By the end of this course, learners are provided a high-level overview of data analysis and visualization tools, and are prepared to discuss best practices and develop an ensuing action plan that addresses key discoveries. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story.
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이 강좌에 대하여
It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations.
배울 내용
Identify stakeholders and key components imperative to an analytics project plan
Name strengths and weaknesses of different analysis and visualization tools
Visually identify, monitor, and remove process variation
Explain how to create a compelling data story
귀하가 습득할 기술
- Data visualization tools
- Data storytelling
- Data analysis tools
- Data-Driven Decision Making
- Statistical process control (SPC)
It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations.
제공자:

뉴욕주립대학교 버펄로 캠퍼스
The University at Buffalo (UB) is a premier, research-intensive public university and the largest, most comprehensive institution of the State University of New York (SUNY) system. UB offers more than 100 undergraduate degrees and nearly 300 graduate and professional programs.

뉴욕주립대학교
The State University of New York, with 64 unique institutions, is the largest comprehensive system of higher education in the United States. Educating nearly 468,000 students in more than 7,500 degree and certificate programs both on campus and online, SUNY has nearly 3 million alumni around the globe.
강의 계획표 - 이 강좌에서 배울 내용
Data Analysis Software Tools
This module provides an overview of the tools needed for data analysis.
Statistical Process Control (SPC)
This module covers SPC, a way to analyze variation over time in your process using data. It is helpful in identifying current problems and can also be used to monitor the process for any deviations once the process is ‘in control'.
Data Visualization and Translation
This module provides tools for leveraging data through visualization and translation.
Project: Data Analysis and Visualization
This module provides an opportunity to bridge theory and practice. Learners apply knowledge from this course to solve a business problem.
Data-Driven Decision Making (DDDM) 특화 과정 정보
This specialization explains why it is important to leverage data when contemplating organizational choices, and supplies the tools at the heart of data-driven decision making (DDDM). The three-course series explores how technology enables the collection and organization of unprecedented amounts of data, and how to dissect that data to gain powerful insights. Course topics include analyzing process maps for driving improvement, software for maximizing data analysis, statistical process control, creating metrics dashboards and translating data stories, and the connection between operations technology metrics and organizational performance. Content touches on leadership’s role in instituting an internet of things (IoT) strategy in manufacturing and service environments. Lessons feature case studies highlighting ROI achieved through DDDM, and the cultural changes required for success.

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