Focus : Economic Data Analysis, Quantitative Research, Financial Markets, Statistical Modeling, Risk Management, Predictive Analytics
Specifications : Economics and Data Analytics
Skills
Technical skills- Spreadsheet Proficiency, Analytical Thinking, Communication, Data Visualization, Statistical Analysis, Advance Excel, Python, R, Cloud computing, MS office, Excel, Power point, E-Views, Visualization Using Power BI, Presentation, Stata, Soft skills- Critical Thinking, Problem-Solving, Communication, Team Collaboration, Adaptability, Attention to Detail, Time Discipline,Languages
English, Hindi, Bengali, Chinese, Spanish,Hobbies
Painting, Learning different Languages, Sports, Reading fiction and Animation Book
Certificate
Summary
I am Ritu Kumari Shaw, currently pursuing a Master's degree in M.Sc. (Economics and Analytics), building on a solid foundation with a Bachelor's in Economics. My academic journey has equipped me with a deep understanding of economic principles and the ability to analyze and interpret data with precision. My proficiency in programming languages such as Python, R, Advanced Excel, and E-views allows me to effectively handle and analyze large datasets, identifying key insights that drive informed decision-making. As an aspiring data analyst, I bring a unique perspective that blends economic theory with practical analytical skills, enabling me to optimize outcomes and deliver high-level achievements for your company. My ability to assess economic activities and strategically maximize benefits sets me apart, making me a strong candidate for the data analyst role.
Education History
Internships
- Managed and analysed datasets in the Data Handling department, applying innovative problem-solving to address challenges.
- Demonstrated strong project management by completing tasks on time and exceeded expectations through a self-motivated approach to learning new tools and techniques, delivering high-quality results.
Projects
Analyzed community needs through surveys and focus groups, identifying five key factors affecting health and well-being; findings informed targeted wellness initiatives that engaged 200+ residents and improved participation rates by 40%.
A hybrid Machine Learning model to forecast CO2 emissions, integrating technological advancements like energy efficiency, renewable energy, and automation, achieving higher accuracy than traditional models and aiding sustainability policy formulation