Anu Sree Baburaj

Results-driven and detail-oriented professional with 1 year of experience in client management, business development, and strategic growth initiatives, now looking to transition into data analytics. Skilled in market research, data analysis, and process optimization using Excel, Python, SQL, and CRM software. Adept at turning data into insights to drive operational efficiency and business performance. Currently pursuing a Master's degree in Business Analytics, eager to apply strong analytical abilities, problem-solving skills, and a passion for using data to drive business growth in a dynamic environment.

European Soccer Game Analysis
This project focuses on analyzing the European Soccer Game dataset from Kaggle, utilizing advanced data analytics and visualization techniques to uncover insights into team performance, player statistics, and game outcomes.
Tools and Technologies Used:
● Python: Employing data cleaning and analysis, leveraging libraries such as Pandas and NumPy to preprocess the dataset, handle missing values, and perform exploratory data analysis.
● SQL: Utilizing database management and querying to efficiently retrieve and manipulate data from the relational database, enabling deeper insights into player and team statistics.
● Power BI: Implementing data visualization and reporting, creating interactive dashboards that highlight key metrics, trends, and correlations in team performance and game results.
Key Achievements:
● Successfully cleaning and transforming the dataset, improving data quality and ensuring accurate analysis.
● Conducting comprehensive analyses that identified performance patterns, leading to actionable insights for teams and coaches.
● Developing interactive Power BI dashboards that provided stakeholders with real-time data visualization, enhancing decision-making processes and strategic planning.

Investigating COVID-19 Virus Trends
This project involves analyzing the global COVID-19 pandemic by leveraging R programming and the dplyr library. The project focuses on employing data wrangling techniques to manipulate, filter, and aggregate the dataset. The primary aim is to uncover critical insights into the relationship between testing numbers and positive COVID-19 test rates, enhancing the understanding of how different countries manage the pandemic.
Tools and Technologies:
● Utilizing R programming language along with libraries such as dplyr, readr, and tibble for data manipulation and analysis.
Key Achievements:
● Loading and Exploring Data: Importing the COVID-19 dataset using readr and tibble to understand its structure and contents.
● Data Filtering and Selection: Employing dplyr functions to filter and select relevant data points, focusing on countries with significant COVID-19 statistics.
● Data Aggregation: Aggregating data by country and calculated summary statistics to identify trends in testing numbers and positive case ratios.
● Analysis of Trends: Identifying top countries based on testing numbers and the ratio of positive cases to tests conducted, revealing insights into testing efficacy.
● Data Structure Creation: Creating vectors and matrices to store key findings, facilitating easier manipulation and analysis of the data.
● Result Compilation: Compiling results into a comprehensive list structure to present actionable insights derived from the analysis.