Course Outline for Python in Data Analysis

Module 1: Introduction to Python

• Objective: Familiarize students with Python basics.
• Topics:Python installation and setup.
• Introduction to Python IDEs (like Jupyter Notebooks).
• Basic Python syntax and concepts: variables, data types, operators.
• Control structures: if-else statements, loops.
• Practical Work:Writing simple Python scripts to understand syntax.
• Exercises on using variables, data types, and basic operations.

Module 2: Introduction to Data Analysis

• Objective: Understand what data analysis involves and its applications.
• Topics:Overview of data analysis, its importance, and fields of application.
• Introduction to data types in data analysis: structured vs unstructured data.
• Practical Work:Case studies of data analysis in different sectors (e.g., finance, business).

Module 3: Python for Data Handling

• Objective: Learn how to handle data using Python.
• Topics:Reading data from different sources (CSV, Excel, databases).
• Introduction to Pandas library for data manipulation.
• Basic data operations: filtering, sorting, and basic aggregations.
• Practical Work:Exercises on loading and manipulating data with Pandas.

Module 4: Exploratory Data Analysis (EDA)

• Objective: Teach how to explore and analyze datasets.
• Topics:Data cleaning techniques: handling missing values, data type conversion.
• Data exploration: basic statistics, correlation analysis.
• Visual data exploration: using Matplotlib and Seaborn for basic plotting (histograms, scatter plots, line charts).
• Practical Work:Projects involving cleaning and exploring a given dataset.

Module 5: Advanced Data Manipulation

• Objective: Dive deeper into data manipulation techniques.
• Topics:Advanced Pandas operations: merging/joining datasets, group by operations, pivot tables.
• Time-series data analysis.
• Introduction to data preprocessing for machine learning.
• Practical Work:Analyzing a complex dataset with advanced Pandas techniques.

Module 6: Basic Machine Learning with Python

• Objective: Introduce basic machine learning concepts.
• Topics:Overview of machine learning and its applications in data analysis.
• Basic machine learning models (linear regression, logistic regression).
• Introduction to scikit-learn library.
• Practical Work:Building simple predictive models using scikit-learn.

Module 7: Capstone Project

• Objective: Apply learned skills in a comprehensive project.
• Topics:Guiding through the steps of a data analysis project from start to finish.
• Project topics could be tailored to business analysis, financial analysis, etc.
• Practical Work:Completing an individual project that incorporates all the skills learned.

Additional Resources

• Videos and Tutorials: Supplement learning with online videos, webinars, and tutorials on Python and data analysis.
• Forums and Communities: Encourage participation in online communities like Stack Overflow, GitHub, and data science forums for peer support and learning.

Assessment and Feedback

• Regular Quizzes: To assess understanding of key concepts.
• Project Reviews: Feedback on capstone project to help students understand areas of improvement.

Difficulty Level on Scale of 1 to 10

• Starts at 1: The course begins at a very basic level, suitable for complete beginners.
• Progresses to 5-6: By the end, students should be at an intermediate level, able to handle most of the tasks required in entry-level data analysis roles.

This course structure aims to provide a comprehensive introduction to Python for data analysis, ensuring that by the end, students have the skills and confidence to apply for jobs in data analytics-related fields.

Learn the basics from https://www.learnpython.org/en/Welcome