Hello, I'm Vivian Makmur, and I bring a distinctive blend of entrepreneurial flair and analytical prowess to the realm of data analysis. Through hands-on experience running my own online shop and pursuing a career in interior design, coupled with formal education in Full-stack Data Analytics, I've cultivated a strong skill set in SQL, Python, and Tableau. My track record speaks for itself; whether it's optimizing sales dashboards for Telkom UMKM or diving into customer churn analysis for credit card companies, I thrive on turning complex data into actionable insights. With a passion for driving growth and empowering businesses with data-driven decisions, I'm excited to continue making a tangible impact in the field of data analysis.

Step 1: Define Objectives and Scope

Begin by understanding the project requirements, goals, and desired outcomes with stakeholders.

Clarify the scope of the analysis, including the datasets available, timeframe, and specific questions to be answered.

Step 2: Data Collection and Preparation

Gather relevant data from various sources, ensuring its quality, completeness, and compatibility for analysis.

Cleanse and preprocess the data to address missing values, outliers, and inconsistencies, ensuring data integrity.

Step 3: Exploratory Data Analysis (EDA)

Conduct EDA to understand the structure, patterns, and relationships within the dataset.

Utilize statistical methods, visualization techniques, and tools like Python, SQL, and Tableau to uncover insights and trends.

Step 4: Data Modeling and Analysis

Apply appropriate statistical and machine learning techniques to model the data and extract meaningful insights.

Evaluate model performance, interpret results, and derive actionable recommendations to address the project objectives.

Step 5: Visualization and Reporting

Develop clear and compelling visualizations using tools like Tableau or matplotlib to communicate findings effectively.

Prepare comprehensive reports and presentations summarizing key insights, methodologies, and recommendations for stakeholders.

Step 6: Iteration and Feedback

Collaborate with stakeholders to gather feedback on the analysis and recommendations.

Iterate on the analysis as needed based on feedback, refining models, visualizations, and insights to ensure alignment with objectives.

Step 7: Implementation and Monitoring

Support the implementation of recommended strategies or solutions, monitoring outcomes and adjusting as necessary.

Continuously monitor data quality, performance metrics, and business impact to refine analysis and drive continuous improvement.