Software Support Engineer Resume Example

NYC AirBnB Dataset:

• Exploring the data set using the Tidyverse and DataExplorer Packages in R programming.
• Analyzing the correlation between various factors, analyzing duplicate and missing data.
• Finding inside information like most booked neighborhood, prices change with respect to room type,Plotting the price distribution,type_of_room_vs_neighbourhood_group,difference in price across different neighbourhood group,
• Used Box plot, Bar plot , Geo map and Scatter plot to visualize the various stats and scenarios.

World Development Indicator - G7 Member Countries:

• Filtered the data for the G7 membered countries using R scripts.
• Analyzed and visualize the GDP Per Capita Growth, Population trend and Unemployment trend per gender for 50 years using GGPlot library.

Machine Utilization for Coal plant:

• Analyze the monthly data for all machine to analyze the idle capacity of the each machine using R programming.
• Cleaning the data and treating the N/A,Null values.
• Find all the machines which utilization was less than 90%.
• Creating Time series plot for each machine.

World Health Trends:

• Building the joins on the bases of the primary key fields using Tableau.
• Analyzing the fertility rate and life expectancy for each countries in the world for last more than 50 years.

Maintenance for coal terminal:

• Creating joins on multiples tables using the date time field using Tableau.
• Applying the pivot functionality on the master table after join.
• Creating the table calculation to find the average idle capacity of 5 machine express in percentage of nominal capacity and the average is taken 8 hours proceeding to the timeline which was shown in the visualization.
• Calculating the machine which cross their idle capacity by 10% so company can plan services of machine before hand to save millions of dollars.
• Used Reference lines and trend lines to analyze the idle value threshold.

Euro Crises:

• Created visualization using chord diagram showing the total amount with respect to debtor and creditor in Power BI.
• Created a dashboard showing the amount owned by each countries and the risk measure with each countries in form of tree map.

Population Stats:

• Analyze the population trend for 150 years using Power BI across the world.
• Imported data from three time range tables and created joins and cleaned data using various Power BI functionality as per requirement.
• Used Dax formulas like pivot, running average and created a dashboard showing all the population dynamics with respect to the gender and overall trend of population in next 80 yrs to last 70 yrs.