Purpose
Python is a powerful, open-source programming language that is widely used in various fields such as data science, machine learning, and web development. It has a vast ecosystem of libraries and frameworks that can be used for research design, mobile data collection, mapping, and data analysis. Python provides several libraries and frameworks that can be used to collect data from various sources, organize and structure data, perform advanced data analysis and visualization, and map data.
Course Objectives
Overall, the main objective of using Python for research design, mobile data collection, mapping, and data analysis is to efficiently collect, organize, and analyze large amounts of data, and gain insights to support research and decision-making.
Target Audience
- Data scientists: Professionals who use Python to analyze and extract insights from large and complex datasets in various fields such as finance, healthcare, and retail.
- Researchers: Professionals in academic and research institutions who use Python to conduct data analysis and generate reports for publications and grant applications.
- Business analysts: Professionals who use Python to support strategic decision making and business operations in various industries by analyzing data and creating visualizations.
- Data engineers: Professionals who are responsible for designing, building, and maintaining the data infrastructure and pipelines that are used to store and process large datasets.
- IT professionals: Professionals in IT departments who use Python to automate data management tasks, enforce data governance rules, and provide data access to different stakeholders.
- Developers: Professionals who use Python to develop web scraping, web scraping and data collection scripts, as well as data analysis tools and visualization software.
 Program Outline
Topic 1. Data collection
This step involves collecting data from various sources such as surveys, interviews, social media, and web scraping using libraries like requests and BeautifulSoup.
Topic 2. Data preparation
This step involves cleaning, transforming, and consolidating large and complex datasets using libraries such as Pandas and NumPy. Tasks in this step might include removing missing or duplicate data, handling outliers, and transforming variables to make them suitable for analysis.
Topic 3. Data exploration
This step involves exploring the data to get a sense of its distribution, patterns, and relationships using libraries such as Pandas, NumPy, and Matplotlib. Tasks in this step might include generating descriptive statistics, creating visualizations, and identifying outliers and anomalies.
Topic 4. Data analysis
This step involves conducting a wide range of data analysis to extract insights and test hypotheses using libraries such as NumPy, Pandas, and Scikit-learn. Tasks in this step might include conducting inferential statistics, building predictive models, and testing for relationships between variables.
Topic 5. Data visualization
This step involves creating high-quality graphics and visualizations to help communicate the results of the analysis using libraries such as Matplotlib, Seaborn, Plotly, and Folium. Tasks in this step might include generating plots, charts, and maps to highlight key findings.
Topic 6. Report generation
This step involves generating detailed reports and summaries of the analysis for stakeholders and decision-makers using libraries such as Pandas, NumPy, and Matplotlib. Tasks in this step might include creating tables, figures, and summaries to present the key findings and conclusions.
Topic 7. Automation
This step involves automating repetitive tasks, such as data preparation and statistical analysis, to improve efficiency and reduce errors. Tasks in this step might include creating functions, scripts, or Jupyter Notebooks to automate processes.
Topic 8. Data Governance
This step involves enforcing data governance rules, such as data lineage and data quality, to ensure data integrity and compliance using libraries such as DVC or Data Version Control.