ANISHA SHARMA

I am Anisha Sharma, a passionate and results-oriented data analyst with a deep commitment to turning complex data into valuable insights that drive business success. With a strong foundation in computer science and advanced expertise in data analysis, I thrive on leveraging cutting-edge tools and technologies to optimize data workflows, enhance decision-making, and solve real-world business problems.

My expertise lies in data manipulation, predictive modeling, and cloud-based analytics, utilizing tools like Python, R, SQL, AWS, and GCP. I have worked with diverse teams to streamline data processes, build scalable data solutions, and improve operational efficiency across various sectors.

I hold a Masters degree in Information Systems from New Jersey Institute of Technology, where I specialized in leveraging data analytics and AI for business optimization. My Bachelor’s degree in Computer Science laid the foundation for my technical and analytical skills.

Outside of work, I stay actively engaged with the data analytics community by sharing insights and exploring new trends in AI and data science. I also continually enhance my skills by participating in hands-on projects and expanding my knowledge through continuous learning.

My journey began at GH Raisoni College of Engineering, where I earned my Bachelor's in Computer Science. During my academic years, I gained foundational skills in programming and data management, which sparked my interest in data analytics. I developed a solid understanding of SQL and Excel, tools that would later become key components of my career.

Throughout my career as a data analyst, I have cultivated a diverse skill set and a passion for transforming raw data into strategic insights that fuel business growth and efficiency.

At Mindtree, I honed my data analysis skills by working on complex datasets and utilizing advanced tools like Python and SQL. I played a key role in automating data wrangling tasks, which significantly improved the efficiency of data preparation processes. Through my work with machine learning models using Scikit-learn, I helped improve business decision-making by extracting actionable insights from large datasets. I also collaborated with stakeholders to develop interactive dashboards using Tableau, which not only facilitated real-time tracking of business metrics but also contributed to faster decision-making and more informed strategic initiatives.

In my current role at Metlife, I have been at the forefront of optimizing data workflows and implementing scalable solutions using cloud platforms like AWS and GCP. I have streamlined complex data extraction processes, improving the speed and reliability of data retrieval. Additionally, I’ve led the development of Tableau dashboards and optimized SQL queries, resulting in enhanced performance and user engagement. My focus on cloud data migration has facilitated seamless access to critical data across teams, driving more agile decision-making and ensuring that the company’s data infrastructure is both scalable and resilient.

With a Masters in Information Systems from NJIT and a passion for continuous learning, I am eager to apply my technical expertise in data analysis, cloud technologies, and machine learning to drive impactful business outcomes. I am always seeking new opportunities to leverage data for innovation and operational excellence.

This virtual job simulation focused on analyzing multiple datasets to extract insights that enhanced a social media client's content strategy. By leveraging Excel features like VLOOKUP, SUMIF, and IFERROR, strategic insights were generated to optimize content effectiveness. Data visualizations, including PivotTables and charts, were designed to communicate trends and influence decisions, which in turn improved user engagement.

This project utilized Azure Data Studio for importing, normalizing, and integrating a large-scale weather dataset. By creating complex T-SQL queries and stored procedures, this project enhanced the ability to conduct in-depth trend analysis. The findings provided valuable geolocation insights that aided in better-targeted decision-making processes.

This virtual job simulation involved predicting customer churn using machine learning models, including XGBoost and Random Forest. The analysis guided the development of retention strategies by identifying key factors contributing to churn. Interactive visualizations helped inform data-driven pricing adjustments, ultimately reducing churn risk and improving customer retention efforts.

In this project, I scraped and cleaned over 53 years of IMDb movie data using Python, WebScraper.io, NumPy, and Pandas. The goal was to identify trends across decades, including ratings, genres, and production patterns. I visualized key findings with Matplotlib and Seaborn, providing actionable insights for predictive modeling in the movie industry. The analysis revealed significant trends that could inform future predictions and help industry stakeholders understand shifts in audience preferences and movie performance over time.

This analysis involved exploratory data analysis (EDA) on a Twitter dataset from Kaggle, improving data quality for more accurate sentiment predictions. By training an LSTM neural network using TensorFlow and Keras, the project achieved a sentiment prediction accuracy of 90%, providing valuable insights for sentiment analysis and social media trend forecasting.