Projects

As a data scientist, I achieve data governance by establishing transparent data management processes, adhering to data privacy and security regulations, implementing data quality controls, documenting data sources and transformations, promoting data literacy across the organization, and collaborating with stakeholders to ensure ethical and responsible use of data.

I approach problem-solving by first understanding the problem domain and defining clear objectives. I gather relevant data, explore and preprocess it, apply appropriate models and algorithms, and evaluate results. Iterative refinement, effective communication, and continuous learning are integral to my problem-solving approach.

Effective communication is vital for data scientists as it facilitates collaboration, comprehension, and decision-making. I ensure best practices by actively listening, asking clarifying questions, and presenting complex findings in a clear, concise manner. I also foster open communication channels, encouraging feedback and promoting transparency within the team.

Twitter is a valuable source for trend updates. I used the R package (rtweet) to gather live tweets about the Russian-Ukraine conflict, analyzing sentiments in the latest 10,000 using 'Russia' and 'Ukraine' as keywords. After cleaning the data, I analyzed unigram sentiment to find positive and negative words. I also assessed President Biden's and Putin's sentiments, analyzing their recent 10,000 tweets while keeping text manipulation codes intact.

I extensively utilized SQL in past projects to proficiently manage and manipulate data in relational databases. One instance involved applying SQL in a CRM system to create a well-structured database, enabling efficient data retrieval, updates, sales tracking, and report generation. This approach ensured the smooth handling of large datasets, preserving integrity and performance. SQL enabled complex operations like dynamic reports, filtering, sorting, and searches in a web app. It also aided data analysis, extracting insights from diverse sources for informed solutions.

Tableau is an essential tool for data scientists, aiding in data visualization and analysis. It helps convert complex data into clear visualizations, effectively allowing patterns and insights to be discovered. I have used Tableau for data storytelling, making findings understandable for various audiences. This platform supports exploring large datasets, creating interactive dashboards, and robust analytics. I have utilized Tableau to build impactful dashboards showcased in my profile, influencing informed business choices.

I extensively employed Looker Studio for data exploration and visualization in my projects, creating dynamic dashboards for comprehensive data insights. Its intuitive interface allowed tailored reports, and Looker's modelling capability turned raw data into meaningful metrics, enabling informed decisions. The collaboration tools also facilitated seamless insight sharing among team members, enhancing data exploration, analysis, and project communication.

As a data scientist, Power BI has been essential for extracting valuable insights from intricate data. Its adaptable capabilities allowed me to connect to various data sources, ensuring smooth integration and carrying out data cleaning and transformation for improved quality. Using Power BI's strong visualization tools, I've crafted engaging, interactive dashboards that communicate data stories to stakeholders, empowering them to decide confidently. Its analytical features, like DAX formulas and advanced filtering, aided in revealing patterns, making it vital in shaping my data-driven strategies.

R Shiny is a powerful data science tool for developing interactive web applications. By integrating R code and visualizations, I created user-friendly interfaces allowing stakeholders to explore and interact with data insights. R Shiny enables real-time updates, customized dashboards, and dynamic visualizations, enhancing collaboration and decision-making. It facilitates the transformation of complex analyses into accessible applications, making data science projects more engaging and actionable for diverse audiences.