Technical SkillsData Management - CRISP-DM framework, Data Analysis, Data visualization with Seaborn and Matplotlib, Data modellingComputer science - Python, Excel functions, MYSQL, RStatistics - Hypothesis formulationSoft Skills• Business Problem Solving, Problem mapping, Solution driven approach• Collaboration and communication• Curiosity, attention to detail• Creativity and StorytellingResponsibilities• Extract, Clean and analyze data from varied sources• Solve problems• Build models• Communicate insightsCase studies Case Study I: Credit EDA• Libraries Used: Numpy, Pandas, Matplotlib.pyplot, Seaborn• Plots Observed: Barplot, Pieplot, Countplot, Boxplot(for Outliers), Histplot, seaborn barplots and distplots, Scatter plots(to observe dependency between variables-linear one), Heatmap(correlation matrix) etc.,• Insights Drawn: Female applicants should be given extra weightage as defaults are lesser. 60% of defaulters are Working applicants. This does not mean working applicants must be refused. Proper scrutiny of other parameters neededCase Study II : SQL-RSVP Movies  Concepts used: SQL and Advanced SQL  IDE : Mysql workbench• Used SQL to analyze the data set and drawn meaningful insights and summarized recommendations to RSVP Movies that can help them start their new projectCase Study III : Bike Sharing• Modelling Type: Linear Regression• Insights drawn : Analyzed the significant variables to predict the demand for shared bikes using modelling techniques

Being consistent is the key to success. Torturing the Data is the key to get valuable insights from Data.Hi there, this is Bhagyasree and that's what I believe inI am on the way in getting trained on PowerBI right from loading the data to creating DAX, Measures, Calculated columns, Modelling and finally creating Dashboards from the experts of Teleperformance(TP-Digital-Analytics)