Data Science: Data Driven Decision Making (DDDM)

DATA DRIVEN DECISION MAKING (DDDM) ERA 

Digital

Data-driven decision-making (DDDM) involves utilizing data insights to shape business and research strategies instead of relying solely on intuition or past experiences. With the vast amount of data available today, organizations can apply advanced analytics, artificial intelligence, and statistical techniques to derive meaningful patterns that improve accuracy, efficiency, and strategic direction. In the present time, DDDM enhances productivity, refines marketing efforts, and elevates customer satisfaction by assessing trends, consumer behavior, and performance metrics. In research, it fosters evidence-based conclusions, reducing the likelihood of subjective bias and ensuring findings are rooted in quantifiable data.

One of the primary benefits of data-driven decision-making is its capacity to reduce errors and biases. By harnessing structured and unstructured data, organizations can forecast trends, mitigate risks, and seize potential opportunities. However, its effectiveness depends on the reliability of data sources, analytical expertise, and the correct interpretation of findings. Despite its advantages, DDDM presents challenges such as data privacy concerns, integration of diverse datasets, and the need for enhanced analytical skills. To fully leverage this approach, organizations should invest in data governance, employee training, and advanced technological tools. In the digital age, making decisions based on data is not just an advantage—it is a necessity for sustainable success.

Lecture:

1: https://www.youtube.com/watch?v=7Rd9OpauUNo&ab_channel=MadhusudanSingh

2: https://www.youtube.com/watch?v=tiABVXTGdcE&ab_channel=MadhusudanSingh 

Digital Data Literacy in Digital Transformation

digitalDigital data literacy is understanding and effectively using digital data in business. In digital transformation, digital data literacy is crucial for employees to leverage data and digital technologies to drive business outcomes effectively. It involves a basic understanding of data, how it can inform decisions, and the ability to work with data-related tools and technologies.

 

Data Science General Education Curriculum Design

dataThe project aims to operate within the framework of the fourth industrial revolution, or Industry 4.0, which consists of a natural evolution involving production-related aspects, relating to new communication resources, data processing, and manufacturing approaches, expanding the possibilities of increasing industrial productivity, resource management, and firm performance, with broad positive impacts in several areas. Using developments in microelectronics, computing, and data communication, this process enables new software and artificial intelligence resources to be continuously associated with platforms and production systems, creating the conditions for the automation of prediction, monitoring, and planning, thereby optimizing production.

Courses included in the program:

Creative Convergence Minor Track Curriculum Development 

courseThis project aims to develop an effective curriculum plan for promoting digital transformation in various industries. While learning about digital transformation methods and processes, the students will also cover the most recent and advanced technologies (such as artificial intelligence, data science, or blockchain). As a result, students’ digital literacy will also be expanded. Therefore, the ultimate goal of this curriculum plan is to boost job opportunities for all students, and the employment rate will be enhanced accordingly. 

Courses included in the program:

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