Venkata Sai Kishore Putta

An adaptable and analytical professional with a Bachelor's degree in Mechanical Engineering, transitioning into the field of data science. Equipped with a solid foundation in engineering principles, I am now eager to leverage my quantitative skills and problem-solving abilities to excel in the data science domain. Through self-study and relevant coursework, I have developed proficiency in data analysis and programming languages such as Python and SQL.

• Programming, Data Structures and Algorithms Using Python - Swayam Central, NPTEL - https://drive.google.com/file/d/1rK19iGTa0XtJ38IAx0kfIKHH3kwfgoXA/view?usp=sharing
• Python (Basic) - HackerRank - https://drive.google.com/file/d/1kk2GQnfdsv-EBVAyFueEgGdTmuH1SQgr/view?usp=sharing
• SQL (Intermediate) - HackerRank https://drive.google.com/file/d/1HAjauQ2HGfP2zgXJieT_DaNGpHCY18aG/view?usp=sharing

Course Name: Data Science and Artificial Intelligence

• Currently, I am actively engaged in coursework focused on Data Science and Artificial Intelligence through the online platform Intellipaat.
• This course focuses on SQL, Python and its libraries, Machine Learning, Artificial Intelligence, Data Visualization using Power BI and also on some mathematical concepts such as linear algebra, statistics basics and probability

Title: Design and Analysis of Maglev vertical Axis Wind Turbine

Tools Used: Jupyter Notebook, SolidWorks, and Ansys Workbench

Description:

• In this project, Our aim is to decrease the mechanical noise and friction due to the mechanical contact between two major parts of assembly to increase mechanical efficiency of wind turbine.
• After gathering demagnetization data from multiple sources, including Google, we analyzed it using Seaborn library by correlating the demagnetization power of different magnetic materials with their weight-bearing capacity over extended periods. Our analysis revealed that neodymium magnets exhibit the most desirable attributes for our research project.
• Before initiating this project, we conducted thorough research on traditional Vertical Axis Wind Turbines (VAWT). Using the Matplotlib library, we analyzed the relationship between friction and efficiency across different turbine models, gaining valuable insights into friction's impact on overall turbine performance.
• After visualizing data related to traditional VAWT, we transitioned to practical implementation by designing a Maglev VAWT using SolidWorks. This entailed creating detailed parts and assembling them to form a comprehensive 3D CAD model. Subsequently, we analyzed the blade structure by integrating the 3D CAD model into ANSYS Workbench software and utilizing its diverse analysis tools.