Week 5
Week 1
I graduated from Gazi University, Department of Economics in January 2016. I have been working as an auditor at “Yapı Kredi Bank” since 2018. During this time, I audited the commercial and retail branches of the bank, but recently transferred to the operational risk unit. During this time, I met SQL due to my job and started to develop myself in this field. I hope to be able to conduct higher quality and more extensive audits with the skills I have gained here.
globaltrends
The globaltrends package is a package that measures, analyzes and visualizes the distribution of data provided by Google Trends between countries. Google Trends normalizes data based on time but it causes problems when working with large scale of data. globaltrends offers a solution via re-normalization. globaltrends re-normalizes data from Google Trends by using “object” and “control” keywords. Instead of looking at the raw search volumes on Google Trends, globaltrends package transform them to a search score.
The package has two measures for internationalization. One of them is degree of internationalization. It is an indicator that checks search volumes of the topics across the globe. If countries across the globe equally interested in same topic then topic has high level of internationalization. This is a unweighted data so every country counts as same. Second measure is volume of internationalization. It is based how much attention topic is getting on the global level.
Analyzing NBA Player Data
The areas where data analytics are used are increasing day by day. In this project author cleans basketball players raw data by using R language. To do this, author starts with cleaning the raw data obtained from basketball-reference.com. Author starts cleaning the data by converting the statistics of the athletes in the “character” format to the “integer” format and deleting the repetitive values. After cleaning the data, he creates a data frame for player stats. Next step is clustering player data. Link-1 Link-2
Use of R in Banking-Finance
The banking industry is one of the data-rich industry. Banks use the market and customer data they collect to make data-driven decisions. In banking industry R used for credit risk modelling and other forms of risk analytics. R plays an important role in the financial sector as well as the banking sector. R is used for risk measurement adjust risk performance and utilize visualizations.
Visualize the Pandemic with R
In this project, covid data was visualized using nCov2019 package. The nCov2019 package makes it possible for us to countries access and visualize historical case data of the countries. In this project you can see how to deploy the package, exploration of the data and how to create visuals based by cases. From this point on it is possible to make inferences like “How many cases will US have in the next 10 days?” or “Is corona virus affecting Hollywood?”. Beside this questions author seeks answers for “Are we still dining out?” To find a answer for this question https://www.opentable.com/state-of-industry which is a web site that provides reservation data.