Margaret Kagwiria Kathurima
About Me
Hello! I’m a data analyst with proficiency in SQL, R, Python, Microsoft Office, VBA Excel, Tableau, Power BI, and Big Query. I have accumulated 2 years of valuable experience in the field of data analysis. While my academic background is in mechatronics engineering, my career has taken a different path towards the exciting world of data analysis.
My journey into data analysis began after completing my mechatronics engineering degree when I realized my passion for working with data and extracting insights from it. I started by taking online courses and attending workshops to build my skills in SQL, Python, and data visualization tools like Tableau and Power BI. I also honed my analytical and problem-solving abilities, which are essential for this field.
During my two years of professional experience, I’ve had the opportunity to work on various projects that involved collecting, cleaning, and analyzing data to provide actionable insights for decision-makers. Whether it’s creating complex SQL queries to extract specific data from databases, using Python for data manipulation and statistical analysis, or designing interactive dashboards in Tableau and Power BI, I’ve enjoyed every aspect of my work.
As for my aspirations, I am committed to furthering my career in data analysis. I aspire to continue learning and staying updated with the latest developments in data science and analytics. I also plan to pursue relevant certifications to enhance my skills and credibility in the field. Ultimately, I aim to take on more challenging and impactful roles in data analysis, working on projects that drive business growth and innovation.
Education
Dedan Kimathi University of Technology
A Bachelor of Science (B.Sc.) in Mechatronics Engineering is an interdisciplinary engineering degree program that combines elements of mechanical engineering, electronics, computer science, and control engineering. Mechatronics engineers are trained to design, develop, and maintain complex systems that integrate mechanical components, electronics, and software to create intelligent and automated systems.
Coursera
This certificate program is designed to provide individuals with the skills and knowledge needed to start a career in data analytics. The program focuses on teaching practical skills and tools used in data analysis, including but not limited to: Data cleaning and transformation Data visualization using tools like Google Data Studio Basic statistical analysis Creating and managing databases Basic programming for data analysis, often using tools like Python and SQL Introduction to machine learning concepts Developing data-driven insights and recommendations
IBM
Python is a versatile and widely-used programming language for data science. It is known for its simplicity, readability, and an extensive ecosystem of libraries and tools that make it a top choice for data analysis and machine learning tasks. Here's a brief description of Python for data science: 1. **Versatile Language**: Python is a general-purpose programming language that can be easily adapted for various data science tasks, from data manipulation and analysis to building machine learning models. 2. **Rich Ecosystem**: Python boasts a rich ecosystem of data science libraries, including NumPy, pandas, Matplotlib, Seaborn, and scikit-learn, which simplify data manipulation, visualization, and machine learning. 3. **Data Handling**: Pandas is a popular library for data manipulation, providing data structures like DataFrames for efficient handling and analysis of structured data. 4. **Visualization**: Matplotlib and Seaborn are commonly used for data visualization, allowing data scientists to create informative plots and charts. 5. **Machine Learning**: scikit-learn is a comprehensive machine learning library that offers tools for classification, regression, clustering, and more. It integrates well with other Python libraries. 6. **Community Support**: Python has a vast and active community of data scientists, programmers, and researchers who contribute to its growth. This community provides ample resources for learning and problem-solving. 7. **Flexibility**: Python can be integrated with other languages like C and C++ for performance-critical tasks, and it can be used in a wide range of environments, including Jupyter notebooks and IDEs like PyCharm. 8. **Open Source**: Python is open source, which means it's freely available and constantly improved by a global community of developers. 9. **Data Science Libraries**: Python offers specialized libraries for data scraping (Beautiful Soup, Scrapy), natural language processing (NLTK, spaCy), and deep learning (TensorFlow, PyTorch). 10. **Data Science Projects**: Python is used in a variety of data science projects, including data analysis, predictive modeling, recommendation systems, image recognition, and more. In summary, Python is a versatile, accessible, and powerful programming language that has become the backbone of data science. Its simplicity, extensive libraries, and strong community support make it an ideal choice for data scientists and analysts working on a wide range of data-related tasks.
FreeCodeCamp
Scientific computing with Python refers to the application of the Python programming language and its associated libraries and tools to solve complex scientific and engineering problems. Here's a brief description of scientific computing with Python: 1. **Numerical Computing**: Python, with libraries like NumPy and SciPy, provides a robust environment for numerical computation. Scientists and engineers use these libraries to perform tasks such as linear algebra, optimization, interpolation, and numerical integration. 2. **Data Analysis**: Python's pandas library is widely used for data manipulation and analysis. Scientists can efficiently explore, clean, and process large datasets, making it suitable for fields like data science and statistics. 3. **Data Visualization**: Python offers versatile data visualization libraries like Matplotlib and Seaborn, enabling researchers to create informative plots and charts to visualize their data and present their findings effectively. 4. **Symbolic Mathematics**: With libraries like SymPy, Python allows for symbolic mathematics. Researchers can perform algebraic manipulations, calculus, and solve equations symbolically, which is valuable in theoretical physics and engineering. 5. **Simulation and Modeling**: Python supports scientific simulations and modeling using libraries like SciPy. Researchers can create computational models to simulate physical phenomena, run experiments, and analyze results. 6. **Machine Learning**: Python has become a go-to language for machine learning and artificial intelligence research. Libraries like scikit-learn, TensorFlow, and PyTorch are extensively used to develop and apply machine learning models to scientific problems. 7. **High-Performance Computing**: Python can be integrated with languages like C and Fortran for computationally intensive tasks, allowing researchers to achieve high performance when necessary. 8. **Scientific Libraries**: Python has a rich ecosystem of specialized scientific libraries for specific fields such as astronomy (Astropy), biology (Biopython), and chemistry (RDKit). 9. **Interactivity and Visualization**: Python's Jupyter notebooks provide an interactive environment where researchers can combine code, data, and visualizations in a single document, facilitating collaboration and documentation of scientific workflows. 10. **Open Source and Community**: Python is open source and benefits from an active and supportive community. Researchers can access a wealth of resources, tutorials, and community-contributed packages to aid their scientific computing endeavors. In summary, scientific computing with Python offers a versatile and accessible platform for scientists and engineers to tackle complex problems in various domains, from physics and biology to data analysis and machine learning. Python's simplicity, extensive libraries, and strong community make it a preferred choice for scientific research and computational tasks.
Work & Experience
Machine Operations Analysis Intern
Devki Group of Companies
A Machine Operations Analysis Intern typically assists in optimizing and improving the efficiency of machine operations within a manufacturing or production environment. Here are the brief duties associated with this role: Data Collection: Gather data on machine performance, including metrics like uptime, downtime, cycle times, and error rates. Data Analysis: Analyze collected data to identify trends, patterns, and areas for improvement in machine operations. Process Evaluation: Evaluate existing machine processes and workflows to identify bottlenecks or inefficiencies. Recommendations: Collaborate with team members to propose recommendations for process improvements, which may include changes in machine settings, maintenance schedules, or operator training. Testing and Validation: Assist in testing proposed process changes and monitor their impact on machine performance. Documentation: Maintain records of machine operations data, analysis results, and any changes implemented. Collaboration: Work closely with machine operators, engineers, and supervisors to gather insights and feedback on machine operations. Reporting: Prepare reports and presentations summarizing findings and recommendations for management and stakeholders. Continuous Learning: Stay updated with industry best practices and emerging technologies in machine operations and process optimization. Safety and Compliance: Ensure that all recommendations and changes adhere to safety regulations and industry standards. Overall, a Machine Operations Analysis Intern plays a vital role in helping organizations enhance the efficiency and reliability of their machinery, contributing to increased productivity and cost savings.
Junior Data Analyst
KPMG East Africa
A Junior Data Analyst at a fintech (financial technology) company plays a crucial role in leveraging data to inform decision-making and improve financial services. Here are the brief duties associated with this role: 1. **Data Collection and Cleaning**: Gather financial data from various sources, ensuring accuracy and completeness. Clean and preprocess data to make it suitable for analysis. 2. **Data Analysis**: Perform data analysis using tools like SQL, Python, or R to extract insights, detect patterns, and identify trends in financial data. 3. **Report Generation**: Create regular reports and dashboards to present findings, key performance indicators (KPIs), and financial metrics to stakeholders and management. 4. **Risk Assessment**: Analyze credit risk, market risk, or fraud risk by evaluating historical data and applying statistical models. 5. **Financial Modeling**: Build and maintain financial models to assess investments, pricing strategies, or financial projections. 6. **Compliance and Regulation**: Ensure that all data analysis and reporting adhere to regulatory requirements and compliance standards specific to the financial industry. 7. **Client Insights**: Analyze customer behavior and financial transactions to gain insights that can improve customer experience and inform marketing strategies. 8. **Data Visualization**: Use tools like Tableau or Power BI to create visually appealing and informative data visualizations and dashboards. 9. **Collaboration**: Collaborate with cross-functional teams, including developers, product managers, and compliance officers, to implement data-driven solutions and strategies. 10. **Continuous Learning**: Stay updated with industry trends, financial market dynamics, and data analysis techniques to enhance skills and knowledge. 11. **Security**: Ensure the security and confidentiality of financial data and comply with data protection regulations. In summary, a Junior Data Analyst in a fintech company plays a critical role in analyzing financial data, supporting risk assessment, improving decision-making processes, and contributing to the company's overall success in delivering innovative financial services while maintaining regulatory compliance.
