Campus Affiliate | Syllabus
Certificate in Data Science Frontier (CDSF)
Data Science Frontier is an international certification given to professionals involved in Data Science. Data Science Frontier is a recognized industry certification program sponsored by Logical Operations, an internationally recognized certification and training provider based in the US, designed to meet the rapidly changing needs of the Data Science field. The Data Science Frontier designation recognizes professionals who have sufficiently demonstrated their knowledge and skills in data analysis, machine learning, and data-driven decision-making.
Program Details
Durations
This program is 3 days of intensive training class ( 3 x 8 hours training )
Method of Delivery
The program provided by Multimatics will be delivered through interactive presentation by professional instructor(s), group debriefs, individual and team exercises, behavior modelling and roleplays, one-to-one and group discussion, case studies, and projects.
Who Should Attend?
Beginners, Students, Fresh Graduates, Lecturers, Professionals and more who are interesting in data science.
Program Objectives
At the end of the program, the participants will be able to:
- Understand the fundamental principles of data science.
- Identify and apply various machine learning algorithms including those used in classification, regression, and clustering.
- Effectively handle data extraction, transformation, and loading (ETL).
- Develop skills in analyzing data, developing predictive models, and implementing these models to solve real-world problems.
Examination Details
At the end of the program, the participants will be assessed with 90 minutes online and/or paperbased exam. Certification will be awarded by Logical Operations upon the completion of study and passing the exam with a minimum of 70%.
Program Modules
Addressing Business Issues with Data Science
Topic Covered
- Introduction to data science in business
- Identifying business problems
- Formulating data-driven questions
- Case studies of successful data science projects