AT METROPLIA
Coupled with the study to strengthen the professional knowledge of the renewable energy field, I involved in various projects and intern positions that significantly improved my competence in not only my specialization but also sustainable development and water management fields.
THE UNIVERSITY
Finland's Largest University of Applied Sciences
Bachelor of Environmental Engineering
GPA 3.4/4.0 (or 4.3/5.0)
RENEWABLE ENERGY
I pursed the major in Renewable Energy as I found my passion for this field throughout numerous studies and researches. I finished a Bachelor Thesis in which I investigated the influence of environmental factors on the photoelectric parameter yielded by photovoltaic modules.
Major
&minor
SUSTAINABLE DEVELOPMENT
I chose sustainable development as a minor track to aid my professional development that I can be fully aware of my role and position in the future circular economy. Thus, I can significantly contribute my knowledge and enthusiasm to the evaluation of this generation.
The Certification
The official certification acquired from coursera
APPLIED DATA SCIENCES WITH PYTHON
The 5 courses in this University of Michigan specialization guided learners to data science through the python programming language. This skills-based specialization focus on applying statistical, machine learning, information visualization, and text analysis techniques to gain new insight into data.
In all of the courses, coding and real-world data analysis projects were provided to build a portfolio that showcases the work as well as gaining insight understanding and application of python language in a specific aspect of data science.
SAS PROGRAMMING FOR
R USER
This course is for experienced R users who want to apply their existing skills and extend them to the SAS environment. Emphasis is placed on programming and not statistical theory or interpretation.
Students in this course should have knowledge of plotting, manipulating data, iterative processing, creating functions, applying functions, linear models, generalized linear models, mixed models, stepwise model selection, matrix algebra, and statistical simulations.
CUSTOMER ANALYTICS
The course enables the learner to describe the major methods of customer data collection used by companies and understand how this data can inform business decisions. Insight of the main tools used to predict customer behavior is presented as well as the identification of the appropriate uses for each tool.
The learner also communicated the key ideas about customer analytics and how the field informs business decisions. Moreover, the history of customer analytics and the latest best practices at top firms are illustrated for more practical understanding