Jocelyn Carmen Dumlao
Education
The Bachelor of Science in Mathematics is a rigorous and versatile undergraduate program that equips students with advanced analytical, problem-solving, and logical reasoning skills. The curriculum focuses on understanding mathematical principles and their applications across diverse fields, fostering both theoretical knowledge and practical expertise. Key Areas of Study: - Pure Mathematics: In-depth exploration of algebra, calculus, geometry, number theory, and topology. - Applied Mathematics: Practical applications in areas like differential equations, optimization, modeling, and computational methods. - Statistics and Probability: Comprehensive study of data analysis, inferential statistics, and probabilistic models. - Mathematical Computing: Use of programming languages, mathematical software, and algorithms for solving real-world problems. - Research and Projects: Opportunities to engage in mathematical research, independent studies, and interdisciplinary projects. Skills Developed: - Advanced quantitative and analytical thinking. - Proficiency in problem-solving and mathematical modeling. - Ability to interpret and work with abstract concepts. - Strong computational and programming skills. - Effective communication of mathematical ideas and solutions.
The Data Science Professional Certificate is a 9-course program designed to teach essential data science skills using R. It covers data analysis, visualization, statistical modeling, machine learning, and practical tools like Git and R Markdown. The program includes hands-on projects to apply learned concepts to real-world problems. Course Highlights: 1. R Basics: Introduction to R programming and data manipulation. 2. Visualization: Create data visualizations using ggplot2. 3. Probability: Study random variables, distributions, and sampling. 4, Inference & Modeling: Learn hypothesis testing and regression modeling. 5. Productivity Tools: Master Git, R Markdown, and reproducible workflows. 6. Wrangling: Clean and preprocess data using dplyr and tidyr. 7. Linear Regression: Build and analyze predictive models. 8. Machine Learning: Implement supervised and unsupervised learning techniques. 9. Capstone Projects: Apply skills to solve real-world data problems. Key Skills: - Proficiency in R programming - Data visualization and wrangling - Statistical analysis and machine learning - Practical experience with real-world data
Work & Experience
As a freelance Data Scientist and Machine Learning , I offer expertise in transforming raw data into actionable insights and intelligent solutions. My services are tailored to meet diverse business needs, from data preparation to advanced model development. Key Services I Provide: 1. Data Preparation and Organization: Identify, collect, and organize datasets for analysis, ensuring data readiness for impactful decision-making. 2. Machine Learning Solutions: Design, build, and implement predictive models and machine learning algorithms to address complex problems and enhance decision-making. 3. Data Quality Assessment: Enhance data collection processes, assess data quality, and handle preprocessing tasks for cleaner, more reliable data inputs. 4. Insightful Analysis: Analyze data to uncover trends, patterns, and insights that drive business strategies and innovation. 5. Database Development: Build and manage databases to store and organize large datasets efficiently. 6. Visualizations and Reporting: Create compelling data visualizations and reports to communicate findings clearly to stakeholders. Why Choose Me? Proficient in industry-leading tools and programming languages such as Python, R, SQL, and popular ML frameworks like TensorFlow and Scikit-learn. Proven ability to transform data challenges into tailored, actionable solutions. Strong commitment to delivering high-quality work within project deadlines.