About me:
Communicating Data Insights Using SQL,Python & MS Excel (PowerQuery)
Data analyst with a knack for bridging the gap between data and the world. Offers a decade of experience in healthcare, project management, and marketing, excelling in translating complex data into actionable insights. Strategy-driven with hands-on experiences improving operational workflows and organizational growth. Constantly expands his analytical toolbox, keen to leverage his data analytics skills and an engaged team-player.
Problem Solving | Critical Thinking | Strong Communication & Presentation Skills
Technical Skills
Excel | Tableau | Power BI | SQL | Python
Work Experiences
Project Management (9+ years)
Data Visualization (2+ years)
Report Development (2+ years)
Online Marketing (2 years)
Featured Projects
MICROSOFT EXCEL
Customer Retention Analysis
The project aimed to boost customer retention in banking by identifying churn signals.
PYTHON | TABLEAU
Real estate transaction analysis
Analyzing global real estate transactions for market trends, price drivers, and regional differences, focusing on property details and broader factors for comprehensive insights.
CAREERFOUNDRY
Collection of real-data analysis projects
These projects span a variety of industries and incorporate the use of multiple tools and data analytics techniques.
Professional Certifications
CareerFoundry - Data Analytics Program
Issued 2024
Udemy - Microsoft PowerBI & Excel PowerQuery
Issued 2024
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Embracing the Future of
Data Analysis
My Journey into the World of Data-driven Decisions
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Unveiling the Hidden Marvels
Navigating the Intricate Pathway to Insights and Impact
Testimonials
"I've had the pleasure of working with this exceptional data analyst who consistently delivers high-quality results. His keen insights and dedication to his craft have been instrumental in driving our business forward. I highly recommend his services to anyone seeking data-driven solutions.Marko Londa, Founder & CEO of Moonshot Pirates
Customer Retention Analysis
Using Microsoft Excel
The project focused on bolstering customer retention rates in a bank by identifying early signs of customer churn. The analysis, which involved compiling a table of potential client attributes and conducting in-depth analysis using Excel's pivot table functionality, aimed to predict the probability of customer churn. The project resulted in a decision tree model that highlighted the top risk factors contributing to client attrition.Applied Techniques:
1) MS Excel Pivot Tables
2) Predictive Analysis
3) Probability & Forecasting
4) Desicion Tree VisualizationKey Business Question:
>> What are the key risk-factors in identifying customers who are most likely to churn?
I took the following steps to create my analysis:1) Data Preparation
The initial data preparation phase involved compiling a table of potential client attributes like age and estimated salary, which could indicate a customer's likelihood to leave the bank. The data was managed in Excel for easy manipulation. The raw project data set was cleaned, variables merged & manipulated to fit the project purpose.2) Data Analysis
The data analysis involved scrutinizing attributes using Excel's pivot tables, revealing patterns within the data,contributing to our understanding of the key factors associated with customer churn. This was followed by probability calculations to predict churn likelihood.3) Results
The project resulted in a decision tree model identifying the top risk factors contributing to client attrition. This model provided valuable insights for designing proactive strategies to retain customers, thereby enhancing customer satisfaction and increasing the bank's revenue.4) Limitations
The data analysis project faced constraints, including limitations of the dataset that may not have captured all factors contributing to customer churn, and challenges in handling large datasets and conducting complex statistical analyses due to the use of Excel as the primary tool.
Key Takeaways:
1) Inactive customers are more likely to leave, so we should increase engagement with attractive rewards.
2) Diversifying and promoting our products can improve customer retention.
3) Regular surveys and interviews can help us understand and anticipate customer needs.
Global Real Estate Market Transactions Analysis
Using Python for Data Wrangling, Tableau for Data Visualization
Project Scope:The project aimed to analyze the global development of the real estate market over time and the factors influencing a building's price, using a dataset from kaggle.com. The dataset provides detailed global real estate transaction information, including property specifics and pricing. It offers opportunities in understanding real estate trends, forecasting, and identifying investment opportunities.Data Setup:The dataset initially contained around 150k transaction records. After applying Python data preprocessing and cleaning techniques, around 15k transaction records from over 25 countries were left for analysis. This includes transactions from the early 19th century until 2024.Applied Techniques:1. Python for Data Preprocessing, Data Cleaning & Data Wrangling
2. Tableau Public for Data VisualizationHypothesis:The study hypothesized that the year of construction and factors related to size, would be influential factors in pricing, with newer and larger apartments typically priced higher.Data Preparation:The data preparation phase involved scrutinizing attributes using Python's data wrangling techniques, revealing patterns within the data and contributing to our understanding of the key factors associated with property values.Data Analysis:The analysis of the global real estate dataset yielded preliminary findings. A correlation was observed between the total area of an apartment and its price. Variations in property prices and apartment sizes across different countries were noted, potentially reflecting cultural preferences or typical living conditions.Results:The study found that property prices vary significantly across countries due to architectural features, demand, and real estate market conditions. More bedrooms and bathrooms generally led to higher prices, and the sizes of apartments have changed over time, potentially influenced by changing living standards or construction practices.Limitations:While these findings provide a broad overview of the factors influencing real estate prices and characteristics on a global scale, there are limitations to the study. The dataset lacked "external" factors like geographical location or proximity to amenities, which could provide a more comprehensive understanding of property prices.Key Takeaways and Outline:- Property prices vary significantly across countries due to factors such as architectural features, demand, and real estate market conditions.
- Larger apartments generally command higher prices.
- The year of construction and the number of rooms in an apartment are influential in pricing.
- More bedrooms and bathrooms generally lead to higher prices.
see the whole Tableau Public Storyboard here
As next steps, further analysis is proposed to refine these observations and uncover more nuanced insights. Exploring additional datasets that incorporate external factors could provide a more holistic perspective on global real estate prices. Furthermore, it would be beneficial to perform a more detailed analysis of the trends towards larger, pricier properties in certain locations based on future construction dates.Analyzing global real estate transactions to understand market trends, price factors, and investment opportunities. The study focused on property specifics like construction year and apartment size while acknowledging the need for broader factors for deeper insights.
Embracing the Future of
Data Analysis
My Journey into the World of Data-driven Decisions
As a prospective data analyst, I recently made a significant career transition into the field of data analysis. With a diverse background in quality and risk management, online marketing, and project management in cybersecurity research projects, I embarked on this new path driven by my passion for exploring data and making data-driven decisions.In my previous roles, I witnessed firsthand the power of data in driving business success and informing strategic decisions. It became clear to me that data analysis is not just a valuable tool, but a key driver of innovation and growth in today's digital landscape. This realization ignited a curiosity within me, pushing me to delve deeper into the world of data analysis and uncover the untapped potential it holds.Throughout my journey, I have actively pursued opportunities to build projects that enhance my skills and knowledge in data analysis. By immersing myself specifically in the finance, health, sharing economy, and real estate industries, I have gained valuable insights into the unique challenges and opportunities they present. These industries, with their complex datasets and dynamic landscapes, have allowed me to apply my analytical skills and contribute to meaningful solutions.What truly fascinates me about the data analyst field is the promising future it holds. With advancements in artificial intelligence and machine learning, we are on the brink of a data revolution. The potential for AI to transform industries immense.
It is not just about uncovering hidden patterns and insights; it is about leveraging these findings to drive innovation, streamline processes, and shape the future of these industries.
I am excited to be a part of this evolving landscape and contribute to the exciting advancements that AI will bring. The prospect of harnessing the power of data to solve complex problems and make informed decisions is what drives me. As a data analyst, I am committed to continuously expanding my skill set, staying up-to-date with the latest technologies, and embracing new challenges.
In addition to my passion for data exploration and manipulation with SQL, I thoroughly enjoy the art of storytelling and visualizing data through tools such as Tableau. Being able to present data insights in a visually appealing and easily understandable manner is a skill that I have honed and continue to develop. I believe that effective data visualization enhances the impact of data analysis and facilitates informed decision-making.Furthermore, I am actively involved in building my own projects to further enhance my skills and knowledge in the field. By undertaking data analysis projects in various domains, I am able to apply my analytical skills to real-world scenarios and contribute to meaningful solutions. These projects not only serve as a testament to my ability to tackle complex data challenges, but also showcase my dedication and commitment to continuous learning and growth.The finance, health, sharing economy, and real estate industries are of particular interest to me due to their immense potential for data-driven insights and innovation. The vast amount of data available in these industries presents intriguing challenges and opportunities that I am eager to explore. By delving deep into these domains, I aim to contribute to the development of robust strategies, optimize operational processes, and identify growth opportunities for organizations operating within these sectors.Looking ahead, I am filled with excitement and anticipation for the future of the data analyst field. With advancements in artificial intelligence and machine learning, we are on the cusp of a new era where data-driven decision-making will drive innovation and shape industries.
The potential for AI to revolutionize whole industries is immense, and I am enthusiastic about the role I can play in leveraging data to drive positive change.
In conclusion, my journey into the data analyst field has been fueled by my passion for exploring data, making data-driven decisions, and embracing the opportunities this industry holds. I am dedicated to continuously expanding my skill set, staying abreast of emerging technologies, and collaborating with industry professionals to shape the future of data analysis. Let's connect and explore how we can drive innovation together!
Unveiling the Hidden Marvels:
Navigating the Intricate Pathway to Insights and Impact
As an enthusiastic and dedicated data analyst, I am extremely excited to delve into the intricate details of the fundamental process that drives the captivating world of data analysis. In this comprehensive article, I will take you on a journey as we explore the fascinating nuances of the data analysis pipeline. This structured approach enables us to extract invaluable insights from complex datasets, empowering us to make well-informed and impactful decisions that shape the future.By publishing articles like this one, I not only have the opportunity to share my extensive knowledge and contribute to a deeper understanding of the intricate data analysis process, but it also allows me to further refine my own skills and expertise as a prospective data analyst.
The data analysis pipeline is an intricate process that consists of six pivotal steps, each playing a crucial role in the overall success of any data analysis endeavor:
1) Requirements, Specification, and Data Collection:
At the inception of our analysis, it is essential to define the problem at hand, meticulously determine the objectives we aim to achieve, and identify the necessary data to collect. This sets the rock-solid foundation upon which the entire analysis process thrives and flourishes.2) Data Pre-processing:
Once we have collected the data, it's time to embark on the critical phase of data pre-processing. During this phase, we meticulously address various challenges that may arise within the collected data, such as noise, unclean data, unbalanced data, duplicates, or missing values. By meticulously resolving these challenges, we ensure that the data we are working with is of the highest quality, reliability, and integrity, setting the stage for accurate and meaningful analysis.3) Data Analysis:
Equipped with cleaned and structured data, we unlock the full potential of an extensive range of models, tools, and analytical techniques. This stage of the pipeline allows us to delve deep into the dataset, exploring, inferring, and deriving meaningful conclusions. Statistical analysis, regression analysis, clustering, and other data exploration techniques all come into play during this stage, enabling us to unravel hidden patterns and gain a profound understanding of the underlying data.4) Data Visualization:
As the old saying goes, "a picture is worth a thousand words." To effectively communicate our findings and insights, we skillfully transform complex data into visually captivating charts, graphs, and visualizations. This artistic step ensures that our insights are not only easily understood but also visually captivating, making them readily accessible to stakeholders. By doing so, we facilitate informed decision-making and foster a deep appreciation for the power of data.5) Storytelling and Communication:
The final step in the captivating data analysis pipeline is the art of storytelling. Armed with our comprehensive analysis results, we present our findings to stakeholders, effectively conveying the key insights and the profound implications they hold. By crafting a compelling narrative that bridges the gap between data analysis and practical decision-making, we empower stakeholders to take action and make data-driven decisions that positively shape their organizations.6) Acting on the Data and Utilizing Analysis Results:
The true value of data analysis lies in its ability to drive action and inform decision-making. Once we have derived valuable insights from the data, it is crucial to act upon them effectively. This involves translating the analysis results into actionable steps and strategies that can be implemented within the organization. By leveraging the power of data analysis, organizations can make informed decisions, optimize processes, identify growth opportunities, and drive meaningful outcomes that positively impact the bottom line.
The ever-evolving field of data analytics continues to captivate and inspire me. Its immense potential to drive innovation, uncover hidden patterns, and shape industries is nothing short of awe-inspiring. I am deeply committed to continually expanding my knowledge and honing my skills in this captivating domain. I am genuinely excited to connect with like-minded professionals who share the same passion and enthusiasm for data analysis.
In today's fast-paced and data-driven world, every company, regardless of industry, must grapple with data in some capacity. Understanding, embracing, and harnessing the power of the data analysis pipeline is becoming increasingly vital for companies seeking to not just survive but thrive in the near future.
Let us embark on this exhilarating journey together, as we explore the vast, uncharted possibilities that the world of data analytics has to offer. If you are interested in connecting and engaging in meaningful discussions and collaborative endeavors, I would be delighted to connect and contribute to the ever-expanding realm of data analysis.
Stay tuned for my upcoming articles as I delve even deeper into the exciting realm of data analysis and its transformative potential.