Statistics with Python

Description

This educational program is designed to teach learners the fundamental and intermediate concepts of statistical analysis, as well as how to use the Python programming language to conduct data analysis. The curriculum provides students with the theoretical knowledge and practical skills required to analyze, visualize, and interpret data to support data-driven decision-making. The program begins with an introduction to the origins of data and the types of data that can be collected. Students learn the difference between structured, semi-structured, and unstructured data and how these data types impact analysis. This foundational knowledge is essential for selecting appropriate analysis techniques and preparing data for processing. In the Data Collection and Data Types module, students explore techniques for collecting data from diverse sources, such as APIs, web scraping, and third-party datasets. They also learn about the importance of data cleaning and preprocessing to ensure data quality and reliability. The curriculum covers Data Visualization and Summarization, teaching students how to create clear, impactful visual representations of data using libraries like Matplotlib, Seaborn, and Plotly. Students learn how to generate visual summaries such as histograms, scatter plots, and box plots, allowing them to identify patterns, trends, and outliers in the data. The program then focuses on Inferential Statistics, where students learn how to make predictions and generalizations from sample data to a larger population. Key concepts like hypothesis testing, confidence intervals, and p-values are introduced. Students also explore how to properly interpret inferential results to avoid misrepresentation of findings. Advanced Statistical Modeling is introduced to provide students with more advanced techniques for predictive analysis. Topics like linear regression, logistic regression, and classification models are covered, giving students the ability to create models that predict future trends and classify data points. Students then learn how to Apply Python for Statistical Analysis, leveraging Python libraries like NumPy, Pandas, and SciPy to conduct complex statistical calculations and automate repetitive data analysis tasks. This hands-on experience allows students to build efficient workflows and prepare them for real-world data analysis scenarios. The final capstone project provides students with the opportunity to apply all their learned skills in a real-world context. Students are tasked with analyzing a dataset, applying statistical modeling, and presenting their findings through visualizations and written interpretations. This hands-on experience demonstrates their proficiency in data collection, cleaning, analysis, and reporting. By the end of the program, participants are equipped with the technical knowledge and practical experience required to work as data analysts, statistical analysts, or data scientists. They possess a strong understanding of data collection, data processing, statistical modeling, and data-driven decision-making.