About Us
Fatih Sultan Mehmet Vakıf University, Data Science Application and Research Center was established on 03.03.2021. The main purpose of this center is to support interdisciplinary studies among the relevant faculties, departments and units within the university with activities in the field of data science, and to cooperate with institutions, organizations and industry working in this field.
Data science is the creation and application of powerful new methods for collecting, organizing, analyzing, and exploring large-scale data. Data are simple pieces of information, values or variables that can be used to describe a person, an object or something else. In the digital age, people generate data around the clock through mediums such as smartphones, the internet and social media. More than 2.5 quadrillion bytes of data are generated every day and this number is constantly growing. Looking at the total data generated by 2020, more than 90% of the total data has been collected in the last five years.
When learning data science, one often hears the term 'big data'. Big data refers to datasets (collections of data) that contain vast amounts of complex information that are difficult to process and understand using traditional data processing methods. Data science focuses on concepts, methods and applications to extract meaning from big data. It covers many areas such as Machine Learning, Deep Learning, Reinforcement Learning, Artificial Intelligence, Data preprocessing, Big Data, Probability and Statistics, Optimization, Natural Language Processing, Data Visualization, Ethics, etc.
Data is generated in various fields (medicine and health, energy and the environment, and economics and politics among others), so data science can be applied to everything. By learning how to interpret data, we can better understand and even solve some of the biggest challenges facing the world today.
Some examples of what skills to have and tools to use in Data Science and Engineering by field are summarized below
|
Field |
Skills |
Tools |
|
Data Analysis |
R, Python, Statistics |
SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner |
|
Data Houses |
ETL, SQL, Hadoop, Apache Spark, |
Informatica/ Talend, AWS Redshift |
|
Data Visualization |
R, Python Libraries |
Jupyter, Tableau, Cognos, RAW |
|
Machine Learning |
Python, Algebra, ML Algorithms, Statistics |
Spark MLib, Mahout, Azure ML studio |