This course focuses on developing Python skills for assembling business data. It delves into data wrangling, descriptive statistics, data visualization, regression, and database interaction using Python. Students will learn to operate Python software, execute fundamental data analytic tasks, and use relational databases.
The course is structured into eight modules, including a comprehensive introduction, foundational Python skills, data manipulation with Pandas and Numpy, data analysis, visualization, production data analytics, and database interaction. Each module offers in-depth learning materials, programming assignments, and quizzes to reinforce learning.
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This comprehensive course is divided into eight modules. It covers a range of topics including data wrangling, descriptive statistics, data visualization, regression, and database interaction using Python.
This module provides a comprehensive introduction to the course, covering important details such as the syllabus, glossary, and guidance on using Jupyter Notebook. Learners will also get an overview of online education at Gies College of Business and will be encouraged to update their profile and engage with fellow learners.
Module 1 focuses on foundational Python skills, including using Jupyter Notebook, navigating different IDEs, and mastering Markdown basics. Learners will also engage in readings, quizzes, and a programming assignment to reinforce their learning of Python fundamentals.
Module 2 delves into the introduction to Python, covering topics such as Python code basics, variables, data types, operators, functions, and conditional statements. Learners will engage in readings, quizzes, and a programming assignment to reinforce their understanding of Python concepts.
Module 3 builds on Python programming, exploring data structures, working with strings, lists, tuples, dictionaries, and loops. Learners will reinforce their learning through readings, quizzes, and a programming assignment.
Module 4 focuses on Python programming, covering topics such as writing Python programs, using Python modules, handling errors and exceptions, and an introduction to NumPy and Pandas. Learners will engage in readings, quizzes, and a programming assignment to apply their knowledge.
Module 5 delves into data analysis with Python, covering file input/output, working with the Pandas DataFrame, and introduction to descriptive statistics. Learners will apply their knowledge through readings, quizzes, and a programming assignment to analyze data using Python.
Module 6 introduces learners to visualization in Python, covering plotting with Pandas, matplotlib, and Seaborn, as well as one-dimensional and two-dimensional data visualizations. Learners will apply their knowledge through readings, quizzes, and a programming assignment to visualize data using Python.
Module 7 focuses on production data analytics, including an introduction to CRISP-DM, data preparation techniques, and linear regression in Python. Learners will apply their knowledge through readings, quizzes, and a programming assignment to analyze production data using Python.
Module 8 introduces learners to databases in Python, covering data persistence, advanced SQL concepts, and Python database programming. Learners will apply their knowledge through readings, quizzes, and a programming assignment to interact with databases using Python.
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