Data Mining Foundations and Practice Specialization

Data Mining Foundations and Practice Specialization

Data Mining Foundations and Practice Specialization Lancez votre carrière en science des données. Master core data mining concepts, techniques, and hands-on skills. Taught in English 21 languages available Some content may not be translated Instructor: Qin (Christine) Lv Enroll for Free Starts Mar 21 Financial aid available 3,070 already enrolled Included with • Learn more

Description

Data mining is a rapidly growing field that aims to uncover valuable insights and patterns from large sets of data. As organizations continue to collect and store massive amounts of data, the need for skilled professionals who can analyze and extract useful information from this data is becoming increasingly important. The Data Mining Foundations and Practice Specialization is a comprehensive program designed to provide students with the knowledge and skills necessary to excel in this field.

The specialization consists of a series of courses that cover the fundamentals of data mining, including data preprocessing, classification, clustering, and association rule mining. Students will also learn about advanced techniques such as text mining, web mining, and social network analysis. The courses are taught by industry experts who are actively engaged in research and application of data mining techniques.

One of the key features of the specialization is

Data Mining Foundations and Practice Specialization

Lancez votre carrière en science des données. Master core data mining concepts, techniques, and hands-on skills.

Taught in English

Some content may not be translated

Qin (Christine) Lv

Instructor: Qin (Christine) Lv

3,070 already enrolled

Included with Coursera Plus

Specialization – 3 course series

Get in-depth knowledge of a subject

3.8

(28 reviews)

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace
Progress towards a degree

What you’ll learn

  • Data mining pipeline: data understanding, preprocessing, warehousing

  • Data mining methods: frequent patterns, classification, clustering, outliers

  • Data mining project: project formulation, design, implementation, reporting

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