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I'm Mukut May Dutta, a data storyteller with a unique journey—from a background in engineering to a stint in software sales, and finally back to my true passion: data analytics. My experience in sales taught me the value of understanding business needs, while my engineering roots instilled a problem-solving mindset. Now, I combine both to transform raw data into meaningful insights using tools like Python, SQL, Power BI, and Tableau etc. My mission is to simplify complexity through visual storytelling and help organizations make data-driven decisions.
An interactive Power BI dashboard analyzing Spotify music trends and user behavior. Includes genre insights, top artists, regional patterns, and seasonal listening shifts.
Tech: Power BI, Python, Excel
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A detailed analysis of Goldman Sachs stock performance using Python and financial libraries. Covers stock trend visualization, moving averages, volatility, and returns analysis.
Tech: Python, Matplotlib, Pandas, yfinance
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Conducted an in-depth analysis of trending YouTube videos using Python. Extracted insights on viewer engagement, video categories, publishing patterns, and trending durations.
Tech: Python, Pandas, Seaborn, Matplotlib
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Developed an interactive sales dashboard to analyze Blinkit's product performance, category trends, revenue growth, and region-wise metrics using dynamic filters and key performance indicators.
Tech: Power BI, Excel, Power Query, DAX
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Created an interactive Power BI dashboard to analyze Netflix content by genre, release year, ratings, and country. Uncovered content trends and viewer preferences across regions.
Tech: Power BI, Excel, Power Query, DAX
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Analyzed emergency 911 call data to identify high-volume zip codes, call types, and response patterns. Built a Django web dashboard with interactive charts, maps, and filters for real-time visualization.
Tech: Python, Django, Pandas, Folium, HTML, CSS
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Performed a comprehensive analysis of Apple’s stock data to explore trends in volume, price movements, and volatility. Visualized key patterns and outliers to understand market behavior over time.
Tech: Python, Pandas, Matplotlib, Seaborn
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An in-depth analysis of HDFC stock performance using Python. Explored historical prices, volatility, moving averages, and market trends to generate meaningful insights for investors.
Tech: Python, Pandas, Matplotlib, yfinance, Seaborn
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Built and deployed NLP models to extract insights from unstructured text data. Included sentiment analysis, topic modeling, and keyword extraction from various text sources.
Tech: Python, NLTK, spaCy, scikit-learn, Pandas, Matplotlib
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Performed end-to-end analysis of HR data to uncover patterns in employee attrition, satisfaction, and performance. Included visual insights and predictive modeling to support HR decision-making.
Tech: Python, Pandas, Matplotlib, Seaborn, Power BI
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In-depth analysis of IMDb movie data to uncover trends in ratings, genres, top-rated directors, and yearly production patterns. Visualizations highlight popularity by genre, time trends, and more.
Tech: Python, Pandas, Matplotlib, Seaborn, Jupyter Notebook
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Comprehensive analysis of Flipkart mobile listings including brand performance, price segmentation, rating trends, and key features like memory, storage, and color. Insights visualized using Python libraries.
Tech: Python, Pandas, Matplotlib, Seaborn, Jupyter Notebook
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H&M coustomer data analysis for buying trand using python libraries. Here we have analysed the buying trand and most selling months of H&M.
Tech: Python, Pandas, Matplotlib, Seaborn, Jupyter Notebook
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