Purpose
A course on quantitative analysis and visualization in Python is a course designed to teach participant how to use the Python programming language to perform quantitative analysis and create visualizations. The course will cover the basics of Python programming, including data types, control structures, and functions. Participant will also learn how to use popular Python libraries such as NumPy, Pandas, and Matplotlib to perform data analysis and visualization tasks. The course will cover topics such as data cleaning, data manipulation, statistical analysis, and data visualization. Participant will learn how to import and export data, create summary statistics, create plots and charts, and perform statistical tests. The course will also cover advanced topics such as machine learning, big data, and web scraping.
The course will provide a hands-on approach, with participant working on real-world examples and datasets and using Python to run the analysis and create visualizations. The course is intended for participant and professionals who have a basic understanding of statistics and programming, and who are interested in learning how to perform quantitative analysis and create visualizations using Python.
Course Objectives
- Understand the basics of Python programming, including data types, control structures, and functions.
- Learn how to use popular Python libraries such as NumPy, Pandas, and Matplotlib for data analysis and visualization.
- Gain experience in data cleaning, data manipulation and statistical analysis using Python.
- Learn how to create plots and charts using Matplotlib and other visualization libraries in Python.
- Understand how to perform statistical tests, such as t-tests, chi-squared tests, and ANOVA, using Python.
- Learn how to import and export data from various sources, such as CSV, Excel, and SQL databases.
- Gain hands-on experience working with real-world examples and datasets using Python to perform quantitative analysis and create visualizations.
- Understand how to apply the concepts learned in the course to various domains such as finance, economics, and social sciences.
- Understand the role of quantitative analysis and visualization in decision-making and problem-solving.
- Learn to understand and work with big data and machine learning techniques in Python.
- Understand the web scraping techniques in Python to extract data from the web.
Target Audience
- Graduate participant in statistics, computer science, finance, economics and social sciences who are interested in learning how to perform quantitative analysis and create visualizations using Python.
- Researchers and academics who want to use Python to perform data analysis and visualization in their field of study.
- Data scientists and analysts who want to learn how to use Python for quantitative analysis and visualization tasks.
- Professionals in the field of finance, economics, and social sciences who want to improve their skills in quantitative analysis and visualization.
- Business analysts and consultants who want to learn how to use Python for quantitative analysis and visualization to understand the behavior of firms and markets.
- IT professionals and programmers who want to learn how to use Python for quantitative analysis and visualization tasks.
- Anyone who is interested in understanding the basics of quantitative analysis and visualization and how it can be applied to real-world problems using Python.
Program Outline
Topic 1. Introduction to Python Programming
Overview of the Python programming language, including data types, control structures, and functions.
Topic 2. Data Manipulation with NumPy and Pandas
Overview of how to use NumPy and Pandas for data manipulation tasks such as cleaning, reshaping, and merging data.
Topic 3. Data Visualization with Matplotlib
Overview of how to use Matplotlib and other visualization libraries in Python to create plots and charts.
Topic 4. Statistical Analysis with Python
Overview of how to perform statistical analysis using Python, including summary statistics, t-tests, chi-squared tests, and ANOVA.
Topic 5. Data Import and Export
Overview of how to import and export data from various sources, such as CSV, Excel, and SQL databases.
Topic 6. Machine Learning and Big Data
Overview of how to use Python for machine learning and big data tasks, including supervised and unsupervised learning, and working with large datasets.
Topic 7. Web Scraping
Overview of how to use Python to extract data from the web using web scraping techniques.
Topic 8. Applications in different fields
Overview of how the concepts learned in the course can be applied in various domains such as finance, economics, and social sciences.
Topic 9. Quantitative analysis and visualization in decision-making and problem-solving
Overview of how quantitative analysis and visualization can be used to support decision-making and problem-solving.