Data Manipulation: Expertise in SQL, Python, and R for data querying and manipulation.

Data Visualization: Skilled with tools like Tableau, Power BI, and Matplotlib for creating insightful visualizations.

Databases: Proficient with relational databases like MySQL, PostgreSQL, and NoSQL databases like MongoDB.

Machine Learning: Familiarity with ML frameworks and libraries like scikit-learn and TensorFlow.

Programming Skills: Mastery in Python, R, and SQL for data analysis and modeling tasks.

Machine Learning: Proficient with ML libraries like scikit-learn, TensorFlow, and Keras for predictive modeling and deep learning.

Statistical Analysis: Expertise in hypothesis testing, regression, and other statistical methods using tools like SAS and SPSS.

Data Visualization: Skilled in creating compelling visuals using tools like Matplotlib, Seaborn, and Tableau.

Operating Systems: Proficient in managing and troubleshooting Windows Server, Linux distributions, and UNIX systems.

Networking: Skilled in configuring and maintaining protocols like TCP/IP, DHCP, DNS, and VPNs.

Virtualization: Expertise in virtualization technologies such as VMware, Hyper-V, and KVM.

Cloud Management: Familiarity with cloud platforms like AWS, Azure, and Google Cloud for infrastructure management.

Hardware Diagnostics: Proficient in identifying and resolving hardware issues in PCs, laptops, and related peripherals.

Operating Systems: Skilled in installing, configuring, and troubleshooting Windows, macOS, and Linux systems.

Software Testing: Experience in manual testing of applications for functionality, usability, and performance.

Test Automation: Familiarity with automated testing tools like Selenium, JUnit, and TestNG.

Bug Tracking: Proficient with bug tracking tools like JIRA, Bugzilla, and Mantis.