I’m currently pursuing an MS in Applied Statistics with a specialization in Business Analytics, backed by a Mathematics degree and a year of industry experience as a Data Analyst. I bring a versatile skill set that combines technical expertise with business insight. From understanding client problem statements to applying domain knowledge and translating complex data into clear, actionable reports, I make insights accessible to audiences of all backgrounds.
In 2021, my journey to become a Data Scientist began, leveraging my programming expertise in C++, Java, HTML, CSS, JS, Matlab, and Fortran, along with a strong foundation in math and statistics from my degree.
The data-driven world and AI revolution inspired me to pursue my career/research in Computer Vision, Deep Learning, ML. I immersed myself in self-learning through YouTube, Google, Coursera and DataCamp. Completing the Data Science career track, I honed skills in SQL, Excel, Python, Pandas, Matplotlib, Seaborn, NumPy, SciKit-Learn, TensorFlow, SciPy, Pingouin, Tableau etc. Taking on the role of a Data Analyst, I secured Datacamp certification exam, excelling among 95% of my peers. I expanded my expertise with continuously learning and undertaking projects, aspiring to master the art of making data dance at my fingertips.
This percentage reflects my confidence level in using each tool.
Working as a Data Analyst with student learning outcomes data from BGUS, NSSE, and similar sources. I collect, clean, and transform data in Excel and create Power BI reports that deliver actionable insights to help university authorities enhance the student experience.
November 2024 - June 2025
Started as an intern, gaining hands-on experience with Microsoft tools like Power BI,
Fabric, Excel, and PowerApps to clean, transform, and analyze business data.
Developed interactive reports and dashboards that provided actionable insights
for informed decision-making.
As a full-time analyst, I maintained and validated
data pipelines, troubleshot prebuilt models, created new client-driven reports,
and optimized performance by converting Power Query transformations into SQL,
significantly reducing load times.
August 2025 - On going
The program emphasizes statistical modeling, data science, and business analytics, with training in database management, business intelligence, and big data. Through my research assistantship and projects, I aim to apply descriptive, predictive, and prescriptive analytics using Power BI, Excel, Python, and SQL to solve real-world business problems.
Since Microsoft tools like Excel, Power BI, and Fabric are widely used in data roles, I learned and got certified directly from the source. I gained hands-on experience applying these tools at DataCrafters, mastering a wide range of functionalities. I hold certifications in PL-300, DP-600, and DP-900.
September 2024- January 2025
Completed one semester with audit courses in Programming, Databases, Machine Learning, and AI before relocating abroad.
DataCamp has been my primary learning platform, where I mastered most data tools and applied them to hands-on projects. Through dedication and hard work, I proudly earned the Professional Data Analyst Certification and Professional Data Scientist Certification
January 2019 - December 2023
I possess a strong foundation in mathematics, encompassing calculus, linear algebra, discrete mathematics, and numerical analysis. Additionally, I have acquired coding proficiency in Fortran and Matlab, allowing me to apply mathematical concepts to real-world problems. I've also done some minor courses in Statistics and Physics.
I have successfully completed various real-world data science projects utilizing Python, Excel, SQL, Tableau, and Statistics. For more details about these projects, feel free to explore them by clicking the link🔗 icon.
Big Data, PySpark, Databricks
This project aims to identify key performance indicators (KPIs) and address essential business questions to improve operational efficiency, enhance customer satisfaction, and optimize financial performance through business analytics techniques.Click Here to learn more.
Python, DL, CNN, Web Scraping
This project focuses on classifying vehicle images into three categories. The project involves data collection, data augmentation, and transfer learning using ResNet50. The objective is to achieve high classification accuracy by leveraging pre-trained deep learning models and robust data augmentation techniques. Click Here to learn more.
Python, Classification, EDA
It's a binary classification problem with business metrics and analysis. Click Here to learn more.
Python, ML, Flask
This project leverages a ML approach to predict the base salary of employees in MNCs. Used libraries: Pandas, Matplotlib, Seaborn, Scikitlearn, Flask. Click Here to learn more.
Big Data, ML, Pyspark, Plotly
This project leverages a Big Data and ML approach to predict the unit quantity for a retail store. Used libraries: PySpark, Plotly. Click Here to learn more.
Python, ML , Streamlit
Regression Analysis to predict car selling price with given datasets. Used libraries: Pandas, Matplotlib, Seaborn, Scikitlearn. Click Here to learn more.
Python, Unsupervised ML
Using Kmeans and PCA analysis to Segment Customers and Visualize. Used libraries: Pandas, Matplotlib, Seaborn, Scikitlearn. Click Here to learn more.
Python, ML, EDA
Using Machine Learning's Classification methods to predict if a person has heart disease or not. Used: Pandas, Matplotlib, Seaborn, Scikitlearn. Click Here to learn more.
Python, Web Scraping, EDA
Web Scraping GSMarena to extract smartphone specs with their ratings. Used: BeautifulSoup4, Requests, Pandas, Matplotlib, Seaborn Click Here to learn more.
Python, EDA, Data Cleanup
Cleaning Smartphone Specs data to get it ready for Analysis and Prediction Model. Used: Pandas, Matplotlib, Seaborn Click Here to learn more.
Python, ARIMA, Prophet, SARIMAX
Using ARIMA, SARIMAX, Prophet model to predict Retail Store's Sales. Used: Pandas, Matplotlib, Seaborn, Plotly, ARIMA, SARIMAX, Prophet. Click Here to learn more.
Tableau
This Tableau dashboard presents insights derived from a company's employee dataset, offering real-time filtration of key performance indicators (KPIs) relevant to HR professionals.. Used: Tableau. Click Here to learn more.
Hypothesis Testing, Python
Hypothesis Testing on Drug Safety using python statistical library. Used: Python, Pandas, Matplotlib, Pingouin, Scipy. Click Here to learn more.
Hypothesis Testing, Python
Hypothesis Testing using python's statistical libraries to answer Yulu business case questions. Used: Python, Pandas, Matplotlib, Pingouin, Scipy. Click Here to learn more.
Tableau
Tabaleu Dashboard/Story creation using Insurance dataset. Used: Tableau. Click Here to learn more.
Excel
Calculates calories, fat, proteins, carbs intake with available foods in dataset. Used: Excel, VLOOKUP Click Here to learn more.
Tableau
Food Sales Analysis using Tableau and created Dashboard, Stories. Used: Tableau Click Here to learn more.
Excel
Work Safety Data Analysis Using Excel . Used: Excel, PivotTable Click Here to learn more.
PostgreSQL
Europearn Football League matches analysis using PostgreSQL. Used: PostgreSQL Click Here to learn more.
Python, Web Scraping, EDA
Fetch data from Spotify Playlist, then cleaned the data and perform EDA. Used: Spotipy, Requests, Pandas, Matplotlib, Seaborn Click Here to learn more.
Python, EDA
Performed EDA on Suicide Data to get insightful information. Used: Pandas, Matplotlib, Seaborn, Plotly Click Here to learn more.
Python, EDA
Performed EDA on Google Playstore's App data. Used: Pandas, Matplotlib, Seaborn, Plotly Click Here to learn more.
Python, EDA
Performed EDA on RentTheRunaway's Clothing Fitting Data. Used: Pandas, Matplotlib, Seaborn, Plotly Click Here to learn more.
Python, ML, Time Series
Using Machine Learning's Boosting methods to predict Delhi's temperature, humidity. Used: Pandas, Matplotlib, Seaborn, Scikitlearn. Click Here to learn more.
Python, ARIMA, Time Series
Using Python's ARIMA model to predict US population. Used: Pandas, Matplotlib, Seaborn, ARIMA. Click Here to learn more.
Python, EDA
It's an A/B testing of which product selling strategy is better. Used: Pandas, Matplotlib, Seaborn Click Here to learn more.
Python, EDA
we're going to analyze the data that made Semmelweis discover the importance of handwashing. Used: Pandas, Matplotlib, Seaborn, Statistics Click Here to learn more.
Python, EDA
Data Analysis Using Python on Netflix Data. Used: Pandas, Matplotlib, Seaborn. Click Here to learn more.
Excited to collaborate on a project? Don't hesitate to reach out to me through the following links. If you have any questions or uncertainties about your project, feel free to message me. Let's explore the possibilities together, and if our goals align, we'll embark on an incredible journey of data-driven success!