Programming Languages
| Programming Language | Proficiency | Importance |
Python | 9/10 | Python now permeates practically every area of my work and has become a crucial component of my learning to code. It has developed into my primary language of choice, merging naturally with my work across a variety of fields. Python has repeatedly demonstrated its adaptability and effectiveness in a variety of fields, including data analysis, machine learning, web development, and automation. |
SQL | 8/10 | SQL plays a vital role in my experience as it enables seamless data management and retrieval. Its powerful querying capabilities allow me to extract meaningful insights, perform data analysis, and ensure efficient database operations. SQL empowers me to work with structured data effectively, making it a fundamental skill in my toolkit. |
MATLAB | 7/10 | In my experience, MATLAB has been crucial since it offers a complete platform for numerical analysis, method creation, and simulation. I’ve been able to take on challenging mathematical issues, improve algorithms, and draw insightful conclusions from data because to its vast function library, simple syntax, and visualization features. |
R | 7/10 | R serves as a powerful statistical programming language and environment. Its vast collection of packages, rich visualization capabilities, and robust data manipulation tools have allowed me to conduct sophisticated data analysis, create compelling visualizations, and build predictive models with ease and accuracy. |
C++ | 7/10 | I developed a thorough understanding of C++ during my bachelor’s degree, which strengthened my base in programming. I can use the language’s capabilities, like as object-oriented programming and memory management, if I have a firm grasp of it, to create dependable applications that can cater to a variety of computing requirements. |
| Methodology | SDLC, Agile, Waterfall |
| Packages | ggplot2, Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, Keras, Spark, Seaborn, Matplotlib, SciPy |
| Machine Learning | Linear Regression, Logistic Regression, Decision Tree Classifier, Decision Tree Regressor, KNN, Na¨ıve Bayes, Support Vector Machine (SVM), K -Means Clustering, Agglomerative Clustering, and Deep Learning. |
| Platforms | Tableau, HEX, Linux, Windows, Arduino, Raspberry, D-wave, Kaggle, AWS |
| Database | MySQL, SQL Server, MongoDB |
Packages
| Package | Proficiency | Importance |
Pandas | 9/10 | Pandas has played a crucial role in my experience, providing a versatile and efficient toolkit for data manipulation, analysis, and transformation. Its robust data structures and intuitive methods have empowered me to handle large datasets effectively, enabling informed decision-making and extracting valuable insights from complex data. |
NumPy | 9/10 | Numpy provides key instruments for numerical operations and scientific computing. My ability to handle and manipulate enormous datasets, carry out intricate computations, and organize my workflow in numerous data-driven projects has been made possible by its effective array operations, mathematical functions, and linear algebra skills. |
Matplotlib | 9/10 | The matplotlib library has played a significant role in my projects, particularly in data visualization. In the Land Use Classification project, I used matplotlib to create informative plots and visualizations that aided in understanding the classification results and the distribution of land usage. Additionally, in the project of Visualizing the Feature Trends of TOP500 Supercomputers, matplotlib allowed me to generate insightful graphs and charts to illustrate the trends and key traits in the November 2022 Top500 list and the HPCG list. Its flexibility and extensive plotting capabilities have been instrumental in effectively communicating complex data patterns and trends. |
TensorFlow | 8/10 | The creation of a reliable and effective model was made possible by the potent neural network architecture of TensorFlow. Additionally, TensorFlow was crucial in the development and testing of the machine learning model used in the Coupon Recommendation System to determine whether customers will accept or reject suggested coupons. Its adaptable structure and optimization abilities helped to produce precise and trustworthy predictions. |
Seaborn | 7/10 | Seaborn’s high-level interface and aesthetic options have been instrumental in effectively conveying complex data relationships and patterns. |
Sanjay Govindaraju 