View source for LII:Machine Learning for Data Science and Analytics

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'''Title''': ''Machine Learning for Data Science and Analytics''

'''Author for citation''': Salleb-Aouissi ''et al.''

'''License for content''': Unknown

'''Publication date''': 2022

This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take, requiring about seven to 10 hours per week of effort. A Verified Certificate can be added for $99. (Note that the free Audit track closes on August 29.)

The edX course description:

"Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.

This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in big data analysis.

What you'll learn:

* What machine learning is and how it is related to statistics and data analysis
* How machine learning uses computer algorithms to search for patterns in data
* How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth
* How to uncover hidden themes in large collections of documents using topic modeling
* How to prepare data, deal with missing data and create custom data analysis solutions for different industries
* Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming"


'''About the authors'''

The course has six instructors associated with it. See the "[https://www.edx.org/course/machine-learning-for-data-science-and-analytics meet the instructors]" section to see more about each instructor.


==General layout and contents of the course==
A pre-enrollment syllabus is not available, and the general course layout is not clear without enrolling.

===The course===
[[File:PDF.png|40px|link=https://www.edx.org/course/machine-learning-for-data-science-and-analytics]]: The course can be found on the edX site, under the [https://www.edx.org/course/machine-learning-for-data-science-and-analytics Data Analysis & Statistics] category. The course is available starting in July 2022.

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