluating applications.

Important Details

• Subject: Data Science
• Language: English
• Duration: 5 weeks
• Instructors: Paul Resnick, Jaclyn Cohen, and Steve Oney

7: Python Basics (Free Enrolling)

The fundamentals of Python 3 are covered in this course, including strings and lists as data structures, conditional execution and iteration as control structures. You’ll instruct an on-screen Turtle to create artistic images. Additionally, you’ll develop your ability to debug programs by learning how to reason about program executions by using reference diagrams. No prerequisites exist for the course.

It will cover Chapters 1 through 9 of the optional and free supplementary material for this course, “Fundamentals of Python Programming.” If you’re new to Python programming, need a refresher on the fundamentals, or have some experience with Python programming but want a more in-depth explanation and language for describing and analyzing programs, this course is for you.

Important Details

• Subject: Data Science
• Language: English
• Duration: 52 weeks
• Instructors: Paul Resnick, Jaclyn Cohen, and Steve Oney

8: Python Classes and Inheritance (Free Enrolling)

Classes, instances, and inheritance are introduced in this subject. You will discover effective and intuitive methods to express data using classes. Additionally, you’ll learn how to make “inherited” classes that reuse code and how to override built-in methods. Additionally, you’ll discover how to create lessons. The excellent programming practice of creating automated tests for one’s own code will be covered in the last section.

The course is most appropriate for you if you already have a basic understanding of Python, which is taught in the courses “Python Basics” and “Python Functions, Files, and Dictionaries” (courses 1 and 2 of the Python 3 Programming Specialization). The course “Data Collection and Processing with Python” (course 3 of the specialty) is optional, however having experience with complicated layered data retrieval and processing is useful.

Important Details

• Subject: Data Science
• Language: English
• Duration: 4 weeks
• Instructors: Paul Resnick, and Steve Oney

How to Apply?

You can choose the course you wish to take and enroll for free when applying.

• Apply for Introduction to Data Science in Python.
• Apply for Applied Social Network Analysis in Python.
• Apply for Using Databases with Python.
• Apply for Understanding and Visualizing Data with Python.
• Apply for Data Collection and Processing with Python.
• Apply for Python Function, Files, and Dictionaries.
• Apply for Python Basics.
• Apply for Python Classes and Inheritance.

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To see other course and for more information, kindly visit University of Michigan’s official website.