Welcome to Introduction to Analytic Thinking, Data Science, and Data Mining. In this course, we will begin with an exploration of the field and profession of data science with a focus on the skills and ethical considerations required when working with data. We will review the types of business problems data science can solve and discuss the application of the CRISP-DM process to data mining efforts. A brief overview of Descriptive, Predictive, and Prescriptive Analytics will be provided, and we will conclude the course with an exploratory activity to learn more about the tools and resources you might find in a data science toolkit.
This course is part of the Data Science Fundamentals Specialization
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About this Course
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
The knowledge and skills needed to work in the data science profession
How data science is used to solve business problems
The benefits of using the cross-industry standard process for data mining (CRISP-DM)
Skills you will gain
- Environmental Data Analysis
- Data Documentation
- Geophysical Data
- Data Mining
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Data Science: The Field and Profession
Data Science in Business
Data Mining and an Overview of Data Analytics
Solving Problems with Data Science
Reviews
- 5 stars53.33%
- 4 stars21.90%
- 3 stars13.33%
- 2 stars5.71%
- 1 star5.71%
TOP REVIEWS FROM INTRO TO ANALYTIC THINKING, DATA SCIENCE, AND DATA MINING
It is informative and gives me overview about data science and the future
I consider this course a must for one's journey into Data Science. The videos are short and to the point to serve the purpose of the course.
The knowledge asked in the first quiz, hasn't been mentioned before in the reading.
I learnt about CRISP DM process, Data Science tools, Decision Trees in this course.
About the Data Science Fundamentals Specialization

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