About Myself
Hello everyone! My name is Mine Kara. I graduated from Istanbul Technical University with a Bachelor’s Degree in Civil Engineering. This year will be my freshman year in Big Data Analytics program at MEF University. Although it is not very relevant to my field, it was a dream for me to start this program. Because I’ve always loved learning to code and I keen on topics like AI and machine learning. Besides, I want to learn image processing especially because I’m really curious how computers recognize images and how this works.
Currently, I am working at architecture company Adatt as civil engineer or to say specifically BIM (Building Information Modeling) modeler. BIM can be explained shortly as process for creating and managing information on a construction project from start to end. Thus, I generate structural 3D models for construction projects and later I collaborate with clients, architects and construction workers. Actually, my job is also data related because all the information coming from different branches stored in one place. We also analyze the data however, probably not the way we will do in this program because we are not applying codes to get the data, we are just using software to get and share it. I just saw a good explanation of BIM in some website, I just want to share with you.
“BIM, as a whole, refers to the process of all parties involved in the construction and lifecycle management of built assets, working collaboratively and sharing data. However, the true power of BIM lives in the “I” (information). All of the information gathered— from conception to completion— isn’t just stored, it’s actionable.”
Reference: What is BIM?
Here is my LinkedIn profile
useR 2021 - Tutorials: Translating R to Your Language
Lecturers: Michael Chirico (Google), Michael Lawrence (Genentech, R-Core)
In the video, the lecturer talked about procedures of translations in R and explains how it works. 16 languages around the world are currently supported including Turkish. In the beginning of this work, translatable messages in R are about 2000 but now almost 5500 messages are translatable.
There are sets of functions such as stop, gettext to translate messages into other languages. The gettext function is basically deciding what language the text is in. After figuring out what language the text needed to translate to, we find a file called corresponding.mo. This file contains a bunch of correspondences between a message and its translation. This file makes it easy and fast to input message id (msgid) to output which is the translation (msgstr).
English to Japanese
Three R-posts:
1 - Digital Image Processing
Digital image processing includes manipulation of digital images with the use of computers through algorithm. The usage of image processing has been growing fast in recent times. It has a wide range of application as listed below.
• Instagram & Snapchat filters
• Computer Vision
• Microscopic Imaging
• Facial Recognition
• Object detection
• X-Ray Bone Detection
• Product Design
For image processing, various languages are being used and R is one of them. To work with image in R, the library called Magick is needed. Magick is one of the famous open source libraries available for editing and manipulating images of different types (Raster & Vector Images). These post below are help us to get started to manipulate images in R.
Post Link: Easy Image Processing in R using the Magick Package
Video: R Image Processing and Image Clustering: Simple Computer Vision in R
2 - Exploratory Data Analysis
Exploratory data analysis (EDA) is used by data scientists to analyze and manipulate data and summarize unique features of them by using data visualization tools. It assist data scientists to get a better understanding of data and extract important variables. Also, it can uncover underlying structure so that it enables for data scientists to draw meaningful conclusions from data.
Post Link: Exploratory Data Analysis
3 - Cluster Analysis in R
Clustering analysis is a form of exploratory data analysis in which observations are divided into different groups that share common characteristics.The goal of clustering is to identify pattern or groups of similar objects within a data set of interest. Applications of Clustering in R are used in various fields such as marketing, retail, medical science and sociology. For example, in cancer research, for classifying patients into subgroups according their gene expression profile. This can be useful for identifying the molecular profile of patients with good or bad prognostic.
There are two main types of classification:
- k-means clustering
- Hierarchical clustering
Post Link: Cluster Analysis in R