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.
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Data Science Fundamentals 특화 과정캘리포니아 대학교 어바인 캠퍼스이 강좌에 대하여
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 1번째 강좌:
중급 단계
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영어
자막: 영어, 인도네시아어
배울 내용
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)
귀하가 습득할 기술
- Environmental Data Analysis
- Data Documentation
- Geophysical Data
- Data Mining
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 1번째 강좌:
중급 단계
완료하는 데 약 7시간 필요
영어
자막: 영어, 인도네시아어
제공자:
캘리포니아 대학교 어바인 캠퍼스
Since 1965, the University of California, Irvine has combined the strengths of a major research university with the bounty of an incomparable Southern California location. UCI’s unyielding commitment to rigorous academics, cutting-edge research, and leadership and character development makes the campus a driving force for innovation and discovery that serves our local, national and global communities in many ways.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 2시간 필요
Data Science: The Field and Profession
Welcome to Module 1, Data Science: The Field and Profession. In this module, we will review data science as a field and explore the concepts of small and big data. We will also survey the skills of successful data scientists and discuss the types of business problems data scientists might be asked to solve in the near future.
완료하는 데 2시간 필요
2 개의 읽기 자료
완료하는 데 1시간 필요
Data Science in Business
Welcome to Module 2, Data Science in Business. In this module, we will take a closer look at the applications of data science in a business environment and discuss ethical considerations to keep in mind when working with data.
완료하는 데 1시간 필요
1개 동영상 (총 3분), 2 개의 읽기 자료, 1 개의 테스트
완료하는 데 2시간 필요
Data Mining and an Overview of Data Analytics
Welcome to Module 3, Data Mining and an Overview of Data Analytics. In this module we will begin with an explanation of CRISP-DM, a cross-industry standard process for data mining. We will also provide an introduction to descriptive, predictive and prescriptive analytics.
완료하는 데 2시간 필요
2 개의 읽기 자료
완료하는 데 3시간 필요
Solving Problems with Data Science
Welcome to Module 4, Solving Problems with Data Science. In this last module of the course we will explore some real-world applications of data science solutions and take a closer look at the types of tools and programs you might expect to see in a data science toolkit.
완료하는 데 3시간 필요
1개 동영상 (총 3분), 2 개의 읽기 자료, 1 개의 테스트
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- 4 stars21.79%
- 3 stars16.66%
- 2 stars7.69%
- 1 star6.41%
INTRO TO ANALYTIC THINKING, DATA SCIENCE, AND DATA MINING의 최상위 리뷰
RS 제공2021년 1월 2일
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.
CA 제공2021년 2월 11일
The knowledge asked in the first quiz, hasn't been mentioned before in the reading.
MM 제공2021년 5월 20일
It is informative and gives me overview about data science and the future
Data Science Fundamentals 특화 과정 정보
This specialization demystifies data science and familiarizes learners with key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques. The course also provides an overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations.
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