Hello! I'm a highly motivated and detail-oriented aspiring Data Analyst eager to apply my foundational skills to real-world data challenges. While I'm at the beginning of my professional journey, I bring a strong analytical mindset, a passion for uncovering insights from data, and a commitment to continuous learning.

My core competencies include:

• Data Cleaning & Preparation: Proficient in transforming raw data into clean, usable formats using Excel and Python (Pandas).

• Data Analysis: Skilled in performing exploratory data analysis (EDA), identifying trends, and deriving meaningful conclusions.

• Data Visualization: Capable of creating clear and impactful dashboards and reports using tools like Tableau (or Power BI).

• Database Querying: Solid understanding of SQL for extracting and manipulating data from relational databases.

I am dedicated to delivering accurate, insightful, and actionable results. I am looking for opportunities to contribute to projects where I can grow my expertise, learn from experienced professionals, and help businesses make informed decisions. I am a quick learner, excellent communicator, and always ready to take on new challenges.

Let's connect and discuss how I can help bring clarity to your data!

Skills:

Technical Skills:

• Programming Languages: Python (Pandas, NumPy, Matplotlib, Seaborn)

• Databases: SQL (MySQL, PostgreSQL - basic to intermediate)

• Spreadsheets: Microsoft Excel (Advanced: Pivot Tables, VLOOKUP, Conditional Formatting, Data Validation)

• Data Visualization: Tableau (or Power BI, Google Data Studio)

• Statistical Analysis: Descriptive Statistics, Basic Inferential Statistics

• Other Tools: Google Sheets, Jupyter Notebooks

Soft Skills:

• Analytical Thinking

• Problem-Solving

• Attention to Detail

• Communication (Written & Verbal)

• Curiosity

• Adaptability

• Time Management

Education:

• [Your Degree/Certification Name] | [Your University/Institution Name] | [Year of Graduation/Completion]

  • Relevant Coursework: Data Structures, Statistics, Database Management, Introduction to Programming, Business Analytics.

• [Any relevant online courses or bootcamps, e.g., Google Data Analytics Professional Certificate, IBM Data Analyst Professional Certificate] | [Platform Name] | [Year of Completion]

Experience:

(Since you're a beginner, focus on projects, coursework, or any volunteer/internship experience. If you have no formal experience, emphasize your projects.)

[Project Title 1] | [Your Role, e.g., Data Analyst, Project Contributor] | [Month, Year] - [Month, Year]

• Description: Briefly describe the project's goal and the dataset used.

• Responsibilities: Detail your specific tasks (e.g., "Cleaned and preprocessed customer sales data using Pandas," "Developed SQL queries to extract product performance metrics," "Created interactive dashboards in Tableau to visualize sales trends").

• Achievements/Results: Quantify impact if possible (e.g., "Identified a 15% increase in customer churn due to X factor," "Presented findings that led to a new marketing strategy").

[Project Title 2] | [Your Role] | [Month, Year] - [Month, Year]

• Description: (Similar to above)

• Responsibilities: (Similar to above)

• Achievements/Results: (Similar to above)

(If you have any academic projects, personal projects, or Kaggle competitions, list them here. If you've done any volunteer work involving data, include that too.)

Portfolio:

(Crucial for beginners! Provide links to your work.)

• GitHub Repository: [Link to your GitHub profile or specific project repositories]

  • Include Jupyter Notebooks, SQL scripts, and any code showcasing your analysis.

• Tableau Public Profile: [Link to your Tableau Public profile if you have dashboards]

• [Link to a personal website/blog if you have one, showcasing projects or case studies]

Hourly Rate:

(For beginners, it's often recommended to start with a competitive but lower rate to gain initial clients and reviews. You can always increase it later.)

$15 - $25/hour (Negotiable based on project scope and complexity)

Availability:

• Full-time / Part-time / Project-based

• Flexible hours, ready to accommodate different time zones.