Sakethram Mamidi

As a recent graduate, I blend Business Analysis skills with a focus on using Excel, SQL, and Tableau to uncover insights. My academic journey equipped me with a solid grasp of data-driven decision-making and how to use analytical tools effectively.

I'm always learning to stay ahead of industry trends and tech, keeping my toolkit sharp. I'm skilled in Excel for crunching data, Tableau for showing it off, and Python for digging deeper into stats. I thrive in teams where I can communicate well and collaborate effectively.

I'm passionate about helping innovative companies succeed. I'm eager to use my skills in Excel, Tableau, and Python to drive efficiency and innovation. Let's connect to explore how we can work together and elevate our careers.

Cisco - Data Analytics Essentials

Cisco - Python Essentials

HackerRank – SQL(Basic, Intermediate, Advanced)

HackerRank - R(Basic)

Project Title: Real Estate Data Analysis and Visualization Dashboard

Overview: The Real Estate Data Analysis and Visualization Dashboard is a comprehensive project aimed at exploring and presenting insights from a dataset containing real estate transaction records. Leveraging Tableau, the dashboard provides a visually intuitive interface for users to explore various aspects of the real estate market, including pricing trends, property characteristics, and geographical distribution.

Dataset: The project utilizes a dataset comprising real estate transaction records, including details such as transaction ID, date, price, number of bedrooms and bathrooms, square footage of living space and lot, property grade, waterfront status, geographic coordinates, and other relevant attributes. The dataset spans multiple years and covers properties located within a specific region (e.g., King County, Washington)

Technical Details:

• Tool Used: Tableau Desktop
• Data Preparation: The dataset was imported into Tableau using the built-in connectors. Initial data exploration and cleaning were performed within Tableau to handle missing values, outliers, and data formatting issues.
• Visualization Components: Line Chart: Displays the average house price over time, providing insights into pricing trends.
• Map Visualization: Utilizes geographic coordinates to visualize the distribution of property prices across the region, with darker shades indicating higher average prices.
• Histograms: Showcase the distributions of house prices, bedrooms, and bathrooms, allowing users to understand the frequency of different property characteristics.
• Heat Map: Presents the relationship between property view and condition, providing a comprehensive view of property quality.
• Calendar Widget: Acts as a time-based filter, allowing users to select specific months and dates to analyze real estate data dynamically.
• Additional Filters: Year built, square footage of living space, and square footage of the lot serve as additional filters, enabling users to narrow down their analysis based on specific criteria.

Project Workflow:

Data Exploration: The dataset was thoroughly explored to understand its structure, features, and potential insights.

Outcome and Impact: The Real Estate Data Analysis and Visualization Dashboard provides a powerful tool for stakeholders in the real estate industry, including analysts, agents, and investors, to gain valuable insights into market trends, property characteristics, and investment opportunities. By enabling dynamic exploration of real estate data, the dashboard facilitates informed decision-making and strategic planning.