RISHITHA KOSURI

Self-starter programmer analyst trainee with real-world experience in developing, maintaining, and improving software. Expert in analyzing programming and system issues, making informed decisions, and delivering effective solutions, and had an understanding of numerous programming languages, database development, quality assurance standards, and testing.Talented in analyzing business procedures and developing compatible solutions to support daily technology needs.

• Microsoft Certified: Azure Fundamentals
• Microsoft Certified: Azure Data Fundamentals
• Introduction to AWS for Non-Engineers: 1 Cloud Concepts
• CSS Essential Training
• HTML Essential Training
• Responsive Web Design
• Learn API Technical Writing: JSON and XML for Writers
• SQL for Beginners: Learn SQL using MySQL and Database Design
• Python for beginners: Learn python programming (Python3)

• Https://rishitha.ccbp.tech
• Https://rishithakosuri3.ccbp.tech/

• Project Title: COVID-19 Data Visualization Using Python

     Objective: Analyzing and visualizing COVID-19 data to gain insights into the pandemic.

     Tools and Technologies: Python,Pandas,Matplotlib,Seaborn, and Jupyter Notebooks.

     Data Visualization Techniques: Time series plots of COVID-19 cases over different periods.

     Insights and Analysis: Trends in the number of cases over time.

• Project Title: Web Scraping for Data Extraction

     Objective: Extracting specific information from websites for analysis.

     Tools and Technologies: Python,Pandas.

     Data Extracted: Product details: name, price, ratings from e-commerce websites.

     Data Analysis: Trends in pricing or availability of products.

• Project Title: Data Cleaning and Analysis of Employee Records

     Objective: Clean and preprocess a dataset containing employee records to ensure data quality and prepare it for further analysis.

     Dataset: Dataset with columns like 'EmployeeID', 'FirstName', 'LastName', 'Salary', 'JoiningDate', 'Department', and 'Location'

     Tools and Technologies: Python,Pandas

     Data Cleaning Techniques: Handle missing values in the dataset, if any. Address outliers in the 'Salary' column. Standardize the 'JoiningDate' format, remove duplicates from the dataset, and encode categorical variables like 'Department' and 'Location'.

     Data Preprocessing: Create a new column for 'Years of Service' derived from 'JoiningDate'.Standardize salary values or categorize them into salary bands. Explore and visualize basic statistics about the dataset.