I graduated from Ozyegin University, Industrial Engineering Department. I have worked in the supply chain departments of different fields in industry like durable goods, cosmetics and cash&carry. My goal is became an expert in data analytics.
For detail information about me, please check my Linkedin profile >> LinkedIn
In this video, lecturer explains identifying errors has a crucial impact on making trustworthy code. It is really important especially in health applications. While working on big data, defining a single root cause is one of the hardest parts. While searching for error, entire process should be examined and analyzed to narrow the focus scope. Then, control points should be located at most serious errors occurred in the data. Check points detect and prohibit errors at relevant part of code. There should be several control points to ensure confidence. You can access the video via link below.
>> 5 Reasons to choose R for Data Science
In this article, author give information about advantages of programming language R. It is most widely used language of Data Science. R community is famous in continuously developed packages for clients to utilize. One of the reasons to consider R language is that most powerful companies like Google are utilizing R. Data manipulation in data science has a significant role and R provides effective tool to use. Finally, R is open-source and you need not pay any membership charges.
>> Data Analysts Captivated by R’s Power
In this column, journalist discuss about the power of R. R language became widespread among statisticians because it doesn’t require high level of computer language knowledge and it is adoptable for different type of studies. Due to the fact that R is free, any person can access and learn it. Some people define R as “supercharged Microsoft’s Excel” and it can be assumed, R language will be second language of statisticians.
This article examine different type of visualization in R language.Data visualization is important to understand and analyze the data.The author claims that in 5 or less code lines, we can create meaningful visualization. She uses two different data sets; the first one includes donated money in Millennium Development Goals in Kenya, the second one includes donated money in Kenya. She creates bar, column, pie and bubble charts, also a map of Kenya. She creates all of them thanks to packages and library inside R.