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専門講座カリフォルニア大学アーバイン校（University of California, Irvine）このコースについて
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100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の1コース
中級レベル
約7時間で修了
英語
字幕：英語, インドネシア語
学習内容
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)
習得するスキル
- Environmental Data Analysis
- Data Documentation
- Geophysical Data
- Data Mining
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の1コース
中級レベル
約7時間で修了
英語
字幕：英語, インドネシア語
提供：
カリフォルニア大学アーバイン校（University of California, Irvine）
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.
シラバス - 本コースの学習内容
2時間で修了
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.
2時間で修了
2 学習用教材
1時間で修了
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.
1時間で修了
1件のビデオ （合計3分）, 2 学習用教材, 1 個のテスト
2時間で修了
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.
2時間で修了
2 学習用教材
3時間で修了
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.
3時間で修了
1件のビデオ （合計3分）, 2 学習用教材, 1 個のテスト
レビュー
- 5 stars47.43%
- 4 stars21.79%
- 3 stars16.66%
- 2 stars7.69%
- 1 star6.41%
INTRO TO ANALYTIC THINKING, DATA SCIENCE, AND DATA MINING からの人気レビュー
MMにより2021年5月20日
It is informative and gives me overview about data science and the future
RSにより2021年1月2日
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により2021年2月11日
The knowledge asked in the first quiz, hasn't been mentioned before in the reading.
Data Science Fundamentals専門講座について
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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