Archana R

• Developed techniques to make my project more accessible
• Gained knowledge of my strengths and weaknesses
• Collaborated with teams on determining solutions to complex problems
• Utilized resources to come up with innovative ideas for my project

• Training: Received first week training on work to be done.
• Data Mapping: Mapping of financial statements of foreign companies.
• Data Verification: Check whether the information updated is right

• Explore phenomenon
• Use age distribution data
• Reproduce difference in average age at death
• Analyse probability of being a certain age at death
• Bayesian statistics
• Pandas
• Explore phenomenon
• Analyse age distribution data
• Reproduce difference in average age at death
• Refute claim of early death for left-handers
• Use pandas and Bayesian statistics

• Applied SQL queries to analyze the order management functionality of an online retail store using the provided "orders" database.
• Generated actionable insights through the execution of queries aimed at understanding customer behavior, product popularity, and sales patterns.
• Leveraged Joins, Sub Queries, and various SQL clauses to extract and manipulate data.
• Utilized MySQL Workbench for database interaction and query execution.
• Contributed to data-driven decision-making by providing valuable insights into customer behavior and sales patterns.
• Demonstrated proficiency in SQL query execution, data manipulation, and database interaction.
• Collaborated effectively with the team to extract meaningful information from the orders database.
• Mention measurable impact or results you helped deliver. Focus on the outcome of tasks. Example: Modified customer service support system to reduce complaints by 10%.

It is a fascinating project! To effectively analyze the Point of Sale (POS) data for the coffee chain restaurant and provide suitable recommendations, you will need to employ several skills and tools

• Exploratory Analysis: This involves delving into the POS data to understand patterns, trends, and anomalies. You'll likely be using statistical techniques and visualizations to gain insights into customer purchasing behavior and popular items.
• Market Basket Analysis: This is a crucial technique for identifying which items are frequently purchased together. Understanding these patterns can help in creating popular combo meals and optimizing the menu.
• Marketing Analytics: Analyzing the POS data to uncover customer preferences and behavior can provide valuable inputs for marketing strategies targeted at increasing revenue.
• KNIME: KNIME is a powerful open-source data analytics, reporting, and integration platform. It can be used for data preprocessing, exploration, analysis, and visualization, making it useful for this project.
• To carry out this project successfully, it will be essential to have a strong understanding of data analysis and statistical methods, as well as proficiency in using tools like KNIME for processing and analyzing large datasets. Additionally, knowledge of marketing principles and consumer behavior will be beneficial for drawing meaningful conclusions from the POS data.
• Mention measurable impact or results you helped deliver. Focus on the outcome of tasks. Example: Modified customer service support system to reduce complaints by 10%.

• Analyzed historical monthly sales data for two different products of ABC Estate Wines company.
• Utilized various forecasting models including Exponential Smoothing, ARIMA/SARIMA, and Moving Average to predict monthly sales for the next 12 months.
• Recommended the optimum forecasting model and provided lower and upper confidence limits for the forecasts.
• Developed accurate sales forecasts to aid in strategic decision-making for ABC Estate Wines company.
• Demonstrated proficiency in time series forecasting methodologies and techniques.
• Collaborated effectively with team members to analyze and interpret sales data.
• Mention measurable impact or results you helped deliver. Focus on the outcome of tasks. Example: Modified customer service support system to reduce complaints by 10%.

• Description: Developed a credit risk default model for Indian companies using historical performance data to predict loan default probability.
• Skills & Tools: Credit Risk Assessment, Loan Default Prediction, Financial Modeling
• Outcome: Successfully created a model with predictive capabilities, contributing to a better understanding of credit risk in the Indian market.
• Mention measurable impact or results you helped deliver. Focus on the outcome of tasks. Example: Modified customer service support system to reduce complaints by 10%.

• Utilized concepts of linear regression to develop a predictive model for gem prices based on multiple variables.
• Analyzed and processed large datasets to identify key factors influencing gem prices.
• Worked collaboratively with a team to optimize the model and maximize profit margins for the company.
• Successfully implemented the predictive model to assist in strategic decision-making and pricing strategies.
• Skills utilized: Linear Regression, Data Analysis, Predictive Modeling, Team Collaboration
• Mention measurable impact or results you helped deliver. Focus on the outcome of tasks. Example: Modified customer service support system to reduce complaints by 10%.

• Leveraged logistic regression and linear discriminant analysis to develop a predictive model for customer purchase behavior regarding holiday packages.
• Analyzed relevant customer data to identify key factors influencing purchase decisions.
• Collaborated with a team to implement the predictive model and target the relevant customer base effectively.
• Contributed to strategic marketing decisions based on the model's insights and recommendations.
• Skills utilized: Logistic Regression, Linear Discriminant Analysis, Predictive Modeling, Data Analysis, Collaborative Problem-Solving
• Mention measurable impact or results you helped deliver. Focus on the outcome of tasks. Example: Modified customer service support system to reduce complaints by 10%.